Top 25 Python Libraries for Machine Learning
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Docker Vs Virtual Machine: Understand the difference
By Susan MayVirtual machines and Docker containers, both are more than enough in order to get the most out of computer resources available in hardware and software. Docker containers are kind of new on the block, but virtual machines or VMs have been there and will continue to remain popular in data centres of all sizes. If you are looking for the best solution to run your services in the cloud, it is advised that you understand these virtualization technologies first. Learn about the differences between the two, the best way they can be used, and the capabilities each one possesses.Most of the organizations have either moved or are planning to move from on-premise computing services to cloud computing services. Cloud computing allows you access to a large pool of configurable resources that can be shared, for example - computer networks, servers, storage, applications, and services. For the implementation of cloud computing in a traditional way, virtual machines are used. However, these days Docker containers have gained a lot of popularity due to its features, as well as Dockers are considered to be of a lightweight compared to virtual machines which are heavier.According to reports, there will be a rise in the use of application containers of 40% by the end of the year 2020. Docker containers have gained a lot of popularity as it facilitates rapid and agile development. But the question arises - How are Docker containers different from virtual machines? The most important thing to know is that Docker containers are not virtual machines or lightweight virtual machines or trimmed down virtual machines. Let us compare the two and understand the major differences.What is exactly a Virtual Machine?It is said that Virtual machines were born when server processing power and capacity was increased but bare metal applications were unable to exploit the new abundance in resources. Virtual machines were built by running software on top of physical servers in order to match the requirements of a particular hardware system. A virtual machine monitor or the hypervisor is a firmware, software or hardware which helps in creating a virtual machine and runs it. It is a necessary component to virtualize the server and it sits between the virtual machine and the hardware. As cloud computing services are available and virtualization is affordable, a lot of large as well as small IT departments have adapted virtual machines in order to reduce costs and increase efficiency.Understanding Virtual MachinesLet us understand how virtual machines work starting from the bottom-most layer:Infrastructure: This can be anything, your PC or laptop, a dedicated server running in a data centre, a private virtual server used in the cloud such as Amazon EC2 instance.Host Operating System: Just on top of the infrastructure layer lies the host which runs an operating system. While you use your laptop, it will likely be Windows, MacOS or Linux. As we are discussing virtual machines, it is commonly labelled as the host operating system.Hypervisor: It is also called a virtual machine monitor. You can consider a virtual machine as a self-contained computer packed into a single file, but something is required to be able to run the file. Type 1 hypervisors and Type 2 hypervisors are used to do so. In Type 1 hypervisor, Hyper-V for Windows, HyperKit for MacOS and KVM for Linux. Some popular Type 2 hypervisors are VirtualBox and VMWare.Guest Operating System: Suppose you would like to run three applications on your server under total isolation. To run, you will need 3 guest operating systems. These guest operating systems are controlled by the hypervisors. Each guest operating system takes a disk space of around 700 MB, so the total of disk space that you use is 2.1GB utilized by guest OS and it gets more complicated when guest OS uses its own CPU and memory resources as well. This is what makes the virtual machine heavy.BINS/LIBS: Each guest operating system uses its own set of various binaries and libraries in order to run several applications. For example, if you are using Python or Node JS you will have to install packages accordingly from this layer. Since each application will be different than the other, it is expected that each application will have its own set of library requirements.Application Layer: This is the layer where you have your source code for the magical application you have developed. If you want each of these applications to be isolated, you will have to run each application inside its own guest operating system.Types of Virtual MachinesThere are different types of virtual machines, each offering various functions:System Virtual MachinesA system virtual machine is a virtual machine which allows multiple instances of the operating system to run on a host system and share the physical resources. They emulate an existing architecture and are built with the purpose of providing a platform to run several programs where real hardware is not available for use. Some of the advantages of system virtual machines are -Multiple OS environments can accommodate the same primary hard drive with a virtual partition which allows sharing files generated in either the “guest” virtual environment or the “host” operating system.Application provisioning, high availability, maintenance and disaster recovery are inherent in the virtual machine software