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How to Become a Data Scientist

What is a Data Scientist?Data Scientist is a professional standing at the confluence of technology, domain knowledge, and business to tackle the data revolution. A Data Scientist needs to be a mathematician, computer programmer, analyst, statistician, and effective communicator to turn insights into actions.Source Link:It's not just the technical skills that make Data Scientist the most in-demand job of 21st Century, it takes a lot more. Data Scientist is a professional who utilizes these new-age tools to manage, analyse and visualize data.Let us take an example to better understand a day in the life of a Data Scientist. On a typical day, A Data Scientist may be given an open-ended problem such as “We need our customers to stay longer and watch/read more content”. The following are a few steps he/she might get started with:The Business HatThe job of the Data Scientist would, first of all, involve translating this problem statement into a quantifiable data science problem. For this, he might first ask or identify the current time being spent by users and discuss with the business teams how to quantify “more”.The Programming HatHe/she would then get towards data collection. He would have to work with different teams to understand what kind of data is available, what all he might require for his analysis and so on. Once clear about what and where related to data, he would extract and prepare data for analysisThe Analytical HatHere he would utilize his analytical and statistical powers to ask important questions using data. This typically involves exploratory analysis, descriptive analysis and so on.There are additional steps after this wherein the data scientist would then head towards building models to actually improve the time spent on the website by developing recommender engines and so on, sharing results/fine tuning models with business teams and so on. He would then take this towards production environment, where it can be actually tested and finally used.The above example is an over-simplified version of tasks a typical data scientist performs. Yet, it should give you a glimpse into how different skill-sets are utilized by such a professional.Data Science vs. StatisticsData Science can be defined in many ways. One of the most interesting and true definitions marks it as the fourth paradigm (link). The first three being experimental, theoretical and computational science. The fourth paradigm, Dr. Jim Gray explains, is the answer to cope with the tremendous flood of data being collected/generated every day.In simple words, Data Science is thus a new generation of scientific & computing tools which can help to manage, analyse and visualize such huge amounts of data.The explanation of the term Data Scientist and Data Science seems to indicate it is a completely new field with its own set of techniques and tools. Though this is true to a certain degree, yet, not entirely. Data Science, as mentioned above, is at the confluence of technology, domain knowledge and business understanding. Thus, it utilizes tools and techniques from various fields to form a set of encompassing methodologies to turn data into insights.Statistics traditionally has been the go-to subject to analyse data and hypothesis. Statistical methods are based on established theories and years of research.Even though Data Science and Statistics have similar goals (and overlapping techniques in certain cases), i.e. to utilize data to reach conclusions and share insights, they are not the same. Statistics predates computing era while Data Science is new-age amalgamation of interdisciplinary knowledge.There is a never-ending debate on the definitions of Data Science and Statistics. The old school believes Data Science is merely a rebranding of Statistics while the new-age experts grossly differ. Amongst all this, an interesting and somewhat accurate take on the issue was presented in an article on the website of Priceonomics (link):“Statistics was primarily developed to help people deal with pre-computer data problems like testing the impact of fertilizer in agriculture or figuring out the accuracy of an estimate from a small sample. Data science emphasizes the data problems of the 21st Century, like accessing information from large databases, writing code to manipulate data, and visualizing data.”Educational Qualifications to become a Data ScientistIt is worth reiterating the fact that Data Science is an interdisciplinary field. This makes sense as Data Science is not limited to just one field of study or industry. It is being used across every field which can or is generating data. It is not a surprise to see Data Scientists coming from varied academic backgrounds. Yet there are a few important and common skills such professionals have in the first place. Educational qualifications required to become a Data Scientist can be summarized as follows:A graduate degree in a quantitative field of study. Areas in mathematics, computer science, engineering, statistics, physics, social science, economics, statistics or related fields are most common.Newer options like bootcamps and MOOCs (Massively Open Online Courses) are quite popular for professionals to pivot into the areas of Data Science.An advanced degree in the form of Masters’ or even PhD certainly helps. Increasingly, a lot many Data Science professionals are with such advanced degrees (link).Technical Skills required to become a Data ScientistThis is the trickiest part of the whole journey. While being interdisciplinary is good in most aspects, it also presents a daunting question for beginners. Data Scientists are storytellers. They turn raw data into actionable insights all the way leveraging tools and techniques from various fields. Yet generic programming skills remain as the common denominator. Apart from programming skills, the following are a few important technical skills a Data Scientist usually has:Mathematical background/understanding (linear algebra, calculus, and probability are important)Machine Learning concepts and algorithms.Statistical concepts (hypothesis testing, sampling techniques and so on)Computer Science/Software Engineering skills (data structures, algorithms)Data Visualization skills (tools like d3.js, ggplot, matplotlib, etc)Data Handling (RDBMS, Big Data tools like Hive, Spark)Though there are no hard and fast rules, most Data Scientists rely on programming/scripting languages like python, R, scala, Julia, Java or SAS to perform everyday tasks from the raw data to insights.Learning Path for Data Scientist - From Fundamentals, Statistics to Problem SolvingTurning Data to Insights is easier said than done. A typical Data Science project involves a lot of important sub-tasks which need to be performed efficiently and correctly. Lets us breakdown the learning path into milestones and discuss how to go about the journey.Step 1: Select a Programming LanguageR and python are widely accepted and used programming languages in the data science community. There are other languages like Java, Scala, Julia, Matlab and to a certain extent even SAS. Yet, R and python have a huge ecosystem and community contributing towards making it better every day. Though there is no such thing as the best programming language for Data Science, yet, there are some favorites and popular ones. When starting off with your Data Science journey, it may be confusing which one to choose. The following are a few pointers that might be helpful:RR is the most popular language when it comes to statistical analysis and time series modeling. It also has a good number of machine learning algorithms and visualization packages. It can have a peculiar learning curve, yet it is good for exploring your data, one-off projects or quick prototypes. It is also usually the go-to language for academic reports, research papers.PythonPython is one of the most widely used programming languages. It is also sometimes referred to as a popular scientific language. Its ever-expanding community, ease of writing code, ecosystem, and support are reasons for its popularity. Python packages like numpy, pandas, and sklearn enable Data Scientists and researchers to work with matrices and other mathematical concepts with ease.The Java FamilyR and python are great languages and are of great help when it comes to quick prototyping (though that is changing slowly with python being used in production as well). The heavyweights of the industry are still the languages from the Java family. Java in itself is a mature and proven technology with an extensive list of packages for machine learning, natural language processing and so on. Scala derives heavily from Java and is one of the go-to languages for handling big data.There are a number of courses on platforms like Coursera and Udemy to get you started with these languages. Some of the courses are:Programming for Everybody(Getting started with python)Applied Data Science with Python SpecializationR ProgrammingAdvanced R ProgrammingJulia and languages of its type are upcoming ones with a special focus towards Data Science and Machine learning. These languages have advantages of having Data Science as one of its core concepts, unlike traditional languages which have been extended to cater to DS/ML needs. Again, it boils down to a personal choice and comfort when it comes to deciding which language to choose.Step 2:  Learn Statistics and MathematicsThese are the basic concepts required to understand the intricacies of more involved ones. The most essential ones are:Linear Algebra, Calculus and Probability theoryHaving an understanding of these concepts would help you in the long run to understand complex concepts. Probability theory is a must have as a lot of machine learning and statistics is based on measuring the likelihood of events, probability of failures or wins and so on. These concepts can be learnt through a number of classroom textbooks like Probability Theory by E.T James, Pattern Recognition and Machine Learning by Christopher M. Bishop, Introduction to Linear Algebra by Gilbert Strang. You could look up for these books/ebooks or even videos on youtube, khan academy and so on.Statistics:These form the very foundation of a lot of things you would be doing as a data scientist. The following are some of the popular online resources which can be helpful in this journey:Statistics:The Statsoft Book on StatisticsOnline Statistics EducationStep 3: Powerup with Machine Learning:Mathematics and Statistics give you the understanding to learn the tools and techniques required to leverage Machine Learning to solve real-world problems. ML techniques expand on a Data Scientist’s capabilities to handle different types and size of data sets. It is a vast subject on its own which can be broadly categorized into :Supervised Methods like classification and regression algorithmsUnsupervised Methods like different clustering techniquesReinforcement Learning like q-learning, etcDeep Learning (spanning across the above three types, it is slowly emerging as a specialized field of its own)Image Source:The following are a few helpful resources to get you started on the subject:Python for Data Science and Machine Learning bootcampR:Complete Machine Learning SolutionsData Science and Machine Learning bootcamp in RDeep Learning SpecializationData Science Nano DegreeProgramming for Data Science Nano DegreeStep 4: Practice!All theory and no practice would lead you nowhere. Data Science has an element of art apart from all the science and theory behind it. A Data Scientist needs to practice to hone the skills required to work on real-world problems. Luckily, the Data Science ecosystem and community is really a great place. To practice Data Science, you need a problem statement and corresponding data. Websites like Kaggle, UCI Machine Learning Repository, and many others are a great resource. Some of the popular ones are as follows:Bike Sharing Demand: Given daily bike rental and weather records predict future daily bike rental demand.Iris dataset: Given flower measurements in centimeters predict the species of iris.Wine dataset: Given a chemical analysis of wines predict the origin of the wind.Car evaluation dataset: Given details about cars predict the estimated safety of the car.Breast Cancer Wisconsin dataset: Given the results of a diagnostic test on breast tissue, predict whether the mass is a tumor or not.There is a detailed list of datasets discussed here as well by Dr. Jason Brownlee on his blog machinelearningmastery.comApart from these datasets, there are regular competitions on Data Science problems on websites like Kaggle, AnalyticsVidya, KDNuggests and so on. It is worth participating in these competitions to learn the tricks of the trade from some of the seasoned performers.Step 5: Build a PortfolioJust like a photographer or a painter, a Data Scientist is as much of an artist. While working on the different datasets and competitions, you can build a portfolio of your completed work to showcase your findings and learnings. This will not only help you showcase your talent but also give you a glimpse of your progress as you learn new and complex methods. A machine learning/data science portfolio is a collection of independent projects which utilizes machine learning in one way or the other. A typical machine learning portfolio can give you the following benefits:Showcase: your skill set and technical understandingReusable code base: As you work on more and more projects, there are certain components which would be required time and again. Your portfolio can be a repository of such reusable components.Progress Map: A portfolio is also a map of your progress over time. With every project, you would be getting better and learning new complex concepts. This is a great way to keep yourself motivated as well.Typically, Data Scientists leverage their portfolios along with their CVs for interviews and prospective employers to have a better understanding of their capabilities. Code repositories can be maintained on websites like github, bitbucket and so on. Maintaining a blog to share your findings, commentary and research to a broader audience along with self-promotion are also quite common.Step 6: Job Search / Freelancing:Once the groundwork is done, it's time to reap some benefits. We are living in the age of data and almost every domain and sphere of commerce is (or trying to) leverage Data Science. To leverage your skill set for Job Search or Freelancing, there are some amazing resources to your aid:Interview Preparation:Machine Learning using PythonData Science and ML Interview GuideDeep LearningData Science Competitions:KaggleInnocentiveTuneditHackathons:HackerEarthMachineHackEach of these platforms provides you with an ecosystem of experts and recruiters who can help you land a job or a freelancing project. These platforms also provide you with an opportunity to fine tune your skills and make them