selected.Some of the disadvantages of system virtual machines are mentioned below:When a virtual machine accesses the host drive indirectly, it becomes less efficient than the actual machine.Malware protection for virtual machines are not very compatible with the "host" and sometimes require separate software.Process Virtual MachinesA process virtual machine is also known as an application virtual machine, or Managed Runtime Environment (MRE). It is used to execute a computer program inside a host OS and it supports a single process. A process virtual machine is created when the process starts and is destroyed as soon as you exit the process. The main purpose of this type of virtual machine is to provide a platform-independent programming environment.Benefits of Virtual MachinesVirtualization provides you with a number of advantages such as centralized network management, reducing dependency on additional hardware and software, etc. Apart from these, virtual machines offer a few more benefits:Multiple OS environments can be used simultaneously on the same machine, although isolated from each other.Virtual machines have the ability to offer an instruction set architecture which differs from real computersIt has easy maintenance, application provisioning, availability and convenient recovery.Popular VM ProvidersHere are some of the selected software we think is best suited for people who want to keep things real, virtually.Oracle VM VirtualboxOracle VM Virtualbox is free of cost, supports Windows, Mac and Linux, and it has the ability to host for 100,000 registered users. If you are not sure about which operating system you should choose to use, Oracle VM VirtualBox is a really good choice to go ahead with. It supports a wide range of host and client combinations. It supports operating systems from Windows XP onward, any Linus level above 2.4, Solaris, Open Solaris and even OpenBSD Unix. It also runs on Apple’s MacOS and can host a client Mac VM session.VMware Fusion and WorkstationVMware Workstation and VMware Fusion are the industry leaders in virtualization. It is one of the few hosts which support DirectX 10 and OpenGL 3.3. It also supports CAD and other GPU accelerated applications to work under virtualization.Red Hat VirtualizationRed Hat Virtualization has more of enterprise users with powerful bare-metal options. It has two versions: a basic version which is included in Enterprise Linux with four distinct VMs on a single host and the other one is a more sophisticated Red Hat virtualization edition.Important features of virtual machinesA typical virtual machine has the following hardware features.The hardware configuration of the virtual machine is similar to that of the default hardware configuration settings.There is one processor and one processor per core. The execution mode is selected for the virtualization engine based on the host CPU and the guest operating system.A single IDE CD/DVD drive is available which is configured after receiving power and detects automatically as a physical drive on the host system when connected.A virtual network adapter is used which gets configured upon power on and uses network address translation (NAT). With the help of NAT networking, virtual machines are able to share the IP address of the host system.It has one USB controller.It has a sound card configured to use the default sound card on the host system.It has one display configured to use the display settings on the host computer.Some of the software features include:The virtual machine is not encrypted.Drag-and-drop, cut and paste features are available.Remote access by VNC clients and shared folders are disabled.What are Containers?A container is a standard unit of software which packages up the code and all its dependencies in order to run the application reliably and quickly from one computing environment to another. A Docker container image is a standalone, lightweight, executable package of the software which includes everything needed to run an application such as system tools and libraries, code, runtime, and settings.Understanding Docker ContainerThere is a lot less baggage compared to virtual machines. Let us understand each layer starting from the bottom most.Infrastructure: Similar to virtual machines, the infrastructure used in Docker containers can be your laptop or a server in the cloud.Host Operating System: This can be anything which is capable of running Docker. You can run Docker on MacOS, Windows and Linux.Docker Daemon: It is the replacement for the hypervisor. Docker Daemon is a service which runs in the background of the host operating system. It also manages the execution and interaction with Docker containersBINS/LIBS: It is similar to that on virtual machines except it is not running on a guest operating system, instead special packages called Docker images are built and finally the Docker daemon runs the images.Application: This is the ultimate destination for the docker images. They are independently managed here. Each application gets packed with its library dependencies into the same Docker image and is still isolated.Types of ContainerLinux Containers (LXC) — LXC is the original Linux container technology. It is a Linux operating system level virtualization method which is used to run multiple isolated Linux systems on a single host.Docker — Docker was first started as a project in order to build single-application LXC containers. This makes the containers more flexible and portable to use. Docker acts a Linux utility at a higher level and can efficiently create, ship, and run containers.Benefits of ContainersIt reduces IT management resourcesIt reduces the size of snapshotsIt reduces and simplifies security updatesNeeds less code in order to migrate, transfer, and upload workloadsPopular Container ProvidersLinux ContainersLXCLXDCGManagerDockerWindows Server ContainersDocker vs Virtual Machines How is a Docker Container different from a Virtual Machine?Containers are user space of the operating system whereas Docker is a container based technology. Dockers are built for running various applications. In Docker, the containers running share the host Operating system kernel.Virtual machines are not based on container technology. They are mainly made up of kernel space along with user space of an operating system. The server's hardware is virtualized and each virtual machine has operating systems and apps which shares hardware resources from the host.Both virtual machines and dockers come with merits and demerits. Within a container environment, multiple workloads can run with one operating system. It also results in reduced IT management resources, reduces the size of snapshots, quicker spinning up apps, less code to transfer, simplified and reduced updates and so on. However, within a virtual machine environment, each workload needs a complete operating system.Basic Differences between Virtual Machines and ContainersVirtual MachinesContainersVMs are heavyweightContainers are lightweightIt has limited performanceIt has native performanceEach of the virtual machines runs in its own Operating SystemAll containers share the host operating systemIt has hardware-level virtualizationIt has OS virtualizationIt takes minutes to startupIt takes milliseconds to startupRequired memory is allocatedIt requires very less memory spaceAs it is fully isolated and hence it is more secureProcess-level isolation takes place in containers, thus less secure compared to VMsUses for VMs vs Uses for ContainersBoth containers and VMs have benefits and drawbacks, and the ultimate decision will depend on your specific needs, but there are some general rules of thumb.VMs are a better choice for running apps that require all of the operating system’s resources and functionality when you need to run multiple applications on servers or have a wide variety of operating systems to manage.Containers are a better choice when your biggest priority is maximizing the number of applications running on a minimal number of servers.Who wins amongst the two?When To Use a Container vs. When to Use a Virtual MachineContainers and virtual machines, each thrive in different use cases. Let us check some of the cases and know when to use a container and when is it a good choice to use virtual machines.Virtual machines take a good amount of time to boot and shut down: This feature is heavily used in development and testing environments. If you have to spin up and power down machines regularly or clone machines, Docker containers are what you should choose over virtual machines.Containers are geared based on Linux: Virtual machines are a better choice when you want to virtualize another operating system.Dockers do not have many automation and security features: Most of the fully fledged virtual management platforms provide a variety of automation features along with built-in security from kernel level to network switches.Virtual Machine and Container Use CasesThere is a fundamental difference between the usage of containers and virtual machines. Virtual machines are applicable for virtual environments, whereas containers use the underlying operations system and do not require a hypervisor.Let us see some use cases:Virtualized EnvironmentsIn a virtualized environment, multiple operating systems are run on a hypervisor which manages the I/O on one particular machine. However, in a containerized environment, it is not virtualized and hypervisor is not used. That does not mean you cannot run a container in a virtual machine.You can run containers in a virtual machine. We know containers run on a single Operating System. As it can run several containers on one physical system, it is like mini-virtualization without a hypervisor. Hypervisors face certain limitations related to performance and it also blocks certain server components like networking controller.DevOpsContainers are used in the DevOps environment for their develop-test-build. These containers perform much faster than virtual machines, they have faster spun up and down and have better access to system resources.Containers are smaller in size and have the ability to run several servers and hundreds of virtual machines. This shows that containers have greater modularity over virtual machines. Using microservices, an app can be split into multiple containers. Due to this combination, you can avoid potential crashes and this will also help you isolate problems.Older SystemsVirtual machines are capable of hosting an older version of an operating system. Suppose an application was built for an operating system many years back, which is quite unlikely to run in a newer generation operating system. In such cases, you can run the old operating system in a virtual machine and without any changes in the app you can run it.More Secure EnvironmentsAs container needs frequent interaction with the underlying operating system or other containers, there is a security risk associated. However, in comparison to containers, virtual machines are ideal and considered to be a more secure environment.