market ready.Top Universities offering a Data Scientist CourseThe educational requirements to become a Data Scientist were discussed previously. Apart from traditional quantitative fields of study, a lot many reputed top universities across the globe are also offering specialized Data Science courses for undergraduate, graduate and online audiences. Some of the top US universities offering such courses are:1. Information Technology and Data Management courses at the Colorado Technical UniversityCourse Name: Professional Master of Science in Computer ScienceCourse Duration: 2 yearsLocation: Boulder, ColoradoCourses: Machine Learning, Neural Networks, and Deep Learning, Natural Language Processing, Big Data, HCC Big Data Computing and many moreTracks available: Data Science and EngineeringCredits: 302. MS in Data Science, Columbia UniversityCourse Name: Master of Science in Data ScienceCourse Duration: 1.5 yearLocation: New York City, New YorkCore courses: Probability Theory, Algorithms for Data Science, Statistical Inference and Modelling, Computer Systems for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and VisualizationCredits: 303. MS in Computational Data Science, Carnegie Mellon UniversityCourse Name: Master of Computational Data ScienceCourse duration: 2 yearsLocation: Pittsburgh, PennsylvaniaCore courses: Machine Learning, Cloud Computing, Interactive Data Science, and Data Science SeminarTracks available: Systems, Analytics, and Human-Centered Data ScienceUnits to complete: 1444. MS in Data Science, Stanford UniversityCourse Name: M.S. in Statistics: Data ScienceCourse Duration: 2 yearsLocation: Stanford, CaliforniaCore courses: Numerical Linear Algebra, Discrete Mathematics and Algorithms, Optimization,Stochastic Methods in Engineering or Randomized Algorithms and Probabilistic Analysis, Introduction to Statistical Inference, Introduction to Regression Models and Analysis of Variance or Introduction to Statistical Modeling, Modern Applied Statistics: Learning, and Modern Applied Statistics: Data MiningTracks available: The program in itself is a trackUnits to complete: 455. MS in Analytics, Georgia Institute of TechnologyCourse Name: Master of Science in AnalyticsCourse Duration: 1 yearLocation: Atlanta, GeorgiaCore courses: Big Data Analytics in Business, and Data and Visual Analytics,Tracks available: Analytical Tools, Business Analytics, and Computational Data AnalyticsCredits: 36There are numerous other courses by other top universities in Europe and Asia as well. Also, MOOCs from platforms like Coursera, Udemy, Khan Academy and others have also gained popularity lately.Roles and Responsibilities of a Data Scientist - What does a Data Scientist do?The role and responsibilities of a Data Scientist vary greatly from one organization to other. Since the life cycle of a data science project involves a lot of intricate pieces, each with their own importance, a data scientist might be required to perform different tasks. Typically, a day in a Data Scientist’s life comprises of one or more of the following tasks:Formulate open-ended questions and perform research into different areasExtract data from different sources from within and outside the organizationDevelop ETL pipelines to prepare data for analysisEmploy sophisticated statistical and/or machine learning techniques/algorithms to solve problems at handExploratory and Descriptive analysis of data.Visualization of data at different stages of the projectStory-telling/communicating results and findings to end-consumers/IT teams/business teamsDeploy intelligent solutions to automate tasksThe above list is by no means exhaustive. Specific tasks may be required for specific organizations and/or scenarios. Depending upon the set of tasks assigned or strengths of a particular individual, the Data Scientist role may have different facets to it. Some organizations divide the above set tasks into specific roles like:Data Engineer: concentrates more on developing ETL pipelines and Big Data infrastructure.Data Analyst: concentrates on hypothesis testing, A/B testing and so onBI Analyst: concentrates on visualizations, BI reporting and so onMachine Learning/Data Science Engineer: concentrates on implementing ML solutions into production systemsResearch Scientist: concentrates on researching new techniques, open-ended problems, etc.Though some organizations separate out the roles and responsibilities, others chose to have a common Data Scientist title.Salaries of a Data ScientistThe title of the most-coveted job of the 21st century ought to have an equally tempting salary as well. The data also confirms the hypothesis from various aspects. Different surveys from across the world have analysed salaries of Data Scientists and the results are astonishing.The Burtch Works Study for Salaries of Data Scientists is one such survey:The survey points out that post the peak increases in data scientist salaries across different levels in 2015-2016, the salaries for 2018 have been more or less steady at the previous year levels.The median base salary for a starting position is around $95k which rises up to $165K for 9+ years of experience (for individual contributors)The median base salary for Managers start out around $145K and go up to $250K (for 10+ years of experience)Image SourceA survey by PromptCloud on the similar lines tried to identify different skills required for different Data Scientist job postings. The results show python as the topmost skill required followed by SQL, R and others. This showcases how important python and python ecosystem is to the Data Science work and community.The Glassdoor 50 Best Jobs in America for 2018 (link) rates Data Scientist as numero uno with an average salary of around USD 120k. The study also identifies other related Data Science job titles like Data Analyst and Quantitative Analyst in the study.Image SourceSimilar results from Payscale, Linkedin and others reconfirm the fact. Data Scientists are really sought after across the globe.Top companies hiring Data ScientistWith the advancements in compute & storage and corresponding lowering of cost for hardware, technology is part and parcel of almost every industry. From aerospace to mining, from the internet to farming, every sphere of commerce is generating an immense flood of data. Where there’s data, there’s data science. Almost every industry today is leveraging the benefits of Data Science.Some of the top companies hiring for Data Scientists are:GoogleTwitterGE-HealthHPMicrosoftAirbnbGE-AviationIBMAppleUberUnitedHealth GroupIntelFacebookAmazonBoeingAmercian ExpressThese are some of the big names in their respective fields. There are a lot of start-ups along with small-medium sized enterprises that are also leveraging Data Scientists to make an impact in their respective fields.How is Data Science different from Artificial Intelligence?Our discussion so far has revolved around Data Science and related concepts. In the same context, there’s another important term, Artificial Intelligence (AI). There are times when terms like AI and Data Science are used interchangeably while there people who perceive them differently as well. To understand each side, let us first try and understand the term Artificial Intelligence.Artificial Intelligence can be defined in many ways. The most consistent and commonly accepted definition states:“The designing and building of intelligent agents that receive percepts from the environment and take actions that affect that environment”The above definition comes from AI heavyweights Dr. Peter Norvig and Dr. Stuart Russell. In simple words, this definition highlights the presence of intelligent agents which act based on stimulus from the environment, which in turn has an effect on the environment as well. Sounds very similar to how we, as humans, function.The genesis of Artificial Intelligence as a field of study/research is credited to the famous Dartmouth workshop in 1956. The workshop was held by John McCarthy and Marvin Minsky, amongst other prominent personalities from computer science and AI space. Their workshop provided the first glimpse of intelligent systems/agents. The programs were learning strategies for the game of checkers. The programs were reported to play better than average human beings by 1959! A remarkable feat in itself. Since then, the field of AI has gone through a great many changes, theoretical and practical advancements.The field of AI is focussed towards being successful at maximizing the agent’s chances of achieving a stated goal. The goal can be termed simple (if its only about winning or losing) or complex (take next steps based on rewards from past moves). Based on these goal categories, AI has focussed at solving problems in the following high-level domains over the course of its history:Knowledge RepresentationThis is one of the core concepts in classical AI research. As part of Knowledge Representation or Knowledge Engineering, we try to capture the world knowledge (where world is some specific narrow domain) possessed by experts. This was the foremost area of research for expert systems. The field of Ontology is highly associated with Knowledge Representation.Problem Solving and Reasoning TasksThis is one of the earliest areas of research. Herein, the researchers focussed at mimicking human reasoning step by step for tasks such as puzzle solving and logical deductions.PerceptionThe ability to utilize input from different sensors such as microphones, cameras, radars, temperature sensors and so on for decision making. This is also termed as Machine Perception with modern day applications like speech recognition, object detection and so on.Motion and ManipulationThe ability to move and explore the environment is an important characteristic highly utilized in the robotics space. Particularly industrial robots , robotic arms and the amazing machines from groups like Boston Dynamics are prime examples.Social IntelligenceIt is considered one of the far fetched goals wherein the intelligent systems are expected to understand human emotions and motives to take decisions. Current-day virtual assistants(the likes of Google Assistant, Alexa, Cortana, etc.) provide a glimpse of such advantages by allowing them(virtual assistants) to converse, joke and make small talk.The domains of Learning Tasks, characterized as supervised and unsupervised learning along with Natural Language Processing tasks have been traditionally associated with AI. Yet, with recent advancements in these fields, they are sometimes seen separately or no longer part of AI. This is also known as AI effect or Tesler’s Theorem. The AI effect simply states:“AI is whatever hasn’t been done yet”On the same grounds, OCR or optical character recognition, speech translation and others have become everyday technologies. This advancement has led to these technologies being no longer considered as part of AI research anymore.Before we move on, there is another important detail about AI. Artificial Intelligence is categorized into two broad categories. These are:Narrow AIAlso termed as weak AI. This category is focussed at tractable AI tasks. Specifically, most of current-day research is focussed on narrow tasks like developing autonomous vehicles, automated speech recognition, machine translation and so on. These areas work towards building intelligent systems which mimic human level performance but are limited to specific areas only.Deep AIThis is also termed as strong AI or better, Artificial General Intelligence. If an intelligent agent is capable of performing any intellectual task, it is considered to possess Artificial General Intelligence. AGI is considered to be a summation of knowledge representation, reasoning, planning, learning, and communication.Deep AI or AGI seems like a far fetched dream yet advancements like Transfer Learning and Reinforcement Learning techniques are steps in the right direction.Image SourceNow that we understand Artificial Intelligence and its history, let us attempt at understanding how it is different from Data Science. Data Science, as we know, is an amalgamation of tools and techniques from different fields (similar to AI). From the above discussion, we see, there is a definite overlap between the definition of weak/narrow AI and Data Science tasks. Yet, Data Science is considered to be more data-driven and focussed on business outcomes & objectives. It is more application oriented study and utilization of tools and techniques. Though, there are certain overlaps and similarities in the areas of research and tools, Data Science and AI are certainly not the same. It would be hard to even set them as subset-superset entities either. They are best seen as interdisciplinary fields which make the best of uncertainties.SummaryData Science is THE keyword for every industry for quite a few years now. In this article on What is a Data Scientist, we covered a lot of ground in terms of concepts and related aspects. The aim was to help you understand what really makes Data Scientist the “top and trending ” job of the 21st century.The discussion started off with a formal definition of Data Science and how it is ushering in the fourth paradigm to tackle this constant flood of data. We then briefly touched upon the subtle differences between Data Science and Statistics along with the point of contention between the experts from the two fields. We also presented an honest opinion on what all it takes, in terms of technical skills and educational qualifications, to become a Data Scientist. Sure, it is cool to be one, but it is not as easy as it seems.Along with the skills, we touched upon the learning path to become a Data Scientist. In this section, we covered the fundamental concepts one should know to advanced techniques like Reinforcement Learning and so on.The world is in deep shortage of Data Scientists. Top universities have taken up this challenge to upskill the existing and next generation of workforce. We discussed some of the courses being offered by these universities from across the globe. We also touched upon different companies that are hiring data scientists and at what salaries.In the final leg, we introduced concepts related to Artificial Intelligence. It is imperative to understand how different yet overlapping Data Science and AI are.With this, we hope you are equipped to get started on your journey to become a Data Scientist and contribute. If you are already working in this space, the article was aimed to demystify some commonly used terms and provide a high-level overview of Data Science.
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How to Become a Data Scientist 8776 How to Become a Data Scientist Blog
Raghav Bali 22 Mar 2019
What is a Data Scientist?Data Scientist is a professional standing at the confluence of technology, domain knowledge, and business to tackle the data revolution. A Data Scientist needs to be a mathema...
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Top 10 Python IDEs

What is an IDE?IDE stands for Integrated Development Environment. It is a piece of development software which allows the developer to write, run, and debug the code with relative ease. Even though the ability to write, run and debug the source code is the most fundamental features of an IDE, they are not the only ones. It is safe to say that all IDEs perform the fundamental tasks equally well, however, most modern IDEs come with a plethora of other features specifically tuned to make the workflow easier for a particular type of development pipeline. In this article, we will focus on IDEs that support Python as a programming language.IDEs are usually developed by a community of people (open source) or by a commercial entity. Each IDE comes with their own strengths and weaknesses. Some IDEs like Jupyter or Spyder are open source and are developed aimed at the Scientific and Artificial Intelligence research community. These IDEs have additional features which make it easy and fast to prototype Machine Learning models and Scientific simulations with great ease. However, they are not well equipped to sustain the development process of an end-to-end application.Why is IDE an important part of Development?Traditionally text editors like Nano or Vim (Linux/Unix), Notepad (Windows) and TextEdit (MacOS) were used to write code. However, they are very good at only one single thing, that is to write text. They lack the common functionalities like syntax highlighting, auto indentation, auto code completion, etc.Next comes the dedicated text editors which were designed to write and edit code for any programming language. These editors like Sublime Text and Microsoft Visual Studio Code are feature rich in terms of the common functionalities like syntax highlighting and auto-indentation. Some even have a Version Control System built in. However, they still lack a significant chunk of functionalities that IDEs have. Their main advantage over IDEs is that they are fast and easy to use.Finally, coming to IDEs, these are full-fledged development software which contains all the features and tools necessary to aid the complete development pipeline of any software. The main disadvantage of IDEs is that they are comparatively slower and more taxing on the system when compared to text editors.Top 10 IDEs used for PythonIn this article, we will look at the top 10 Python IDEs that are used across the industry. We will learn about their features, pros, cons and will finally conclude what makes one special over the other.1. PyCharmCategory: IDEWebsite: https://www.jetbrains.com/pycharm/PyCharm is a cross-platform Integrated Development Environment specifically developed for Python, by Czech company < rel="nofollow"a href="https://www.jetbrains.com" rel="noopener noreferrer" target="_blank">JetBrains. It primarily has two versions of the software that is available to download - Professional Edition and Community Edition. The Professional Edition has additional features for development, which the Community Edition lacks and is to be purchased. The Community Edition is released under Apache License and is a free-to-use, open source IDE which is identical to the Professional Edition in most ways, however, it lacks the additional features.Features: Listed below are some of the features of this IDEDevelopment Process: PyCharm Supports the complete development pipeline and its convenience starts to show right from the beginning of the creation of the project, where the developer is given the choice to choose between various interpreters, create a virtual environment or opt for remote development.Inbuilt VCS: Like many other modern IDEs, VCS is baked right into PyCharm. When inside a project which employs VCS, PyCharm automatically generates a graphical interface showing the various branches and the status of the projectDedicated Database Tools: PyCharm makes it quite easy to access and modify databases. It allows the developer to access any of the popular SQL databases like MySQL, PostgreSQL, Oracle SQL, etc. right from inside the IDE. It also allows to edit SQL commands, alter schemas, browse data, run SQL queries and analyze schemas with UML diagrams along with support for SQL Syntax Highlighting. Support for IPython Notebooks: As most Data Scientists will swear by, IPython Notebooks are one of the best functionalities that are available in Python. Even though PyCharm is not as functional in running IPython Notebooks as the more preferred Jupyter Notebooks are, PyCharm allows the developer to successfully open the IPython Notebook file with proper Syntax Highlighting and Auto-Code Completion and allows the developer to run the notebook as well.Dedicated Scientific Toolkit: One of the most commonly used features in this toolkit is SciView. SciView is primarily used for data visualization. It carries forward the functionalities of the well-known Spyder IDE, which comes as a part of Anaconda Installation. SciView allows the developer to easily view plots and graphs built into the editor without having to deal with pop-up windows showing the graphs. Additionally, one of the best features of SciView is the Variable Explorer or Data Explorer, which provides the user with a tabular visualization of the data and its values contained in the variable.  Pros: PyCharm offers what most other IDEs don’t, and that is a complete package that allows PyCharm to be used for any kind of end-to-end development or prototyping process across almost all development fields.Cons: PyCharm, being so packed in features, is sluggish and consumes a considerable about of system resources even while idling. This may create problems in low-end systems and prevent the developer from using his/her system’s full potential for the project.2. SpyderCategory: IDEWebsite: https://www.spyder-ide.org/Spyder is an open-source Scientific Python Development Environment which comes bundled with Anaconda. Spyder has multiple features that are developed to aid the scientific and data-driven development and hence is an ideal IDE for Data Scientists. It is written in Python itself with the PyQt5 library and hence offers some added functionality which is mentioned below.  Features: Listed below are some of the features of this IDEVariable Explorer: Variable explorer is one of the main features of Spyder. This allows the developer to view the contents, datatypes, and values of any variable in the program. This is particularly useful for data scientists since variable explorer allows the developer to view the format and shape of the data. Additionally, it allows the developer to plot histogram and visualize time-series data, edit DataFrames and Numpy arrays, sort a collection and dig into nested objects.  Live Library Docs: Accessing the documentation repeatedly for a particular class or object of a library via a third party browser can be tiresome. Spyder has an inbuilt HTML viewer which displays the documentation for that particular object or library directly inside the IDE.IPython Console: All the lines of codes that are executed by the Spyder IDE, is done so in the IPython console. This console stays open even after the program execution has been concluded and the user can further write extra commands to view, test or modify the existing objects while keeping the changes temporary, ie. outside the main editor. Debugger: Debugging is quite an important part of the development process of any software/program. Spyder supports inbuilt debugger via its iPython console, which allows the user to debug each step of the code individually.Plugins: Spyder, being open-source, supports third-party plugins which allow the developer to improve his/her development experience. A few of the most used ones are Spyder Notebook, Spyder Terminal, Spyder UnitTest, and Spyder Reports.Pros: Spyders is developed by scientists for scientists. Hence it consists of all the important tools and functionalities that may be required for the development process for any Data Scientist and is ideal for this situation.Cons: Spyder being specifically designed and developed for a certain community of developers (Data Scientists), it lacks most of the end-to-end development tools that are present in other IDEs like PyCharm.3. Jupyter NotebookCategory: IPython Notebook EditorWebsite: https://jupyter.org/  Jupyter Notebook is one of the most used IPython notebook editors used across the Data Science Industry. It makes the best use of the fact that Python is an interpreted language, which means that Python lines of code can be run one line at a time and the whole thing need not be compiled together like C/C++. This makes IPython Notebooks ideal for writing and prototyping Machine Learning models. Since there is a significant amount of preprocessing done initially, and after that, there is a process of repeated hyperparameter tuning and model prototyping, the ability to run a cell (a group of lines) together at a time gives Data Scientists the ability to tune their models easily.Features: Listed below are some of the features of this IDEMarkdown and LaTeX Support: Jupyter Notebooks, in addition to being able to write Python code, supports documentation and commenting with text formatting via Markdown Editor. Each cell can be converted to use Markdown or Code. Additionally, Jupyter Notebook, being a scientific tool, also supports LaTeX commands to write down equations at any cell in the notebook.Dedicated display for DataFrame and Plots: Since data is the core component of Data Science and Machine Learning, Jupyter Notebook has an inbuilt display for data-tables or pandas DataFrames. Additionally, Data Visualization is an important process of the Exploratory Data Analysis of the Data Science Pipeline. Thus, Jupyter Notebooks has an integrated display for plots and diagrams so that the developer does not have to deal with pop-up plots.Remote Development Support: Jupyter Notebook is a server-based application which, when run locally, creates a localhost server backend before opening up via a web browser. But the same can be used for remote development as the Notebook can be run on a remote server which can then be connected to, to run the notebook locally on the web browser, while the processing is done in the server side. Direct Command Prompt or Linux Shell access from inside the notebook: Since the notebook can be used as a remote development tool, the notebook allows the developer to directly access the Linux Shell or Windows Command Prompt directly from the notebook itself without having to open up the shell or command prompt. This is achieved by adding an exclamation mark (“!”) before writing the shell command.Multi-Language Support: Jupyter Notebook supports both Python and R. R is also a programming language popularly used by Data Scientists and Statisticians.Pros: The main advantage is the convenience of using it in R&D and in prototyping for Machine Learning and Scientific problems. It significantly reduces the time required for prototyping and tuning of Machine Learning models in comparison to other IDEs.Cons: The main con is that this IDE does not support the entire development pipeline and is ideal just for prototyping. It does not have the additional tools or functionalities that make other IDEs ideal for deployment of programs and scripts.  4. AtomCategory: Code EditorWebsite: https://atom.io/Atom initially started as an open source, cross-platform, light-weight Node.js based code editor developed by GitHub. It is popularly known as the “Hackable Text Editor for the 21st century” by its developers. Atom is based on Electron, which is a framework which enables cross-platform desktop application using Chromium and Node.js and is written in CoffeeScript and Less.Features: Listed below are some of the features of this IDEPlugins: Atom’s strength is its open-source nature and plugin support. Outside of the usual auto code-completion, syntax highlighting and file browser, it has no such “features’ of its own. However, there are numerous third-party plugins to full up this gap and make it a recommendable IDE. Some of the useful plugins are listed below:git-plus: Git-plus is a feature which allows the developer to use common Git actions without the need to switch terminal.vim-mode: This plugin allows developers who are used to vim, to feel right at home. It adds most of vim’s features to be readily available in Atom.merge-conflicts: Since Atom is developed by GitHub, this plugin provides the developers to find, preview and edit code which has merge-conflicts in a similar view to that of GitHub’s own merge-conflict viewer.Tight Git Integration: One of the advantages of being an IDE developed by GitHub is that it has very tight integration of Git built into it and it is hence quite easy to run git operations directly from inside the code editor.Package Installer: Atom has a user-friendly package installer which allows the developer to install and apply any available package or theme instantly. It does not require any restart of the app post-installation, and hence, avoids the inconvenience. Project Manager: Atom has an inbuilt project manager which allows the developer to easily access and manage all his projects in an organized manner.Pros: Being open source and with plugin support, Atom is one of most functional code editors out there. It checks all the boxes for it to be designated as an IDE. Additionally, it is lightweight when compared to other IDEs and is not resource hungry.Cons: Atom, essentially being a code editor, lacks a lot of the integrated tools that developers usually require to carry out a complete end-to-end development pipeline.5. Enthought CanopyCategory: IDEWebsite: https://www.enthought.com/product/canopy/  Canopy is an IDE developed and maintained by Enthought which is specially designed keeping Scientists and Engineers in mind. It contains integrated tools for iterative data analysis, visualization, and Python application development. It has two specialized versions of Canopy: Canopy Enterprise and Canopy Geoscience. Needless to mention that these products contain a specific set of features which is not present in the vanilla Canopy. In this section, we will concentrate on the vanilla version of Canopy, which is free to use.Features: Listed below are some of the features of this IDEIntegrated Package Installer: Canopy provides a self-contained installer which is capable of automatically installing Python and other scientific libraries with minimal effort from the user. It is similar to Anaconda installation.Scientific Tools: Like a few of the IDEs mentioned earlier, Canopy has a set of tools specifically tuned and designed for Scientific and Analytical data exploration and visualization. It has special tools for viewing and interaction with plots. In addition to that, it also contains a “variable browser” which allow the user to view the contents of variables in tabular form and their respective datatypes.Integrated IPython window: Similar to Spyder, Canopy contains an integrated IPython console which allows the developer to execute code line by line or all at once. This results in easier visualization and debugging.Integrated Scientific Documentation: Again, similar to Spyder, Canopy has inbuilt documentation support for scientific articles which allow the user to refer to the documentation for specific libraries directly inside the IDE, without the need to switch to another window and search for the documentation. This, in turn, makes the development process faster.Pros: Since it is specifically designed for Engineers and Scientists, it contains a set of specialized tools which allow the developers from that domain to build prototypes faster and with ease. Being similar to Spyder, it is a good alternative to Spyder IDE for Data Scientists.Cons: Canopy lacks the tools that are essential for deployment, group development, and version control system. This IDE is suitable for prototyping but not the development of deployable code.6. Microsoft Visual StudioCategory: IDEWebsite: https://visualstudio.microsoft.com/   Microsoft Visual Studio IDE is one of the most preferred IDEs across the development industry. It was initially designed for C/C++ development. However, with increasing popularity and adoption of Python in the industry, Microsoft decided to add support for Python Development via an open-source extension called Python Tools. This brought the best of both worlds together under one integrated environment. Visual Studio’s superior development centric features are second to none. With all the features bundled together, it almost comes neck-to-neck with PyCharm.Features: Listed below are some of the features of this IDEIntelliSense: IntelliSense is an auto-code-completion feature that is baked right into Microsoft Visual Studio’s editor. This allows the IDE to predict and autocomplete code while being typed by the developer with a high level of precision and accuracy.  Built-in Library Manager: Similar to other IDEs like PyCharm, Visual Studio has a built-in library manager, which allows the developer to easily find and download libraries from PyPI without the need to manually use pip via command line interface.Debugger: Microsoft’s offering is one of the best in the industry. It offers a plethora of debugging tools. Starting from basic debugging, like setting breakpoints, handling exceptions, step-wise execution and inspecting values, it goes all the way to Python Debug Interactive Window, which, in addition to supporting standard REPL commands, also supports special meta commands.Source Control: Again, similar to PyCharm, Visual Studio has a fully integrated Version Control System. It provides a GUI interface to ease the management of Git/TIF projects. Management of branches, merge conflicts and pending changes can be easily achieved by a specialized tool called Team Explorer.Unit Tests: Visual Studio can be used to set specialized test cases called “Unit Tests”, which allows the developer to test the correct working of the code under various input scenarios. It allows to view, edit, run and debug test cases from the Test Window.Pros: Microsoft Visual Studio is a very successful full-fledged IDE on its own, only became better with the added support for Python Development. Similar to PyCharm, it is one of the most complete and feature packed IDEs out there. Unlike PyCharm, it is quite lightweight in terms of System Resource Utilization.Cons: Machine Learning being one of the primary applications of Python, Microsoft Visual Studio lacks any kind of specialized tools for data exploration and visualization.7. Sublime TextCategory: Code EditorWebsite: https://www.sublimetext.com/Similar to Atom, Sublime Text is more of a Code Editor than IDE. However, due to its support for various packages, it packs in enough features to be considered for a full end-to-end development environment. Its support for languages is not limited to any one or two programming languages. It in-turn supports almost all languages that are used across the industry. It has syntax highlighting and auto code completion for almost all languages and hence is quite versatile and flexible. Sublime text has a free trial and post that it is paid. It is a cross-platform Editor, which supports a single license key across all the platforms.Features: Listed below are some of the features of this IDEKeyboard Shortcuts: One of the primary strengths of Sublime Text is its support for keyboard shortcuts for almost all operations. For developers who are familiar with the different shortcut combinations, it becomes quite easy for them to quickly perform certain tasks without having to tinker with the menu.Command Palette: This is a special functionality that can be accessed via keyboard shortcut: Ctrl+Shift+P, which pops up a textbox, where the developer can type to access functions like sorting, changing syntax and even changing the indent settings.  Package and API Ecosystem: Sublime text enjoys a plethora of various package and API support by the community which vastly enhances its functionality. Starting from remote access and development over servers, to packages specifically developed for certain languages; Sublime supports it all.  Added Editing Functionalities: One of the key features of Sublime that many other editors have been inspired from is its highly customized code editing interface, which allows the developer to have multiple cursors at once and dit more than one location simultaneously.Pros: Sublime Text is the fastest and the lightest Text Editor among the competition and yet is functional enough to be used as an IDE. It provides a unique combination of versatility and functionality, which is truly unique.Cons: Being a text editor, even though it makes up for its lack of built-in functionality via plugins and add-ons, at the end of the day it is still a text editor and lacks a few key features that dedicated IDEs possess.8. Eclipse + PyDevCategory: IDEWebsite: http://www.pydev.org/Eclipse is one of the best open-source IDE suites available for Java development. It supports numerous extensions. One such open-source extension is PyDev, which turns Eclipse into a powerful Python IDE.Features: Listed below are some of the features of this IDEDjango Integration: For backend developers, this IDE would make development easier and faster by having Django integration baked right into it, along with Syntax Highlighter and Code Auto Completer.Code Debugging and Analysis: Eclipse has a good set of code debugging and analysis tools and supports features like refracting, hinting code debugging and code analysis. It also has support for PyLint, which is an open-source code bug and quality checker for python.Package Support: Eclipse with PyDev brings a lot of additional features into the IDE. Support for Jython, Cython, Iron Python, MyPy etc. is inherently present in the IDE.Pros: The main advantage of Eclipse is that it is one of the most used IDE in the Java development industry, and hence, any Java developer will feel right at home with this. Additionally, the added package support makes it competitive enough to go head to head with the other available native python IDEs.Cons: Even though there is good package support with additional functionalities that make it unique, the integration of PyDev with Eclipse feels half baked. This is primarily noticeable when the IDE slows down while writing long programs with a lot of packages involved.9. WingCategory: IDEWebsite: https://wingware.com/Wing is a cross-platform Python IDE packed with necessary features and with decent developmental support. It is free for personal use but has a fee associated with it for the pro version, which is targetted towards commercial use. The pro version comes with a 30-day trial for developers to try it out. It even has a specialized version called Wing 101, which is targetted at beginners and is a toned downed version which makes it easier for beginners to start with.Features: Listed below are some of the features of this IDETest-Driven Development: One of the key features of Wing is its test-driven development and debugging tools. It supports unittest, pytest, doctest, nose, and Django testing framework.Remote Development: Similar to PyCharm, Wing supports easy-to-setup remote development which allows the developer to connect to remote servers, containers or VMs and develop remotely with ease.Powerful Debugger: It has a debugging toolset which allows the developer to perform easy bug-fixes. Some of the features it provides are conditional breakpoints, recursive debugging, watch value, multi-process, and multi-threaded workload debugging.Intelligent Editor: In addition to supporting the mundane syntax highlighting and auto code completion, Wing’s editor supports refactoring, documentation, invocation assistance, multi-selection, code folding, bookmarks, and customizable inline code snippets. Additionally, it can emulate Eclipse, Visual Studio, XCode, Emacs, and Vim.Pros: As apparent from the above-mentioned features, Wing provides quite a complete package in terms of development tools and flexibility. It can be coined as “ideal” for backend web development using Django.Cons: The commercial version can be quite expensive.10. RodeoCategory: IDEWebsite: https://rodeo.yhat.com/Rodeo is a cross-platform, open-source IDE developed by yhat. It is designed and developed targetting Data Science and thus, has a lot of tools required for Data Analysis and Visualization.Features: Listed below are some of the features of this IDEData Visualization: Similar to other IDEs targeted at Data Scientists, Rodeo also supports specialized data visualization tools and graph visualization.Variable Explorer: Again, similar to Spyder, Rodeo allows the user to explore the contents of the variables in the program along with their respective data types. This is an important tool in Data Science.Python Console: Rodeo has a Python console built into the IDE which allows the user to execute and debug code line by line along with block execution.Documentation Viewer: Rodeo has a built-in documentation viewer, which allows the developer to consult the official documentation of any library on-the-go.Pros: It is lightweight and fast, and hence is ideal for quick code prototyping for Data Science.Cons: The Development of this IDE has been halted for the past two years. It does not receive any new updates and the project is likely dead. Even though it is a good IDE, it may never receive new updates in the future.ConclusionHaving listed out the features, pros, and cons of some of the best IDEs available for Python, it is time to conclude which one is the best.To be honest, there is no clear answer to which IDE is the best since most of them are specifically designed for a given group of developers or scientists. Hence, we will choose the most preferred IDE for each type of use case.General Python/Web Development: This is more like an all-rounder IDE, which can perform any given task with relative ease. In this use case, PyCharm and Microsoft Visual Studio come neck-to-neck in terms of their features and ease of use. However, being natively developed for Python and with added functionality for the Scientific community, PyCharm clearly has an edge over Visual Studio. Hence PyCharm is the most preferred one here.Scientific Development and Prototyping: This use case is mainly targeted at Data Scientists and Machine Learning Engineers who primarily handle data. In this use case, the two most used IDEs are Jupyter Notebooks and Spyder. Spyder is more like an IDE with additional features specifically tailored towards Data Science. Jupyter is an IPython Notebook which cannot be used for development, but us superior in model building and prototyping. Hence, there is no clear winner here, since the usage of the IDE solely depends on the user’s requirements.Code Editors: The final category is simple code editors which perform similarly to full-fledged IDEs due to additional packages and add-ons. Sublime Text is a clear winner in this segment, primarily due to its simple and fast interface along with great community support and a good python development support.
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Top 10 Python IDEs