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Python in a Nutshell: Everything That You Need to Know
By Susan MayPython is one of the best known high-level programming languages in the world, like Java. It’s steadily gaining traction among programmers because it’s easy to integrate with other technologies and offers more stability and higher coding productivity, especially when it comes to mass projects with volatile requirements. If you’re considering learning an object-oriented programming language, consider starting with Python.A Brief Background On Python It was first created in 1991 by Guido Van Rossum, who eventually wants Python to be as understandable and clear as English. It’s open source, so anyone can contribute to, and learn from it. Aside from supporting object-oriented programming and imperative and functional programming, it also made a strong case for readable code. Python is hence, a multi-paradigm high-level programming language that is also structure supportive and offers meta-programming and logic-programming as well as ‘magic methods’.More Features Of PythonReadability is a key factor in Python, limiting code blocks by using white space instead, for a clearer, less crowded appearancePython uses white space to communicate the beginning and end of blocks of code, as well as ‘duck typing’ or strong typingPrograms are small and run quickerPython requires less code to create a program but is slow in executionRelative to Java, it’s easier to read and understand. It’s also more user-friendly and has a more intuitive coding styleIt compiles native bytecodeWhat It’s Used For, And By WhomUnsurprisingly, Python is now one of the top five most popular programming languages in the world. It’s helping professionals solve an array of technical, as well as business problems. For example, every day in the USA, over 36,000 weather forecasts are issued in more than 800 regions and cities. These forecasts are put in a database, compared to actual conditions encountered location-wise, and the results are then tabulated to improve the forecast models, the next time around. The programming language allowing them to collect, analyze, and report this data? Python!40% of data scientists in a survey taken by industry analyst O’Reilly in 2013, reported using Python in their day-to-day workCompanies like Google, NASA, and CERN use Python for a gamut of programming purposes, including data scienceIt’s also used by Wikipedia, Google, and Yahoo!, among many othersYouTube, Instagram, Quora, and Dropbox are among the many apps we use every day, that use PythonPython has been used by digital special effects house ILM, who has worked on the Star Wars and Marvel filmsIt’s often used as a ‘scripting language’ for web apps and can automate a specific progression of tasks, making it more efficient. That’s why it is used in the development of software applications, web pages, operating systems shells, and games. It’s also used in scientific and mathematical computing, as well as AI projects, 3D modelers and animation packages.Is Python For You? Programming students find it relatively easy to pick up Python. It has an ever-expanding list of applications and is one of the hottest languages in the ICT world. Its functions can be executed with simpler commands and much less text than most other programming languages. That could explain its popularity amongst developers and coding students.If you’re a professional or a student who wants to pursue a career in programming, web or app development, then you will definitely benefit from a Python training course. It would help if you have prior knowledge of basic programming concepts and object-oriented concepts. To help you understand how to approach Python better, let’s break up the learning process into three modules:Elementary PythonThis is where you’ll learn syntax, keywords, loops data types, classes, exception handling, and functions.Advanced PythonIn Advanced Python, you’ll learn multi-threading, database programming (MySQL/ MongoDB), synchronization techniques and socket programming.Professional PythonProfessional Python involves knowing concepts like image processing, data analytics and the requisite libraries and packages, all of which are highly sophisticated and valued technologies.With a firm resolve and determination, you can definitely get certified with Python course!Some Tips To Keep In Mind While Learning PythonFocus on grasping the fundamentals, such as object-oriented programming, variables, and control flow structuresLearn to unit test Python applications and try out its strong integration and text processing capabilitiesPractice using Python’s object-oriented design and extensive support libraries and community to deliver projects and packages. Assignments aren’t necessarily restricted to the four-function calendar and check balancing programs. By using the Python library, programming students can work on realistic applications as they learn the fundamentals of coding and code reuse.
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Top 5 Benefits of Data Science With Python Foundation Training
By Susan MayThere are vast amounts of data generated every second. From your smartphone to your online behavior, every action you take can be consolidated into big data. Now, this huge amount of data houses great potential. Businesses and corporations can use this data to understand user behavior, predict patterns, and be better prepared to deal with future challenges. To do this, they need data scientists.Data science is a relatively new term that has taken the world by storm. The profession has been named the “Sexiest Job of the 21st Century” by Harvard Business Review. This job offers many perks and is being touted as the most in-demand profession. Data scientists are all-rounders who need to know at least the basics of statistics, maths, and computer science.With time, Python has become a popular choice among data scientists and getting a data science with Python foundation training can do wonders for your career.Why Data Scientist’s are in high demand?Is becoming a data scientist all that it is made up to be? Is it really worth the effort? Why should I choose it as my career? These are valid questions that anyone can have. Here are a few reasons that justify the choice of data science as a career.Demand Across IndustriesSince data science is related to computer science and requires coding skills, many think that a data scientist is a tech industry job. This is not entirely true. Almost every industry has a need for data scientists, from tech to gaming to the financial sector to retail. You can pick the industry that you want to work in and become a data scientist in that industry.Shortage of Data ScientistsAs per a report by IBM, there will be a shortage of about 62,000 data scientists by the year 2020. This can also be attested by the fact that around 80% of people working in the field right now say that there is a severe shortage of trained data scientists right now.High SalarySince there is a wide disparity between the demand and supply of data scientists, the job currently fetches a high salary. This is the most in-demand profession right now and a well-trained data scientist with good qualifications can easily get an impressive package. The average salary of a data scientist is the US right now is around USD 91,000 per annum.Average Company wise Data Scientist SalaryExciting ChallengesWhen you work in a new field that is still in its infancy such as data science, the potential for learning on the job is enormous. You will be constantly discovering new things and finding new solutions to problems or facing new challenges. If you like a challenge and want to constantly reinvent yourself and change your thinking, then you should definitely consider becoming a data scientist.Immense GrowthThe field of data science has a projected growth of around 11% between 2014 and 2024. This means that the demand for data scientists with Python foundation training is set to increase. The field is also growing faster than any of its counterparts.Why is Python Foundation Training Important for a Data Scientist?The language used by a data scientist can have a great impact on the time taken to analyze the data and interpretation of the results. Python is one of the most popular languages used by data scientists. Its simplicity, scalability, flexibility, and power are the main reasons for this.Python is relatively easy to learn. Even a non-programmer can understand the basics and start coding. Python also has good community support. If you ever get stuck while learning and need to clear some doubts, just post your query online and your doubt will be cleared in no time. The Python community is also actively involved in building new packages that are helpful for data scientists. This has only made the language more attractive to data scientists and has increased its adoption in the field.Not only is Python a powerful language that does quite a lot with just a few lines of code, but it is also well-supported by powerful packages that make it easier to solve complex data science problems.Give Your Career a Boost!Data science with Python foundation course can help your career reach great heights. It is the best way to enter an exciting profession whose impact can be felt globally in our everyday life. It is also a good way to enter a high paying career where you also get to keep learning.