Top 10 Python IDEs

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What is an IDE?IDE stands for Integrated Development Environment. It is a piece of development software which allows the developer to write, run, and debug the code with relative ease. Even though the...
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What’s New in React 16.8

What is React?React is a library by Facebook that allows you to create super performant user interfaces. React allows you to disintegrate a user interface into components, a functional unit of an interface. By composing components together, you can create UIs that scale well and deliver performance.What sets React apart is its feature set.1. The Virtual DOM - An in-memory representation of the DOM and a reconciliation algorithm that is at the heart of React’s performance.2. Declarative Programming & State - State is the data that describes what your component renders as its content. You simply update the state and React manages the rest of the process that leads to the view getting updated. This is known as declarative programming where you simply describe your views in terms of data that it has to show.3. Components - Everything that you build with React, is known as a component. By breaking down UIs into functional and atomic pieces, you can compose together interfaces that scale well. The image below demonstrates a login interface which has been composed together using three components.4. JSX - The render method inside a class component or the function component itself allows you to use JSX, which is like an XML language that incorporates JavaScript expressions. Internally, JSX is compiled into efficient render functions. 5. Synthetic Events - Browsers handle events differently. React wraps browser specific implementations into Synthetic Events, which are dispatched on user interaction. React takes care of the underlying browser specific implementation internally.6. Props - Components can either fetch data from an API and store in the local state, or they can ingest data using props, which are like inlets into a prop. Components re-render if the data in the props update.The road to React 16.8On 26th September, 2017, React 16.0 was announced with much fanfare. It was a major leap forward in the evolution of React and true to its promise, the 16.x branch has marched on, conquering new heights and setting benchmarks for the library, the developer experience,and performance.So, let’s look back at the 16.0 branch, right from its inception and analyze its evolution, all the way to React 16.8.React 16.0Released: 26th September, 2017React 16.0 marked a major leap forward in the evolution of the library and was a total rewrite. Some of the major features introduced in this release include:A new JavaScript environment: React 16.0 was written with modern JavaScript primitives such as Map and Set in mind. In addition, this version also introduced the use of requestAnimationFrame. As a result, React 16.0 and above are not supported by Internet Explorer < v11 and need a polyfill to work.Fiber: React 16.0 introduced a brand new reconciliation engine known as Fiber. This new engine is a generation leap over the previous generation of React’s core and is also responsible for the many new features that were introduced in this release. Fiber also introduces the concept of async rendering which results in more responsive apps because React prevents blocking the main thread. Fiber incorporates a smart scheduling algorithm that batches updates instead of synchronously re-rendering the component every time. Re-renders are only performed if and when optimally needed.Fragments: Till this release, the only way to render lists of components was by enclosing them in a div or some other enclosing node that would also get rendered in place. React 16.0 introduced the concept of fragments allowing you to render an Array of nodes directly without the need of an enclosing element.Code Example : import React, { Component } from 'react'; import { render } from 'react-dom'; const FruitsList = props => props.fruits.map((fruit, index) => {fruit}); class App extends Component {   constructor() {     super();     this.state = {       fruits: ["Apple", "Mango", "Kiwi", "Strawberry", "Banana"]     };   }   render() {     return (                            );   } } render(, document.getElementById('root'));The component in the example above simply renders an Array of list items with keys and without an enclosing element at the root. This saves an extra and unwanted element from being rendered in the DOM.Numbers & Strings: Components, in addition to rendering Arrays using fragments, were also empowered with the ability to return plain strings, which are rendered as text nodes. This prevents the use of paragraph, span or headline tags for instance when rendering text. Likewise, numbers could be rendered directly.Code Example:import React, { Component } from 'react'; import { render } from 'react-dom'; const App = () => 'This is a valid component!'; render(, document.getElementById('root'));Error Boundaries: Until this release, error management in React was quite painful. Errors arising inside components would often lead to unpredictability and issues with state management and there was no graceful way of handling these issues. React 16.0 introduced a new lifecycle method called componentDidCatch() which could be used to intercept errors in child components, to render a custom error UI. Such components that allow the interception of errors in child components are known as error boundaries. In addition to rendering custom error UIs, error boundary components can also be used to pass data to loggers or monitoring servicesCode Example : import React, { Component } from 'react'; import { render } from 'react-dom'; class ErrorBoundary extends Component {   state = {     error: false   }   componentDidCatch(error, info) {     this.setState({ error: true });   }   render() {     if (this.state.error) {       // You can render any custom fallback UI       return There was an Error!;     }     return this.props.children;   } } class DataBox extends Component {   state = {     data: []   }   componentDidMount() {     // Deliberately throwing an error for demo purposes     throw Error("I threw an error!");   }   render() {     return 'This App is working!'   } } const App = () =>  render(, document.getElementById('root'));Portals: Using portals, components get the ability to render content outside the parent component’s DOM node and into another DOM node on the page. This is an incredible feature as it allows components mounted inside a given node, to render content elsewhere on the UI, without explicitly bringing it inside the hierarchy of the parent node. This is made possible using the createPortal(component, DOMNode) function from the react-dom packageCode Example :import React, { Component } from 'react'; import { render, createPortal } from 'react-dom'; import "./style.css"; const Notice = () => createPortal('This renders outside the parent DOM node', document.getElementById("portal")); class App extends Component {   render() {     return ['Renders in the root div', ]   } } render(, document.getElementById('root'));Improved Server-Side Rendering: Single page apps such as the one React delivers are great for performance because they execute in the client’s browser, but are terrible in terms of search engine optimization (SEO). Additionally, the client has to wait for the application package to load before the app renders. Server side rendering solves these problems by rendering the page on the server so the user sees the content right away before the client version of the app takes over for that incredible experience. React 16.0 introduced a new and rewritten server renderer that supports streaming which allows data to be streamed to the client’s browser as it is processed on the server. This naturally boosts performance and is approximately 4x faster than React 15.x’ SSR system.Reduced file size: React 16.0 is smaller than its predecessor, with approximately 32% smaller codebase. This results in more optimised app bundles and consequently a faster load time on the client.Support for custom DOM attributes: Any HTML or SVG attributes that React does not recognize are simply passed on to the DOM to render. This prevents unwanted errors, but more importantly, React 16.0 does away with an internal whitelist mechanism that used to prevent unwanted attributes from getting processed appropriately. This removal of the whitelist mechanism has resulted in a smaller codebase which we discussed earlier.VersionRelease DateFeatures added in the release16.19th November, 2017Support for Portals in React.ChildrenIntroduced the react-reconciler package16.228th November, 2017Fragment as a named exportCode Example 16.329th March, 2018The brand new Context APICode Example :React.createRef()Code Example :React.forwardRef()Code Example :static getDerivedStateFromPropsgetSnapshotBeforeUpdateStrict Mode16.423rd May, 2018Profiler (Experimental)Code Example :16.55th September, 2018Mouse events16.623rd October, 2018React.memo()Code Example : React.lazy() & Code splitting using the Suspense APICode Example :Context for class componentsCode Example :getDerivedStateFromError()16.719th December, 2018React 16.7 was a small release with a few bug fixes and performance enhancements in the React DOM package.React 16.8Released: 6th February, 2019React 16.8 marks a major step forward in React and the way developers can implement function components and use React features.Hooks : So far, the only way to implement local state was to build class components. If a function component needs to store local state in the future, the only way was to refactor the code and convert the function component into a class component. Hooks enables function components to not only implement state, but also add other React features such as lifecycle methods, optionally without the need to convert the component to a class component.Hooks offers a more direct way to interact with React features. If you’re starting a new React project, Hooks offers an alternative and somewhat easier way to implement React features and might be considered as a good replacement for class components in many cases. Hooks is demonstrated later in this article.Companies using ReactReact was built by Facebook to solve real and practical challenges that the teams were facing with Facebook. As a result, it was already battle-tested before release. This and the continuous and progressive development of React has made it the library of choice for companies worldwide. Facebook itself maintains a huge code base of components ( ~50K+ ) and is a big reason why new features are gradually added without sudden deprecations or breaking changes.All of these factors contribute to an industry grade library. It is no wonder that interest for React has grown tremendously over the past 3+ years. Here’s a Google Trends graph demonstrating React’s popularity when compared to Angular, over the past 3 years.Some of the big & popular names using React in production include:AirBnbAmerican ExpressAir New ZealandAlgoliaAmazon VideoAtlassianAuth0AutomatticBBCBitlyBoxChrysler.comCloudFlareCodecademyCourseraDailymotionDeezerDiscordDisqusDockerDropboxeBayExpediaFacebook (Obviously)Fiatusa.comFiverrFlipboardFlipkartFree Code CampFreechargeGrammarlyHashnodeHousing.comHubSpotIGNIMDBImgurInstagramIntuitJeep.comKhan AcademyMagic BusMonster IndiaNHLNaukri.comNBC TV NetworkNetflixNew York TimesNFLNordstromOpenGovPaper by FiftyThreePayPalPeriscopePostmanPractoRackspaceRalph LaurenRedditRecast.AIReuters TVSalesforceShure UKSkyscannerSpotify Web PlayerSquarespaceTreeboTuneIn RadioTwitter - FabricUberUdacityWalmarWhatsApp for WebWixWolfram AlphaWordPress.comZapierZendeskThis is of course a small list, compared to the thousands of sites and apps that are built using React and its ecosystem of products.New features of React 16.8React 16.8 added the incredible Hooks API, giving developers a more direct and simpler way to implement React features such as state and lifecycle methods in function components, without the need to build or convert function to class components. In addition to these abilities, the API is extensible and allows you to write your own hooks as well.Hooks is an opt-in feature and is backward compatible. And while it offers a replacement to class components, their inclusion does not mean that class components are going away. The React team has no plans to do away with class components.As the name implies, Hooks allows your function component to hook into React features such as state and lifecycle methods. This opens up your components to a number of possibilities. For instance, you can upgrade your static function component to include local state in about 2 lines of code, without the need to structurally refactor the component in any form.Additionally, developers can write their own hooks to extend the pattern. Custom hooks can use the built-in hooks to create customised behaviour which can be reused.The Hooks API consists of two fundamental and primary hooks which are explained below. In addition to these fundamental hooks, there are a number of auxiliary hooks which can be used for advanced behaviour. Let’s examine these, one by one.useState : The useState hook is the most fundamental hook that simply aims to bring state management to an otherwise stateless function component. Many components written initially without local state in mind, benefit from easy adoption of state without refactoring needed.The code below is a simple counter which started off as the following basic component:Before Using Hooksconst App = ({count}) => ({count});To turn this into a stateful counter, we need two more ingredients:A local state variable called “Count”Buttons to invoke functions that increment and decrement the value of the “Count” variable.Let’s say we have buttons in place, to bring the state variable to life, we can use the useState() hook. So, our simple component changes as follows:After using the useState() Hookconst App = () => { const [count, setCount] = useState(0);  return (        {count}     setCount(count + 1)}>Increment     setCount(count - 1)}>Decrement      ); }The statement const [count, setCount] = useState(0) creates a local state variable named “Count” with an initial value of 0, as initialized by the useState() method. To modify the value of the “Count” variable, we’ve declared a function called “setCount” which we can use to increment or decrement the value of the “Count” variable.This is really simple to understand and works brilliantly. We have two buttons named Increment and Decrement and they both invoke the “setCount()” method which gets direct access to the “count” variable to update directly.Code Example on (StackBlitz) :import React , {useState} from 'react'; import { render } from 'react-dom'; import "./style.css"; const App = () => { const[count, setCount]= useState(0); return( {count} setCount(count +1)}>Increment setCount(count -1)}>Decrement ); } render(, document.getElementById('root'));useEffect : The useEffect hook enables a function component to implement effects such as fetching data from an API, which are usually achieved using the componentDidMount() and the componentDidUpdate() methods in class components. Once again, it is important to iterate that hooks are opt-in which is what makes them flexible and useful.Here’s the syntax for the useEffect() hook:const App = () => {  const [joke, setJoke] = useState("Please wait..."); useEffect(() => {    axios("https://icanhazdadjoke.com", {      headers: {        "Accept": "application/json",        "User-Agent": "Zeolearn"      }    }).then(res => setJoke(res.data.joke));  },[]);   return ({joke}); }Code Example:import React , {useState, useEffect} from 'react'; import { render } from 'react-dom'; import "./style.css"; import axios from "axios"; const App = () => { const[joke, setJoke]= useState("Please wait..."); useEffect(() => { axios("https://icanhazdadjoke.com", { headers: { "Accept":"application/json", "User-Agent":"Zeolearn" } }).then(res => setJoke(res.data.joke)); },[]); return(Dad says,"{joke}"); } render(, document.getElementById('root'));The code above fetches a random dad joke from the “icanhazdadjoke.com” API.  When the data is fetched, we’re using the setJoke() method as provided by the useState() hook to update the joke into a local state variable named “joke”. You’ll notice the initial value of “joke” is set to “Please wait…”.This will render right away while useEffect() runs and fetches the joke. Once the joke is fetched and the state updated, the component re-renders and you can see the joke on the screen.But behind all this, there is an important caveat. Note the second argument to the useEffect() function, an empty array. If you remove this array, you’ll get a weird problem where the component keeps re-rendering again and again and you see a new joke update repeatedly. This happens because unlike componentDidMount(), useEffect() runs both on mount and update, so whenever a component re-renders, the hook runs again, updates, re-renders and the process repeats.To stop this behaviour, a second argument, an Array may be passed as shown above. This array should ideally contain a list of variables which you need to monitor. These could also be props. Whenever the component re-renders, the state or prop mentioned in the array is compared with the previous value and if found same, the component doesn’t re-run the hook. This also happens when there is nothing to compare, as in the case of a blank array, which is what we’ve used here. This is, however, not the best of practices and may lead to bugs since React defers execution of the hook until after the DOM has been updated/repainted.The useEffect() function can also return, which is somewhat equivalent to componentWillUnmount() and can be used for unsubscribing from publisher-subscriber type APIs such as WebSockets.Besides the above two hooks, there are other hooks that the API offers:useReducer : If you’ve ever used Redux, then the useReducer() hook may feel a bit familiar. Usually, the useState() hook is sufficient for updating the state. But when elaborate behaviour is sought, useReducer can be used to declare a function that returns state after updates. The reducer function receives state and action. Actions can be used to trigger custom behaviour that updates state in the reducer.Thereafter, buttons or other UI elements may be used to “dispatch” actions which will trigger the reducer.This hook can be used as follows:const [state, dispatch] = useReducer(reducer, {count: 0});Here, reducer is a function that accepts state and action. The second argument to the useReducer function is the initial state of the state variable.Code Example : import React , {useReducer, useEffect} from 'react'; import { render } from 'react-dom'; import "./style.css"; import axios from "axios"; const reducer = (state, action) => { switch(action.type){ case'ticktock': return{ count: state.count +1}; case'reset': return{ count:0}; } } const timer; const App = () => { const[state, dispatch]= useReducer(reducer,{count:0}); return( {state.count} { timer = setInterval(() => dispatch({ type: 'ticktock' }), 1000); }}>Start { clearInterval(timer); dispatch({ type: 'reset' }); }}>Stop&Reset ); } render(, document.getElementById('root'));In the code example above, we have a reducer function that offers two state, “start” and “reset”. The “start” action simply increments the count by 1, while “reset” sets it to 0.The Start button then instantiates a setInterval timer that keeps dispatching the “start” action, which keeps incrementing the count every second.The Reset button clears the timer and dispatches the “reset” action which resets the count back to 0.useReducer is best used when you have complex state logic and useState is not enough.Here’s a summary of other available hooks in the v16.8 release:useCallback : The useCallback hook enables you to implement a memoization enriched callback function which enables an equality check between a function and inputs, to check if renders should be performed. This is equivalent in concept to the shouldComponentUpdate function that the PureComponent allows you to implement.useMemo : This hook enables you to pass in a function and an array of input values. The function will only be recomputed if the input values change. This, like the useCallback, enables you to implement equality check based optimizations and prevent unwanted renders.useRef : This hook is useful for accessing refs and initializing them to a given value.useImperativeHandle : This hook enables you to control the object that is exposed to a parent component when using a ref. By using this hook, you can devise custom behaviour that would be available to the parent using the .current property.useLayoutEffect : This hook is similar to the useEffect hook but it is invoked synchronously after the DOM has been mutated and updated. This allows you to read elements from the DOM directly. As a result, this can block updates and hence should ideally be avoided.useDebugValue : This hook is used to display a custom label for hooks in the React DevTools.To summarize, v16.8’s revolutionary, Hooks API opens the door to a whole new way of developing React components.Upgrading to React v16.8.xUpgrading to v16.8 is a relatively simple affair, mainly because it doesn’t introduce breaking changes unless you’re on a very old branch. Team React ensures that incremental upgrades do not introduce sudden API changes or breaking changes that would cause an app to crash or behave erratically.Likewise, if you’re anywhere on the 16.0 branch already, you can conveniently upgrade to 16.8.x by either downloading and installing both react and react-dom packages using npm or yarn, or using the CDN links to unpkg.com, as listed here https://reactjs.org/docs/add-react-to-a-website.html If you’ve used create-react-app to setup your React project, then you can edit the package.json to upgrade versions of react-scripts, react and react-dom to their latest versions before running npm install to download and upgrade the packages.Into the futureA product’s future depends on how well it is embraced by consumers. Many products are built first and attempts are made to entice developers to create a demand. React isn’t one of those frameworks. It was born from the kiln of production at Facebook and it powers more than 50 thousand components and more growing every day. And besides Facebook, React now empowers thousands of companies to write and design scalable UIs that are highly performant with a fantastic developer experience.It is thus, quite natural that the team at Facebook is hard at work, developing the next cutting edge edition of React. Over the years, a vibrant and global community of React developers have sprung up and they’re actively contributing to the ecosystem, in much the same fervour as seen during the days of jQuery.With that said, React is poised to take a leap forward. React 16.x has paved the way for the future by introducing the “Fiber” reconciliation engine and a slew of super cool features such as Context, Hooks and the Suspense API.Going forward, React’s next big feature will land in Q2 of 2019. Concurrent Rendering would allow React to prioritize updates such that CPU usage is optimised for high-priority tasks first, thereby massively improving the user experience.Another feature that is expected to land later in 2019 is Suspense for Data Fetching. This API will allow components to display a fallback UI if asynchronous data fetching is taking more time than a prescribed limit. This ensures that the UI is responsive and displays indicators for the user to understand data fetch latencies in a better way.To summarize, React 16.x is going to get a whole lot better before the leap to v17.In ConclusionThe team at Facebook have done a commendable job with React. Their commitment to improving the user experience as well as the developer experience is seen by the incredible array of features that are released from time to time. Whether it is the Context API, or Hooks or the upcoming concurrent rendering, React is the battle-tested weapon of choice for building your next winning site or even a mobile app!
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What’s New in React 16.8

What’s New in React 16.8

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What is React?React is a library by Facebook that allows you to create super performant user interfaces. React allows you to disintegrate a user interface into components, a functional unit of an inte...
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Docker vs. Kubernetes

Containers are a virtualization technology; however, they do not virtualize a physical server. Instead, a container is operating-system-level virtualization. What this means is that containers share the operating system kernel provided by the host among themselves along with the host.Container ArchitectureThe figure shows all the technical layers that enable containers. The bottommost layer provides the core infrastructure in terms of network, storage, load balancers, and network cards. At the top of the infrastructure is the compute layer, consisting of either a physical server or both physical as well as virtual servers on top of a physical server. This layer contains the operating system with the ability to host containers. The operating system provides the execution driver that the layers above use to call kernel code and objects to execute containers.What is DockerDocker is used to manage, build and secure business-critical applications without the fear of infrastructure or technology lock-in.Docker provides management features to Windows containers. It comprises of two executables:Docker daemonDocker clientThe Docker daemon is the workhorse for managing containersImportant features of docker :Application isolationSwarm (Clustering and Scheduling tool)ServicesSecurity ManagementEasy and Faster configurationIncrease ProductivityRouting MeshWhy use docker for Development?Easy Deployment.Use any editor/IDE of your choice.You can use a different version of the same programming language.Don’t need to install a bunch of language environment on your system.The development environment is the same as in production.It provides a consistent development environment for the entire team.What is Kubernetes:Kubernetes is a container orchestration system for running and coordinating containerized application across clusters of Machine. It also automates application deployment, scaling and management.Important features of Kubernetes :Managing multiple containers as one entityContainer replicationContainer Auto-ScalingSecurity ManagementVolume managementResource usage monitoringHealth checksService DiscoveryNetworkingLoad BalancingRolling Updates Kubernetes ArchitectureWhat is Docker swarm:Docker swarm is a tool for clustering and scheduling docker containers. It is used to manage a cluster of docker nodes as a single virtual system. It uses the standard docker application programming interface to interface with other tools. Swarm uses the same command line from Docker.This can be understood with the following diagram :It uses three different strategies to determine which nodes each container should run:SpreadBinPackRandomImportant features of Kubernetes :Tightly integrated into docker ecosystemUses its own APIFilteringLoad BalancingService DiscoveryMulti-host NetworkingScheduling systemHow are Kubernetes and Docker Swarm related :Both provide load balancing features.Both facilitate quicker container deployment and scaling.Both have a developer community for help and support.Docker Swarm vs Kubernetes :One needs to understand that Docker and Kubernetes are not competitors. The two systems provide closely related but separate functions.Docker SwarmKubernetesContainer LimitLimited to 95,000 containersLimited to 3,00,000 containersNode SupportSupport 2000+ nodesSupport up to 5000 nodesScalabilityQuick container deployment and scaling even in large containersProvides string guarantees to the cluster states at the expense of speed.Developed ByDocker IncGoogleRecommended Use CaseSmall clusters, Simple architectures, No Multi-User, for small teamsProduction-ready, recommended for any type of containerized environments, big or small, very feature richInstallationSimple installation but the resultant cluster is not comparatively strongComplex installationBut a strong resultant cluster once set upLoad BalancingCapability to execute auto load balancing of traffic between containers in the same clusterManual load balancing is often needed to balance traffic between different containers in different podsGUIThere is no dashboard which makes management complexIt has an inbuilt dashboardRollbacksAutomatic rollback facility available only in docker 17.04 and higherAutomatic rollback with the ability to deploy rolling updatesNetworkingDaemons are connected by overlay networks and the overlay network driver is usedOverlay network is used which lets pods communicate across multiple nodesAvailabilityContainers are restarted on a new host if host failure is encounteredHigh availability, Health checks are performed directly on the podsLet’s understand the differences category wise for the following points :Installation/Setup:Docker Swarm: It only requires two commands to set up a cluster, one at the manager level and another at the worker end.Following are the commands to set up, open terminal and ssh into the machine :$ docker-machine ssh manager1 $ docker swarm init --advertise-addr Kubernetes: In Kubernetes, there are five steps to set up or host the cluster.Step 1: First run the commands to bring up the cluster.Step 2: Then Define your environment.Step 3: Define Pod network.Step 4: Then bring up the dashboard.Step 5: Now, the cluster can be hosted.GUI:Docker Swarm: Docker Swarm is a command line tool. No GUI dashboard is available. One needs to be comfortable with console cli, to fully operate docker swarm.Kubernetes: Kubernetes has a Web-Based kubernetes user interface. It can be used to deploy the containerized application to a kubernetes cluster.NetworkingDocker Swarm: User can encrypt container data traffic while creating an overlay network.Lots of cool things are happening under the hood in docker swarm container networking which makes it easy to deploy production application on multi-host networks. The Node joining a swarm cluster generates an overlay network for services that span every host in the docker swarm and a host-only docker bridge network for containersKubernetes: In Kubernetes, we create network policies which specify how the pods interact with each other. The networking model is a flat network, allowing all pods to interact with one another The network is implemented typically as an overlay. The model needs two CIDRs: one for the services and the other from which pods acquire an IP address.ScalabilityDocker Swarm: After the release of Docker 1.12, we now have orchestration built in which can scale to as many instances as your hosts can allow.Following are the steps to follow:Step 1: Initialize SwarmStep 2: Creating a ServiceStep 3: Testing Fault ToleranceStep 4: Adding an additional manager to Enable Fault toleranceStep 5: Scaling Service with fault toleranceStep 6: Move services from a specific nodeStep 7: Enabling and scaling to a new nodeKubernetes: In Kubernetes we have masters and workers. Kubernetes master nodes act as a control plane for the cluster. The deployment has been designed so that these nodes can be scaled independently of worker nodes to allow for more operational flexibility.Auto-ScalingDocker Swarm: There is no easy way to do this with docker swarm for now. It doesn’t support auto-scaling out of the box. A promising cross-platform autoscaler tool called “Orbiter” can be used that supports swarm mode task auto scaling.Kubernetes: Kubernetes make use of Horizontal Pod Autoscaling , It automatically scales the number of pods in a replication controller, deployment or replica set based on observed CPU utilization.This can be understood with the following diagram :AvailabilityDocker Swarm: we can scale up the number of services running in our cluster. And even after scaling up, we will be able to retain high availability.Use the following command to scale up the service$ docker service scale Angular-App-Container=5in a Docker Swarm setup if you do not want your manager to participate in the proceedings and keep it occupied for only managing the processes, then we can drain the manager from hosting any application.$ docker node update --availability drain Manager-1Kubernetes: There are two different approaches to set up a highly available cluster using kubeadm along with the required infrastructures such as three machines for masters, three machines for workers, full network connectivity between all machines, sudo privileges on all machines, Kubeadm and Kubelet installed on machines and SSH Access.With stacked control plane nodes. This approach requires less infrastructure. The etcd members and control plane nodes are co-located.With an external etcd cluster. This approach requires more infrastructure. The control plane nodes and etcd members are separated.Rolling Updates and Roll BacksDocker Swarm: If you have a set of services up and running in swarm cluster and you want to upgrade the version of your services.The common approach is to set your website in Maintenance Mode, If you do it manually.To do this in an automated way by means of orchestration tool we should make use of the following features available in Swarm :Release a new version using docker service update commandUpdate Parallelism using update—parallelism and rollback—parallelism flags.Kubernetes:To rollout or rollback a deployment on a kubernetes cluster use the following steps :Rollout a new versionkubectl patch deployment $DEPLOYMENT \      -p'{"spec":{"template":{"spec":{"containers":[{"name":"site","image":"$HOST/$USER/$IMAGE:$VERSION"}]}}}}'Check the rollout statuskubectl rollout status deployment/@DEPLOYMENTRead the Deployment historykubectl rollout history deployment/$DEPLOYMENT kubectl rollout history deployment/$DEPLOYMENT --revision 42Rollback to the previously deployed versionkubectl rollout undo deployment/$DEPLOYMENTRollback to a specific previously deployed versionkubectl rollout undo deployment/$DEPLOYMENT --to-revision 21 Load BalancingDocker Swarm: The swarm internal networking mesh allows every node in the cluster to accept connections to any service port published in the swarm by routing all incoming requests to available nodes hosting service with the published port.Ingress routing, the load balancer can be set to use the swarm private IP addresses without concern of which node is hosting what service.For consistency, the load balancer will be deployed on its own single node swarm. Kubernetes: The most basic type of load balancing in Kubernetes is load distribution, easy to implement at dispatch level.The most popular and in many ways the most flexible way of load balancing is Ingress, which operates with the help of a controller specialized in pod (kubernetes). It includes an ingress resource which contains a set of rules for governing traffic and a daemon which applies those rules.The controller has an in-built feature for load balancing with some sophisticated capabilities.The configurable rules contained in an Ingress resource allow very detailed and highly granular load balancing, which can be customized to suit both the functional requirements of the application and the conditions under which it operates.Data Volumes:Docker Swarm: Volumes are directories that are stored outside of the container’s filesystem and which hold reusable and shareable data that can persist even when containers are terminated. This data can be reused by the same service on redeployment or shared with other services.Swarm is not as mature as Kubernetes. It only has one type of volume natively, which is a volume shared between the container and its docker host, but It won’t do the job in a distributed application. It is only helpful locally.Kubernetes: At its core, a volume is just a directory, possibly with some data in it, which is accessible to the containers in a pod. How that directory comes to be, the medium that backs it, and the contents of it are determined by the volume type used.There are many volume types :LocalNode-Hosted Volumes (emptyDir, hostpath and duh)Cloud hostedgcePersistentDisk (Google Cloud)awsElasticBlockStore (Amazon Cloud – AWS)AzureDiskVolume ( Microsoft Cloud -Azure)Logging and MonitoringDocker Swarm: Swarm has two primary log destinations daemon log (events generated by docker service) and container logs(generated by containers). It appends its own data to existing logs.Following commands can be used to show logs per container as well as per service basis.Per Container :docker logs Per Service:docker service logs Kubernetes: In Kubernetes, as requests get routed between services running on different nodes, it is often imperative to analyze distributed logs together while debugging issues.Typically, three components make up a logging system in Kubernetes :Log Aggregator: It collects logs from pods running on different nodes and routes them to a central location. It should be efficient, dynamic and extensible.Log Collector/Storage/Search: It stores the logs from log aggregators and provides an interface to search logs as well. It also provides storage management and archival of logs.Alerting and UI: The key feature of log analysis of distributed applications is virtualization. A good UI with query capabilities, Custom Dashboard makes it easier to navigate through application logs, correlate and debug issues.ContainersPackaging software into standardized units for shipment, development and deployment is called a container. It included everything to run an application be it code, runtime, system tools, settings and system libraries. Containers are available for both Linux and windows application.Following are the architecture diagram of a containerized application :Benefits of Containers :Great EfficiencyBetter Application DevelopmentConsistent OperationMinimum overheadIncreased PortabilityContainer Use Cases :Support for microservices architectureEasier deployment of repetitive jobs and tasksDevOps support for CI(Continuous Integration) and CD(Continuous Deployment)Existing application refactoring for containers.Existing application lift and shift on cloud.Containers vs Virtual Machines :One shouldn’t get confused container technology with virtual machine technology. Virtual Machine runs on a hypervisor environment whereas container shares the same host OS and is lighter in size compared to a virtual machine.Container takes seconds to start whereas the virtual machine might take minutes to start.Difference between Container and Virtual Machine Architecture :Build and Deploy Containers with Docker:Docker launched in 2013 and revolutionized the application development industry by democratizing software containers.  In June 2015 docker donated docker specification and runc to OCI (Open container Initiative)Manage containers with KubernetesKubernetes(K8s) is a popular open-source container management system. It offers some unique features such as Traffic Load Balancing, Scaling, Rolling updates, Scheduling and Self-healing(automatic restarts)Features of Kubernetes :Automatic binpackingSelf-HealingStorage OrchestrationSecret and Configuration ManagementService discovery and load balancingAutomated rollouts and rollbacksHorizontal ScalingBatch ExecutionCase Studies :IBM offers managed kubernetes container service and image registry to provide a fully secure end to end platform for its enterprise customers.NAIC leverages kubernetes which helps their developer to create rapid prototypes far faster than they used to.Ocado Technology leverages kubernetes which help them speeding the idea to the implementation process. They have experienced feature go the production from development in a week now. Kubernetes give their team the ability to have more fine-grained resource allocation.First, we must create a docker image and then push it to a container registry before referring it in a kubernetes pod.Using Docker with Kubernetes:There is a saying that Docker is like an airplane and Kubernetes is like an airport. You need both.Container platform is provided by a company called Docker.Following are the steps to package and deploy your application :Step 1: Build the container imagedocker build -t gcr.io/${PROJECT_ID}/hello-app:v1 .Verify that the build process was successfuldocker imagesStep 2: Upload the container imagedocker push gcr.io/${PROJECT_ID}/hello-app:v1Step 3: Run your container locally(optional)docker run --rm -p 8080:8080 gcr.io/${PROJECT_ID}/hello-app:v1Step 4: Create a container clusterIn case of google GCPgcloud container clusters create hello-cluster --num-nodes=3 gcloud compute instances listStep 5: Deploy your applicationkubectl run hello-web --image=gcr.io/${PROJECT_ID}/hello-app:v1 --port 8080 kubectl get podsStep 6: Expose your application on the internetkubectl expose deployment hello-web --type=LoadBalancer --port 80 --target-port 8080Step 7: Scale up your applicationkubectl scale deployment hello-web --replicas=3 kubectl get deployment hello-webStep 8: Deploy a new version of your app.docker build -t gcr.io/${PROJECT_ID}/hello-app:v2 .Push image to the registrydocker push gcr.io/${PROJECT_ID}/hello-app:v2Apply a rolling update to the existing deployment with an image updatekubectl set image deployment/hello-web hello-web=gcr.io/${PROJECT_ID}/hello-app:v2Finally cleaning up using:kubectl delete service hello-webKubernetes high level Component Architecture.ConclusionDocker swarm is easy to set up but has less feature set, also one needs to be comfortable with command lines for dashboard view. This is recommended to use when you need less number of nodes to manage.However Kubernetes has a lot of features with a GUI based Dashboard which gives a complete picture to manage your containers, scaling, finding any errors. This is recommended for a big, highly scalable system that needs 1,000s of pods to be functional. 
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Docker vs. Kubernetes