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There are vast amounts of data generated every second. From your smartphone to your online behavior, every action you take can be consolidated into big data. Now, this huge amount of data houses great...Continue reading
The Ultimate Guide to Node.Js
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Top 6 Benefits of Learning Data Science with Python
By Susan MayOver the last decade, a new requirement has emerged in the industry that has taken the world by storm and has completely revamped our thinking. This requirement is none other than that of Data Scientists. Data Scientist is one of the hottest requirements in the job market. One of the main reasons for this widespread popularity is that data analytics can find use in all industries. Its use is not limited to just the software or IT industry. It has found application in industries such as intelligence and security, healthcare, business, government, energy, and much more. This article will not only give you reasons on why you need to learn data science, but it will also tell you why learning data science with Python training is the better option.Why Learn Data Science?Data analytics is all about solving problems. It involves looking at the data you have and using it to solve a problem that you are either facing currently or you anticipate you will have to face in the future. One of the main advantages of studying data science is that you can work in the field you like. Every industry has its own unique set of present and future problems and data science is the way to solve them. This is why every industry is currently looking for data scientists and you can have your pick among them. You will not get this option with any other course.Data Science is not just the current trend, it is also the future. When you are planning your career, it is important to consider the present as well as future requirements. Currently, there is a shortage of data scientists. Companies are looking to hire more people in this post but they are unable to find qualified candidates. Studying data science or data analytics right now will put you on the path of some very lucrative career choices.Why learn Data Science with Python Training?While there are many different ways to implement data analytics, Python has become very popular and rightfully so. Python is a powerful language that is easy to learn and implement. Here is why you should learn data science with Python training.1. Ease of LearningPython is one of the easiest languages to learn. Even if you have no background with coding, learning Python will not be difficult. One of the main things that hold people back when they hear about becoming a data scientist is the lack of coding skills and the perceived difficulty in learning the same. You won’t face this problem with Python.2. Faster Development and ProcessingWhile dealing with huge amounts of data, speed is key. A slow language can slow things down incredibly. Python is a clean, easy to handle language that requires only a few lines of coding. This significantly cuts down on the coding time required. Python’s slow execution was one of the reasons that held it back from being fully accepted. However, since the introduction of the Anaconda platform, even this complaint has been dealt with.3. Powerful PackagesPython also comes with huge range packages such as NumPy, SciPy, PyBrain, Pandas, etc. that makes it incredibly simple to code complex data analytics problems. There are also many libraries that support the integration of Python with other languages such as C and SQL. These further aid Python in making it more powerful.4. Community SupportOne thing that makes Python is easy to learn and understand is its strong community. Any time you get stuck with any problem, you can ask the community and they will always help you. In addition to this, many in the community are also constantly developing new packages and libraries for a variety of uses. With the popularity of Python for data science increasing, many of these are being developed for the use of data scientists.5. Better Data VisualisationVisualization is key for data scientists as it helps them understand the data better. With libraries such as ggplot, Matplotlib, NetworkX, etc. and APIs such as Plotly, Python can help you create stunning visualizations. You can also integrate other big data visualization tools in Python. All of this adds to Python’s usefulness for a data scientist.6. Compatible with HadoopOne of the most popular open source platforms for big data, Hadoop is inherently compatible with Python. The Python package known as PyDoop lets you access the API for Hadoop. This lets you write Hadoop programs using Python. The package also lets you write code for complex problem solving with little effort.Kickstart Your CareerIf you are at the start of your professional journey and are thinking about which path to take, then you should definitely consider going for data science with Python course. This is one of the most sought after career options that can set you on the fast track for a very high paying and exciting profession.