Docker vs. Kubernetes

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Containers are a virtualization technology; however, they do not virtualize a physical server. Instead, a container is operating-system-level virtualization. What this means is that containers share t...
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How to Upgrade to the Latest Version of Node Js (Linux, Ubuntu, Osx, Others)

Just like all the other vast number of open-source frameworks, Node.js is one of the highly recommended technologies. Minor updates turn out at regular intervals to keep the architecture steady and solid as well as provides enhanced security among most of the version branches.  There are many upcoming techniques to update Node JS to the latest version. Each method is designed keeping in mind the various operating systems. So there is no reason to hold you back from upgrading Node.js to its latest version. Due to this Node JS development company also focusing on this. Here is an accumulated list of ways to introduce the most up to date version of Node. These methods are both simple and effective ways to upgrade to the latest version release. This article explores the upgrades on Linux-based systems, Windows, as well as Mac OS machines. Make sure that before you begin, check the version of Node.js you're using at present. You can do so by merely running the command ‘node – v’ in a command line terminal. Every now and then, when you upgrading to Node JS latest version, you have to make a great deal of edits to the ‘Dockerfile’ as well as ‘circle.yml’ files. This can be a very monotonous and tedious errand.To simplify this undertaking, you can run an ‘update-node’ in the directory which contains most of your archives. You need to enter the new Node.js version as one of the parameters using the command$ update-node One key thing to remember is that ‘update-node’ does not submit or push any changes on its own. You can still manage the outcome of the update. This allows you to initially check what are the updates performed by the ‘update-node’ command. To submit and push for every single change in a repository, you are better off using ‘forany’ command.Here is the list of ways how to update node js to the latest version. It covers all the three primary operating systems: Linux, Windows, and Mac.Ways to update Node JS on LinuxFor Linux users, there are multiple ways or steps to upgrade Node js in your system, three ways to be specific. The first option is probably the highly recommended method. The simplistic nature and highly effective methods are very popular among developers and coders. In worst case scenarios, if you are not able to use the Node Version Manager, you can alternatively use the package manager or the binary packages.Option 1: Updating Node JS with the help of Node Version ManagerA common terminology for Node Version Manager is NVM. It is probably the best way to upgrade to the latest version of the technology. You also need to get your hands on a C++ compiler. You will also need to use libssl-dev package and the build-essential package. You can first run the update and then get the packages. You can run the update by the commands1    sudo apt-get update2    sudo apt-get install build-essential checkinstall libssl-devIn the event you need to update the NVM or install a version of it, you can get the installation script from any of the online portals or website.Once it is installed, you will need to reboot the terminal for the changes to take effect. For checking if the installation was successful you could run the command ‘–v nvm.’ Based on the output, you can figure out if the installation was done correctly. This is a one-time effort after which it is a walk in the park to get Node JS updated to the latest version. For information on the currently installed versions, you can use nvm Isand. To get a list of available versions, you can use nvm Is-remote.Once you are confident about the version details of the Node that you want to install, you can install it by running the command ‘nvm install .’ You can also check out the list of nvm versions and set a default version using alias command.Option 2: Update Node JS version through a Package Manager If at all you are not able to upgrade the Node JS version using NVM, then the next best option is to use a package manager. Node package manager or NPM can be instrumental in discovering, using and sharing codes as well as to manage any dependencies on previous versions of Node JS. The node package manager is part of installation setup for Node JS. To check out the current version of the package manager, you can use the command ‘npm –v.’ For installing the most recent update, you can use the command ‘install npm @ latest –g.’ Once updated, you can check if the operation was successful by running ‘npm –v.’You will also need to have access to the Node Package Manager ‘n module’. You can clear the npm cache with the command ‘npm cache clean –f.’ Once the cache is cleared, run the command which will install the n module, i.e., ‘sudo npm install –g n.’ Now you can choose the most stable version of Node, or you can specify the version using the version number.Option 3: Update Node using Binary Package k(Ubuntu/Debian/CentOS and Linux)This is least sought out path to update the version of Node JS and is probably the last resort. You can go to the official downloads page for Node JS to get your hands on the 32-bit binary file or the 64-bit binary file for Linux. You can use the command ‘wget https://nodejs.org/dist/v6.9.2/node-v6.9.2-linux-x64.tar.xz‘. You can download this file through a browser or with the help of a console. However, it is important to remember that the Node JS version could possibly change with the release of newer updates. Extract the file and install it using ‘xz-utils.’ The next step is to install the binary package using the command ‘1 tar -C /usr/local --strip-components 1 -xJf node-v6.9.2-linux.x64.tar.xz’.Updating the Node JS Version (Windows and Mac OS) with Installers Once you check out the downloads page for Node JS, you can see that there are binary packages for Windows and Mac. You can also use the existing and pre-defined installers, i.e. *.msi for Windows platform and *.pkg for a Mac. This can hasten and ease the installation process. Once you download the file in your local storage and run the file, the wizard takes over and handles the rest of the installation process. Once an update has been downloaded, the version numbers of the Node in the NPM will automatically be updated from the older versions. You can also learn more about the Important Tips and Features in Node.js to Practice here.ConclusionThe above steps will ensure that your version of Node Js is upgraded and the updates are completed. However, this does not make any changes in your packages or modules. Updating the packages along with the dependencies are also necessary. This is required for increasing compatibility levels and enhance security. Sometimes, Node JS modules become obsolete and are not compatible with the latest versions. You can use ‘npm update’ command to update the command modules to their most recent version.
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How to Upgrade to the Latest Version of Node Js  (Linux, Ubuntu, Osx, Others)

How to Upgrade to the Latest Version of Node Js (Linux, Ubuntu, Osx, Others)

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Just like all the other vast number of open-source frameworks, Node.js is one of the highly recommended technologies. Minor updates turn out at regular intervals to keep the architecture steady and so...
Continue reading

Top Artificial Intelligence Implications You Can’t Ignore in 2019

Artificial Intelligence has paved its way to different industries and revamped the business arena. According to International Data Corporation, Artificial Intelligence will aid 40% of digital transformations in 2019, and by 2025, the technology will facilitate 75% of commercial enterprise apps.According to Ibrahim Hadid, Director of Research at the Linux Foundation, ‘2019 is going to be the year of open source Artificial Intelligence’ and Peter Trepp, CEO FaceFirst says, ‘Artificial Intelligence will help elevate in-store customer experiences.’Artificial Intelligence has hooked startups and enterprises, but the technology will soon become an integral and consistent feature in business across industries. The massive potential of AI is evident from the AI industry’s expected market value of $190 billion by 2025.This article explores the top AI implications for business strategies.More Transparency in Artificial IntelligenceArtificial Intelligence aims to behave like humans, in terms of wisdom, intelligence, and decision-making. However, it gives birth to several ethical reservations and cannot be adequately utilized unless blindly trusted. It is assumed that AI accesses users’ data, search history, and other personal data. The misconceptions and inability to comprehend AI’s decision-making is a significant hurdle in its success and will be addressed in 2019. This will also be the beginning of the best AI prediction strategies for 2019.It is difficult to know the pattern adopted by Artificial Intelligence to reach a conclusion. However, it is possible to identify factors which contribute to the decision. In 2019, AI will become more transparent, to build the trust of businesses and consumers.Even businesses are reluctant to adopt Artificial Intelligence, to avoid liabilities in the future. Therefore, we will witness enhanced transparency for maximum utility for AI. Recently IBM revealed its technology to track the decision-making process of AI. It helps in monitoring the decisions, process of deciding, potential biases in data, decision weighting, and more.Enhanced transparency will encourage business to use AI in its processes and foster trust in the consumers’ minds. Artificial Intelligence will Enhance Job OpportunitiesUnlike popular belief that robots and machines will reduce job roles for humans, Artificial Intelligence will create more jobs in 2019 than it will take away, creating a positive impact on the job market. Robotics and AI will create 7 million job opportunities in the United Kingdom, between 2017 and 2037. Research by Gartner shows that AI will take away 1.8 million jobs and generate 2.3 million jobs. Most of the jobs will be created in health, hospitality, and technical services while reducing jobs in logistics, transport, and public administration.Artificial Intelligence shares responsibilities for repetitive tasks. For example, chatbots resolve customer queries about product deliveries, order updates, availability of products, expected delivery time, and so. The data is automatically fetched from the system using keywords. The customer service representatives can spend time on productive tasks instead. However, in some jobs, the final decision remains with humans, such as of doctors. AI helps in data analytics and diagnostics, but the human professionals make the last call.AI will replace humans in administrative work but create more jobs in intellect-based industries, such as healthcare and law. AI Assistants will Become Smarter and Wiser2019 will witness smarter and wiser digital assistants, aided by Artificial Intelligence. This will pave the path of the top Artificial Intelligence future trends for 2019. Users are already familiar will the AI assistants in the firm of Alexa, Cortana, and Siri; therefore, the acceptance of AI assistant will be easier for consumers. According to Global Web Index, 1 out of 5 adults uses voice search in mobile phones at least once a month.Alexa, Cortana, and Siri still have a robotic touch to their conversations. The coming year will see more natural and meaningful discussions with the AI assistants. Don’t be surprised if AI assistants enable online and offline interactions with televisions, refrigerators, and other electronic devices.Siri, Alexa, and other digital assistant perform basic tasks such as making calls, finding meaning, playing songs, or guiding the way. The assistants will become active in 2019 to plan journeys, suggest lunch and dinner menus, arrange calendars, anticipate behaviors, ordering taxis, recommending eateries, and more.The technology allows AI assistants to better understand humans via extensive exposure to users’ communication, behaviors, habits, preferences, interests, and desires. The natural language algorithms will efficiently encode the user’s speech into readable data for computers, enabling AI assistants to help their human users in a wide array of tasks.Be ready to interact with AI assistants at home, work, recreational areas, hospitals, and beyond.Artificial Intelligence is here to stay and grow. 2019 will bring some jaw-dropping AI uses that will leave you stunned.Look for your Artificial Intelligence platform with the best web development company and strengthen your competitive edge today!
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Top Artificial Intelligence Implications You Can’t Ignore in 2019

Top Artificial Intelligence Implications You Can’t Ignore in 2019

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Artificial Intelligence has paved its way to different industries and revamped the business arena. According to International Data Corporation, Artificial Intelligence will aid 40% of digital transfor...
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Hadoop and Its Core Features: The Most Popular Tool of Big Data

If you are looking to learn Hadoop, this is the right stage you have landed at. In this Hadoop tutorial, you can learn from the basics to the advance level in a very simplified manner. Are you looking to learn Hadoop? If yes, then you have landed on the right page. This tutorial will take you through the basics to the advanced level of Hadoop in a very simplified manner. So without wasting much time let’s plunge into the details of Hadoop along with the suitable practical scenarios. In this Hadoop tutorial, we are going to coverHow it all started What is all about big dataBig data and Hadoop: restaurant illustration What is HadoopHadoop solutions Hadoop features Hadoop mandatory services to set upHadoop tutorial: how did it start?Before starting the technical part in this Hadoop tutorial, let’s discuss some exciting story about how Hadoop came into existence. Hadoop was started by two people Doug Cutting and Mike Cafarella, who were in mission mode to build a search engine which could have a capability to index 1 Billion pages. They had undergone research for that, and they came to know that it requires to set up a system which hardware costs half million dollars and a monthly running cost of $30,000, which is a considerable capital expenditure for them. However, soon they realized that it is tough for their Architecture to support one billion of web pages on it.In 2003 they red a paper clip about the Google Distributed File System known as ‘GFS’ which used in Google's production. Now the news on GFS is something exactly they were looking, and it became a solution for their storage problem of the vast amount of data that's get generated in the process of web crawl and indexing.  Later in 2004 google came up with one more invention which is MapReduce into this technical world. These two inventions from Google led to the origin of software called “HADOOP.” Doug has given the following on the google contribution to the development of the Hadoop framework“Google is living a few years in the future and sending the rest of us messages.” So with the above discussion, you would have got an idea about Hadoop and how powerful it is.Hadoop tutorial: what  Big Data is all about?The world is transforming at a rapid speed with the technological advancements in each sector, and we can get the best out of everything. In the olden days we used to have landlines, but now we have moved to smartphones which can perform all most all works like a PC. Similarly, we used to store the data into floppy drives back in the 90’s.  And these floppy drives had been replaced by the hard disks due to its limited storage and processing capability. But now we can store terabytes of data on to Cloud, without bothering about the storage limitations. Let’s consider some crucial sources from where the significant portions of data get generated. Have you heard about IoT? Now it’s been a disruptive technology in all industries. IoT connects all physical devices to the internet or to the other methods, to perform certain things without human intervention in between the Devices. Here let us discuss the best example of IoT is smart Air conditioners in homes. The Smart AC’s are connected to the internet, and it can adjust the temperature inside the room by monitoring the outside temperature.  With this, you could get an idea of how much data has been generated by the IoT devices by the massive no of different devices worldwide and its share in contributing to big data.Now We should consider the main contributor to big data is social media. Social media is one of the biggest reason for the evolution of big data, and it gives the information about population behavior. The following image will give you a clear idea about the amount of data generated by social media every minute.Apart from the data rate of data generating, the second challenge comes with the unstructured or unorganized data sets which make processing a problem.Big Data & Hadoop - AnalogyHere let’s take a restaurant as an example to get a better understanding of the problems related to bid data and how Hadoop solves it.John is a businessman who has opened a  small restaurant. Initially, he had one chef with one food shelf, they started receiving two orders per hour and the restaurant set up was enough for handling orders.Here we are going to compare restaurant scenario with a traditional system where data was used to generated in a consistent way, and our storage system like  RDBMS was capable enough for processing it just like John’s chef. Here in this scenario, the chef is compared with traditional processing and the food shelf is compared with data storage as shown in the above image. After a few months, Bob thought to expand his business and started taking online orders as well by adding few more Cuisines to the menu to serve a large number of people.  The Orders were rose to an alarming level of 10 per hour, and it became tough for a single chef to handle the extra work. Aware of that situation John started to think about the measures that he needs to take for handling the case. The same thing happened with big data; suddenly the data started generating at a rapid rate because of the data growth factors such as social media, etc. now the traditional system, just like a chef in john restaurant unable to cope up with the situation. Thus, there was a need for different kind of solution to tackle the problem.After a lot of research john came up with an idea to increase the no of chefs to four, and everything is functioning well, and they can handle the orders.  But soon this solution was lead to another problem which is a food shelf since four chefs were sharing the single shelf it became a hurdle for them. John started to think again to put a stop to the situation.In the same fashion, to handle the data processing of vast volumes of data many processing units were installed( just like John hired extra chefs to manage the orders). But even in this case setting up additional processing units was not a solution to the problem. Here, the real bottleneck is the central unit for data storage.  In other words, the whole performance of the processing units was driven by the primary Storage. If it is not efficient the entire system would be affected. Hence, there was a storage problem to resolve.  John came up with another idea to divide the chefs into two categories, i.e., junior and senior chefs and assigned each junior chef with a food shelf. Each senior chef was appointed with two junior chefs to prepare meat & sauce.  According to John's plan, among two junior chefs, one will make the meat, and the other will prepare sauce, so both the ingredients will be transferred to the head chef and he will mix both the ingredients to prepare the final order. Hadoop works similarly as John’s restaurant does. The way food shelves distributed in John’s restaurant, in the same fashion the data will be stored in a distributed manner with replications, to give fault tolerance.  For parallel processing, first, the data gets processed by the slave nodes, where, it is stored for a while to obtain intermediate results, and later these intermediate results are merged by the master node to produce the final result.The above analogy must have given you a fair idea about how big data is a problem statement and how Hadoop is a solution for it. As we discussed in the above scenario, there are three significant challenges with Big Data. The first problem is storing a massive amount of data:Saving vast amounts of data in a traditional storage system is not possible. The reason is known to everyone, the storage capacity is limited, but the data is generating at a rapid speed.The second problem is storing diversified data: As we know data storage is a problem, but even there is another problem is associated with it which is storing heterogeneous data. The data is not only colossal, but it presented in different formats such as semi-structured, structured, and unstructured. So you have to make sure that you have a multitasking system to store the diversified data which is generating from different sources and in different formats. The final problem is the processing speed:As we all aware big data consists of a significant number of datasets it is tough to prepare the data in a less span of time.To overcome the storage issue as well as a processing issue there are two components created in Hadoop which are - HDFS & YARN. HDFS stands for Hadoop Distributed File System, it resolves the storage problem by storing the data in a distributed manner, and it is easily scalable. YARN Stands for  Yet Another Resource Negotiator, and it is designed to decrease the processing time drastically. Let’s move ahead and understand what Hadoop is?What is Hadoop?Hadoop is an open source programming structure which is intended to store the colossal volumes of informational collections distributedly on expansive groups of the ware. Hadoop programming structured on a paper discharge by google on MapReduce, and it can apply to all thought of practical programming. Hadoop was produced in Java programming dialect, and Doug Cutting and Michael J. Cafarella structured it.Hadoop Key features:Reliability:When machines are working in tandem mode process if one device fails, there is another device ready to take charge of the responsibility of it and perform the functions of it without any interruption in between. Hadoop is designed with an inbuilt fault tolerance feature which makes it highly reliable. Economical:Hadoop can operate on standard commodity hardware (your PC or Laptop). For instance, in a mini Hadoop cluster, all data nodes requires a standard configuration setup like 5- 10 terabytes hard disk,  Xeon processor and 8-16 GB RAM is enough. So, the cost for Hadoop is very economical and easy to operate on your regular PC or Laptops. And more importantly, Hadoop is an open source software, so, you need not pay costs like licensing. Scalability: Hadoop has the inbuilt capacity of integrating with cloud computing technology. Especially, when Hadoop is installed on cloud technologies you need not consider the storage problem. You can arrange the systems and hardware according to your requirements.Flexibility:Hadoop is very flexible when it comes to its performance in dealing with different methods of data. Hadoop is a flexibility feature to process the different kinds of data such as unstructured, semi-structured, and structured data. The above are the four features which are helping in Hadoop as the best solution for significant data challenges. Let’s move forward and learn what the core components of Hadoop are.Core components of HadoopWhile you are setting up the Hadoop cluster, you will be provided with many services to choose, but among them, two are more mandatory to select which are HDFS (storage) and YARN (processing).  Let’s get more details about these two.HDFS ( Hadoop distributed file system) The main components of HDFS  are Namenode and Datanode. First, let’s discuss the NameNodeYARN:YARN consists of two essential components Resource Manager and Node Manager Resource Manager:It works at the cluster level and takes responsibility to run the master machine. It stores the track of heartbeats from the Node manager. It takes the job submissions and negotiates the first container for executing an application. It consists of two components: Application manager and Scheduler. Conclusion:The above explanations and examples might have given you a brief idea about big data, how does it get generated, problems that related to Big data, and how Hadoop is useful to solve these problems.Happy learning and I will come up with a new post soon.    
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Hadoop and Its Core Features: The Most Popular Tool of Big Data

Hadoop and Its Core Features: The Most Popular Tool of Big Data

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If you are looking to learn Hadoop, this is the right stage you have landed at. In this Hadoop tutorial, you can learn from the basics to the advance level in a very simplified manner. Are you lo...
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Native vs. Cross-Platform Apps – You’ll Be the Winner

Native vs. Cross-platform is an age-old debate. It is a debate that has kept the tech community divided for years. Many claimed to have found the ultimate answer but both cross-platform and native app development technologies are in a constant state of evolution. And due to this changing nature of technologies, it serves to revisit these topics from time to time to find out which of these options is currently in the lead.Both the native and cross-platform apps have a dynamic list of pros and cons. These factors can affect everyone involved with the app, including the app owner, the app users, and the app developers. Now, app developers have preferences based on the technologies they are most comfortable with. Today, however, we will limit the scope of discussion to app owners and users.So, let’s start with the basics.How Important Is the Mobile Application Platform?Apple’s iOS and Google’s Android are by far the biggest and most popular mobile platforms in the world. According to stats, a majority of the global market is captured by Apple and Android stands second.                                                                               Image sourceThe US, for instance, has a bigger iOS market share i.e. 56 per cent compared to 43.5 per cent of Android. Not to forget, there are other smaller platforms such as Windows. This means if you choose your app simply in the global data, you might lose an important target region. That is why you need to choose your app platform wisely. You need to know where your audience is and build your app’s presence on each of those platforms. Now there are two ways to do that - you either build a native app for each platform or create a single cross-platform app supported by multiple platforms. Let’s take a look at how this decision will affect your app:The Basic Difference between Native and Cross-Platform AppNative apps are developed exclusively for a specific platform. These apps are developed in a language compatible with the platform. Apple, for instance, prefers Objective C and Swift for iOS while Google favors Java for Android. Using these acceptable languages, developers can make better use of the innate features of these platforms. A native app developed for Android will not function on iOS and vice versa.Cross-platform apps are compatible with multiple platforms. Due to the market share of Android and iOS, most cross-platform apps are limited to these two operating systems. These apps are developed in HTML and CSS since these standard web technologies are platform independent. There are several cross-platform application development tools that allow developers to create these apps with little trouble. Now that you know the difference between cross-platform and native apps, let’s see how they compare.Performance – Native vs. Cross-PlatformNative apps make the best use of resources and utilize the platform’s capabilities up to its full potential. This means native apps are high performing apps that are fast, responsive, and less likely to crash. If the developers have enough knowledge of the platform they are working on, they can optimize native apps to highlight the best features and capabilities of that platform. Cross-platform apps are often plagued with performance issues. Since they are the built-on one-app-fits-all approach, it is not unusual for these apps to act out on certain devices. Winner - NativeFeatures – Native vs. Cross-Platform Native apps can make use of the device’s native feature, especially with iOS, which runs only on Apple’s proprietary devices. Another great advantage of native apps is that they allow offline features, which is something not easily possible with cross-platform apps. Cross-platform apps cannot utilize the native features of the device because they have limited access to the API. Since they are developed for different devices with varying features, developers usually avoid making assumptions about the available features.Winner - NativeFeasibility – Native vs. Cross-Platform Native app development takes twice as much time as cross-platform apps. The cost is also higher since it usually requires building more than one app. Maintenance is equally time-consuming and costly, as the developers have to identify bugs and problems for each platform and create different updates accordingly. Cross-platform apps are relatively cheaper in terms of development and maintenance. You are investing in a single app and that is all you will have to maintain. However, sometimes, the higher number of issues and bugs outweigh this advantage. Winner – Tie User Experience – Native vs. Cross-Platform The importance of user experience is increasing by the minute, which is why it is the most essential thing that you must ensure in your app. Considering the above stats, this one really is a no-brainer. With better performance, higher speed, and better device utilization, native apps offer a tremendous experience. Designers and developers have more creative freedom to create good-looking apps and smooth functioning apps. Native apps are not just responsive but also intuitive. While developers can create equally intuitive cross-platform apps, such features often come at the cost of speed. It is difficult for the developers and designers to simultaneously fulfill all the UX requirement of multiple platforms. Overall, cross-platform apps do not deliver a desirable user experience. Winner – Native A Fair Conclusion Native apps seem far superior in terms of performance and user experience. This is enough to make them the winner. However, let’s not forget that the choice truly depends on your application. Simple applications like games and content distribution apps are usually developed as a cross-platform app while apps with specific features are native.Cross-platform is also preferred for B2B apps where deployment time is of utmost importance. Many small businesses also opt for cross-platform due to their limited budget. However, compromising performance and user experience for the sake of savings is often counterproductive. It is important for you to choose a platform that meets your needs, requirements, as well as your target audience,  needs to create your winning app.
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Native vs. Cross-Platform Apps – You’ll Be the Winner

Native vs. Cross-Platform Apps – You’ll Be the Winner

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Native vs. Cross-platform is an age-old debate. It is a debate that has kept the tech community divided for years. Many claimed to have found the ultimate answer but both cross-platform and native app...
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How AR can Help Improve Customer Engagement in eCommerce?