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Over the last decade, a new requirement has emerged in the industry that has taken the world by storm and has completely revamped our thinking. This requirement is none other than that of Data Scienti...Continue reading
Top 8 Advantages Of Learning React JS
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MEAN Stack Web Development: A Beginner’s Guide
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Become A Web Developer With NodeJS: The Blueprint To A Successful Career
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Top 9 Benefits Of Learning Apache Spark and Scala
By Susan MayWhat Is Apache Spark and Scala All About?Big Data and Analytics are transforming the way businesses take informed market-oriented decisions, craft strategies for targeting customer segments that are optimally promising, and remain shielded from market quirks and economic volatilities. These abilities are impacted by mining information that is locked in large data volumes generated online or from other connected sources.Big Data can be reliably processed with the Apache Spark interface. Apart from facilitating seamless programming for data clusters, Spark also offers proper tolerance for faults and data parallelism. This implies that large datasets can be processed speedily by this open source platform. Apache Spark has an edge over Hadoop in terms of better and sophisticated capabilities on data handling, storing, evaluation and retrieving fronts. Spark framework comes integrated with modules for ML (Machine Learning), real-time data streaming, textual and batch data, graphics, etc., which makes it ideal for different industry verticals.Scala or Scalable Language is a general-purpose object-oriented language with which Spark is written for supporting cluster computing. Scala offers support with immutability, type interference, lazy evaluation, pattern matching, and other features. Features absent in Java such as operator overloading, named parameters, no checked exceptions, etc. are also offered by Scala.Why Should I Learn Apache Spark and Scala?Data science offers unparalleled scope if you want to scale new heights in your career. Also, as part of an organization, if you are strategizing on cornering your niche market, you need to get focused insights into how the market is changing. With Apache Spark and Scala training, you can become proficient in analyzing patterns and making conclusive fact-driven assumptions.There are many incentives for learning this framework-language combination as an aspirant or by exposing your organization’s chosen employees to this.1) Ideal for Implementing IoTIf your company is focusing on the Internet of Things, Spark can drive it through its capability of handling many analytics tasks concurrently. This is accomplished through well-developed libraries for ML, advanced algorithms for analyzing graphs, and in-memory processing of data at low latency.2) Helps in Optimizing Business Decision MakingLow latency data transmitted by IoT sensors can be analysed as continuous streams by Spark. Dashboards that capture and display data in real time can be created for exploring improvement avenues.3) Complex Workflows Can Be Created with EaseSpark has dedicated high-level libraries for analyzing graphs, creating queries in SQL, ML, and data streaming. As such, you can create complex big data analytical workflows with ease through minimal coding.4) Prototyping Solutions Becomes EasierAs a Data Scientist, you can utilize Scala’s ease of programming and Spark’s framework for creating prototype solutions that offer enlightening insights into the analytical model.5) Helps in De-Centralized Processing of DataIn the coming decade, Fog computing would gain steam and will complement IoT to facilitate de-centralized processing of data. By learning Spark, you can remain prepared for upcoming technologies where large volumes of distributed data will need to be analyzed. You can also devise elegant IoT driven applications to streamline business functions.6) Compatibility with HadoopSpark can function atop HDFS (Hadoop Distributed File System) and can complement Hadoop. Your organization need not spend additionally on setting up Spark infrastructure if Hadoop cluster is present. In a cost-effective manner, Spark can be deployed on Hadoop’s data and cluster.7) Versatile FrameworkSpark is compatible with multiple programming languages such as R, Java, Python, etc. This implies that Spark can be used for building Agile applications easily with minimal coding. The Spark and Scala online community is very vibrant with numerous programmers contributing to it. You can get all the required resources from the community for driving your plans.8) Faster Than HadoopIf your organization is looking to enhance data processing speeds for making faster decisions, Spark can definitely offer a leading edge. Data is processed in Spark in a cyclic manner and the execution engine shares data in-memory. Support for Directed Acyclic Graph (DAG) mechanism allows Spark engine to process simultaneous jobs with the same datasets. Data is processed by Spark engine 100x quicker compared to Hadoop MapReduce.9) Proficiency EnhancerIf you learn Spark and Scala, you can become proficient in leveraging the power of different data structures as Spark is capable of accessing Tachyon, Hive, HBase, Hadoop, Cassandra, and others. Spark can be deployed over YARN or another distributed framework as well as on a standalone server.Learn Apache Spark and Scala To Widen Your Performance HorizonCompleting an Apache Spark and Scala course from a renowned learning center would make you competent in leveraging Spark through practice sessions and real-life exercises. Once you become capable of using this cutting-edge analytics framework, securing lucrative career opportunities won’t be a challenge. Also, if you belong to an organization, gaining actual and actionable insights for decision making would be a breeze.
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