The first age of the internet was about information and the next one will be about experiences. The biggest companies of the first generation are built around access to information. Google's main goal is to sort out the world's information. Facebook gives you information about the important moments that occurred in the lives of your near and dear ones.However, the coming generation of big companies is being built around giving people experiences. Snap Chat is meant for in-the-minute experiences with your friends with no digital information left behind. Airbnb plans to make you experience another city like a local.And, with regards to retail, an impact of Augmented Reality on E-commerce customers is changing the whole game. This can be well studied from the stats supporting the statement of how AR improved e-commerce customer engagement. This cutting-edge technology makes it possible for the owners to augment real-world shopping conditions with computer-generated resources, bringing efficiency, clarity, and comfort to the customers. But, how can retailers with little experience in this high-tech tool best use AR to help increase conversions? While there are different strategies, one of the techniques more retailers are utilizing is to improve customer engagement using AR. Here are the tips for increasing the Customer Engagement using AR:Steps to engage e-Commerce customers with ARBy Improving Customer Experience The AR technology gives customers who shop online the opportunity to view the product in form of a model. They can connect with in a similar way they would if they were visiting a physical store. This gives the clients a superior feel of the product and how it fits into their lives. As per the reports, 61% of online customers like to make purchases on sites that offer AR technology.By Providing Modification and CustomizationMost of the customers like to explore various preferences and choices before buying a product. With AR, clients would now be able to experience this virtually from the solace of their homes. Customizations including designs, colors, patterns and much more can be explored with augmented reality.By Increasing the Shopping TimeToday, most online stores offer instructional videos on a few products so as to enable the purchaser to understand the product. With Augmented reality not only they could interact with the product but they can also explore its functionalities. Generally, an average online customer spends about 2 minutes online before making a purchase, however, with the AR that time can be increased. Moreover, it has been showing that the more time a client spends in a store, the more certain they are to make a purchase.Amazon Is Already On-BoardAmazon, an eCommerce and cloud computing organization has added 'AR View' function in its app for ease of shopping. As of now, Amazon offers AR view function for the selected number of products. But, the company has plans to continue including more products in this list so that customers can test the look and feel of it.The products can be rotated to know how extensive it is and what it looks like when placed in the real area. AR view feature of Amazon application has improved shopping experience by giving customers a satisfied feeling and winning their confidence while placing the order.Incorporating AR in eCommerce Websites Made SimplerForecasting the rising need of AR-based mobile applications in the coming future, big giants like Google and Apple have built up their very own Augmented Reality platform. These platforms make it easy for developers to develop eCommerce applications having AR features.ARKit: Augmented Reality for iOS iOS app developers can breathe a sigh of relief with the accessibility of ARKit – a platform to develop AR iOS applications. This kit decreases the time and efforts of the developer in building apps that flawlessly mix digital objects and information with the real-world environment.ARCore: Augmented Reality for Android ARCore is a software development kit for Android developers to create amazing applications with Augmented Reality capacities. This kit contains every one of the tools that developers require to make greater and better bets with AR that superbly blend the virtual and real world.The Future of AR in eCommerceAugmented Reality is the future of omnichannel experience. This technology uses the perk of online shopping, for example, convenience, and beats the challenges of 'vulnerability' to deliver impactful outcomes for eCommerce business. AR enables customers to interact with the product in real time and deliver touch and feel experience like that of in-store shopping.By taking out the guesswork from online shopping, AR technology has lessened company losses resulting from return request of clients. These pain points are addressed with innovations in the field of eCommerce. AR enhanced e-Commerce customers, making their life easy and hassle-free. AR is genuinely a market differentiator and many eCommerce brands are betting big on it.
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How AR can Help Improve Customer Engagement in eCommerce?

How AR can Help Improve Customer Engagement in eCommerce?

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The first age of the internet was about information and the next one will be about experiences. The biggest companies of the first generation are built around access to information. Google's main ...
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Why the invention of machine learning is considered to be the end of the human era?

Modern machine learning powers a great many applications such as social network filtering, self-driven vehicles, finding terrorist suspects, medical diagnosis, computer vision, and playing video and board games. Generally, it's regarded as a type of Artificial-Intelligence or Al. In other words, it is a subset of AI where computer programs are used to learn new things independently using the available data and information. The main idea behind this type of AI relies on mathematics, statistics, computer science and the enormous scientific work such as the development of the Least Square method (1808), Bayes Theorem (1812), and Markov Chains (1913) that was contributed by the great scientists who lived many of years ago.Machines that operate using this kind of technology are autonomous and require no human intelligence. In 2015, more than three thousand researchers from the Al and Robotics department led by Elon Musk, Stephen Hawking, and Steve Wozniak signed a letter warning about the risks of using autonomous weapons which work with freedom and independently without human involvement.This account focuses on the invention of artificial super-intelligent humans with the help of machine learning, and the dangers it poses on the existence of human beings, creating a negative impact on the human eraHistory of Machine Learning and AIOriginally, in the 1940s, when the ENIAC, the world's first electronic general-purpose computer system was being invented, the term the computer' was used to refer to a person with intensive capabilities to do numerical computations. The ENIAC, therefore, was identified as a numerical computing device and the media, in 1946 when the machine was being announced referred to it as a °Giant Brain'. No one knew exactly whether the device was a learning machine. But from the very beginning, the main idea was to create a powerful machine that could surpass learning and human thinking. Around the 1950s, the first gaming program that was believed to win against the checkers' world champion emerged. The program was used in a great deal by checkers-players to better their skills. Later, with great support from the Office of Naval Research, a United States international Navy and Marine research department, Frank Rosenblatt invented an algorithm referred to as the Perceptron that became a powerful monster when it was combined in a network with large numbers. Several years later, we saw the Department of the neural networks becoming stagnant due to limited abilities to find answers to some problems.In the 1990s, machine learning became popular although not as much as today. The combination of statistics and computer science led to the development of Artificial Intelligence which further shifted toward the so-called data-driven applications. Scientists used plenty amounts of data available to create intelligent systems that could inspect, analyze, and learn using large-scale data Today, the concept of artificial intelligence is viewed as the last invention and end of the human era.Machine Learning and Al Training ProgramsIt requires great knowledge of science, mathematics, and computational statistics to become smart in working with models and algorithms used in machine learning. Knowledge of computer hardware and software skills used in machine learning such as Python, C/Coo, R, Java, Scala, Julia, and JavaScript is needed to fully develop an Al application. The following are the current programs used to train individuals who are interested in building Artificial Intelligence applications.Artificial Intelligence Masters Programs MS degree programs in AI provide postgraduate students with a great deal of knowledge of the design, theory, development, and application of socially, linguistically, and biologically motivated computer systems. Research-oriented programs such as MPhil and MRes are also available although not in every institution. This degree, depending on the program, may include a specialization in topics such as cognitive robotics, machine learning, multi-agent systems, and autonomous system design. To pursue this career, learners should have an undergraduate degree in a related field such as Robotics and Computer Science.MS In Computer Science Online This degree is designed to train busy professionals anywhere in the world to study at their own pace. The program is non-thesis and provides a thorough preparation of the techniques and concepts related to programming, design and computer systems applications. Learners are equipped with in-depth knowledge and understanding of the essentials and current matters in computer science and engineering. It includes about thirty courses depending on the institution on topics such as robotics, AI, machine learning, and computational perception. The program requires a student to choose one of the various areas of study such as Software analysis and design, Bioinformatics, Information Technology, Computer programming, and etcetera.  M Tech Distance LearningAfter completing a B-Tech degree, you can advance your career by applying for an M-Tech degree program which takes exactly two years. Through distance learning, you can attend lectures at your own time. Additionally, you can access various programs through video chat, voice chat, and conferences which are considered a part of the course. It equips learners with a lot of research in the respective fields (Machine Learning, Computer Science and Engineering, and Mechanical and Electronic Engineering) and also an in-depth knowledge and training required to work in various research and development centers, power and mining generation companies, and consultation firms.India has the most popular universities such as Amity, Annamalai, and Indira-Gandhi Open Universities known to provide an M Tech Distance Learning degree. To pursue this career, you must have obtained an undergraduate Tech degree with the minimum qualifications required by the university council. Additionally, you must have a Certificate of Migration to be eligible for the distance learning degree in India.Machine Learning: Why it's The End of Human Era Today, the development of AI has crossed borders due to the invention of many systems that nearly imitate the behavior of an intelligent human being. Machines, things that are known to possess no life, having capacity perform assignments that require human judgment such as decision-making, speech recognition, translation between languages, visual perception, and etcetera is posing a great threat to our existence. In his book, Our Final Invention: Artificial-Intelligence and the End of the Human Era, author James Barrat cautions strongly the risks that artificial super-intelligent human beings pose to the existence of people. He further stresses how difficult it could be to predict or control a thing that is more intelligent than its own creator. Moreover, all the incredible threats and difficulties human face today including climate change, nanotechnology, nuclear weapons, biotechnology, and a lot more, are brought about by one thing: Artificial Intelligence. That's why Barratt states, in a plain and very clear language to completely go against Al. Our intelligence could be the only attribute that separates us from the aliens and other animal species out there. Humans were not the strongest animals in the world, but with their smart brains, they were able to emerge on top of all the beasts in the jungle. Sadly, our existence is put in jeopardy by things that we have created with our own hands.Scientists could be creating an Al that has the greatest intelligence than any other creature on earth that could become very powerful to do all our daily tasks or even fail us and kill us all depending on how they are programmed. Although the current era is more focused on the development of machine learning and artificial super-intelligent human beings, professionals think that the world is heading in the wrong direction. This is due to the fact that computer automated machines, no matter how intelligent they are, will never "think or understand" as the normal human brain. Additionally, comparing computer networks and the way they analyze things with their powerful algorithms to the conspiracies of the human brain is very similar to comparing oranges and apples.Conclusion To sum up, based on research and several reports published on various journals, it's very clear that the invention and development of machine learning in robotics and Al applications can become a great threat in the way of life of humans. This’s considered very possible when a machine is programmed to perform important duties, but instead develops a devastating technique for attaining their own goal. Moreover, when robots and other machines are programmed to perform destructive duties such as killing, getting into the hands of criminals and other dishonest people could lead to enormous casualties all over the world. This is the reason why the invention of machine learning becoming threats to humans in the future. 
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Why the invention of machine learning is considered to be the end of the human era?

Why the invention of machine learning is considered to be the end of the human era?

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Modern machine learning powers a great many applications such as social network filtering, self-driven vehicles, finding terrorist suspects, medical diagnosis, computer vision, and playing video and b...
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Amalgamation of BI and deep learning helps companies grow beyond imagination?

Machine Learning is indeed playing a vital role in business intelligence. There is no doubt about the fact that the companies all around the globe, have now realized the power of data. A large amount of data is indeed a significant part of every business strategy. When it comes to investments in the field of machine learning, deep learning, big data or artificial intelligence, they have indirectly or indirectly boosted business intelligence as well. And, as a result, now companies are enjoying a greater Return on Investments.What is Business Intelligence, and why is it important for a company? Business Intelligence is nothing but the effective utilization of data to allow the companies to efficiently deal with the ever-evolving needs of almost every industry. Though BI is not a new thing, and it has been in existence since years, but, if we talk about the importance and need of BI, it is intense now. Business intelligence definitely helped a plenty of companies to reach pinnacle point. The use of BI isn’t any more perplexing as it has proven its importance in the market.BI with the definitive foundation to amass, integrate, review and present business information and amplify the quality of the data-focused decision making, has come out to be one of the most useful strategies for businesses. Therefore, the road to the transformation of the data into actionable intelligence has smoothened and streamlined by the use of BI.Why is BI even important?Business Intelligence is the collection, analysis, presentation, and use of information in such a way that helps the company do better. Say, for example, if a firm which deals with jackets checked their Business Intelligence tools, the company would be easily able to segregate their sales. They would know how many woolen jackets were sold in say September and how many can they expect to sell in the month of June. The sales parameters (declining or growing sales) will help them to decide their marketing plans better. They would know when to offer discounts, which is the perfect time to get rid of the old stock quickly etc.Deep learning in businesses Deep learning an element of machine learning algorithms which makes use of the deep neural network. It uses the neural network to learn more. When it comes to a deep neural network, it is nothing more than an assemblage of artificial neurons. All of them are the trainable mathematical units. The basic function of them is to together learn even the most intricate of all the mathematical functions. They do in order to map raw input to the desired output. Neural networks are not new. In fact, they have been in existence since years.Deep learning plays a tremendous role in BI. Whenever we talk about anything that includes “Predictive” it does include deep learning or machine learning in one way or the other. When it comes to activities like forecasting future orders or looking for a deceitful insurance claim machine learning or deep learning comes to play.  Deep learning is used by various businesses to automate processes and boost efficiencies. At the same time, deep learning is also helping firms to reduce cost and provide a better superior experience to their customers. However, the most eminent use of deep learning is by the business intelligence systems.Companies now a days are making use of deep learning and AI to forecast customer behavior as well. This helps the business managers to predict the possible risks, and to make better marketing and sales plans. All this is a part of business intelligence, as only these kind of intelligent insights can help the firms to perform better.Use case: Deep learning is effectively used in the field of customer support. When it comes to satisfaction prediction, they make use of deep learning algorithm to process all the outcomes related to the historical satisfaction surveys. Also, the most efficient part of deep is that it keeps ‘learning’ from signals. These signals include things like, total time taken to answer a query, time taken to solve a problem, customer satisfaction ratings etc.The Combination of BI and deep learning for boosting business We already know that more than 76% of businesses are using machine learning to boost their sales. Deep learning, the evolved part of machine learning is used widely by the companies as well. Deep learning is a lot more than just stats kept aside, gathering the data or running intricate algorithms. Though stats are important for a successful business intelligence strategy, the most important part is to process them well. It is basically the art of learning and offering better outcomes.Deep learning in financial sectors Deep learning as we know is apt for solving even the most complicated image classification. Also, it has the ability to tackle even the toughest object recognition issues. Therefore, deep learning is widely used in facial comparison as well as identity verification technologies a lot. For instance, it is widely used in the financial services institutes. The financial sectors have to verify the identities of the customers. That’s one of the most important part of their activity. Here, automatic verification is going to saves a lot of time of both the banks as well as of the customers. Also, banks even save a lot of money by adopting the new technology.Conclusion Business Intelligence is growing rapidly and so is deep learning. Hence, the blend of both is surely going to help companies in numerous ways. Hence, developers are making topnotch BI tools which are powered by the deep learning methodologies.
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Amalgamation of BI and deep learning helps companies grow beyond imagination?

Amalgamation of BI and deep learning helps companies grow beyond imagination?

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Machine Learning is indeed playing a vital role in business intelligence. There is no doubt about the fact that the companies all around the globe, have now realized the power of data. A large amount ...
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