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Learning Large Scale Data Processing

Large scale data processing is one of the most important aspects of growing any business in a strategic manner. It is necessary to collect and analyze all the data collected from customers,social media, websites and other sources in order to identify the business’ shortcomings and its progress. The large volumes of data collected from different sources are called Big Data. A very powerful analytical tool is required to analyze all the data collected. Apache Hadoop and Apache Spark are some of the most popular analytical tools used in the industry. What is Apache Spark? It is a powerful opensource processing engine which was built on the fundamental principles of speed, ease of use and sophisticated analytics. It is almost 100 times faster than Apache Hadoop. Spark gives a unified framework which is comprehensive and manages big data requirements with the aid of many diverse data sets like text data, image data, graph data, as well as the source of data. It also lets you write applications in Java, Scala or Python. Apache Hadoop has Map and Reduce modules for data processing. In addition to these modules, Spark can support SQL queries, graph data processing, streaming data and machine learning. Developers can use these functions individually or use them in combination operations in a single data pipeline. A higher level API is also provided to improve developer productivity. Spark doesn’t write intermediate results on the disk, it stores the results in the memory which is a huge bonus when you have to work on the same data multiple times. It is an execution-engine that works both in-memory and on-disk. It will try to retain as much data in the memory before storing it to the disk. It is used by popular companies like Yahoo, Baidu and Tencent. What is Scala? Scala is a programming language and is an acronym for “Scalable language.” It is the precise integration of functional language and object-oriented concepts. This implies that Scala can be used to write one-line expressions or for large scale critical systems. The syntax to write the code is succinct. It also contains REPL and IDE worksheets for quick feedback. Scala is also the preferred language for many mission critical server systems. The quality of the code is on par with Java, but, since the typing is precise, most errors are detected during the compile time and not during run time. Why is Scala Considered the Best Choice for Apache Spark? Before I answer this question, the choice of language used in a Spark project depends on the requirements of the team, their skill set and ultimately, personal taste. An Apache Spark project can be written in Java, Scala, Python and R language (version 1.4). Amongst these, Scala is considered as the best choice because: More lines of code are required in Java than Scala to achieve the same goal. Unlike Scala, REPL(Read-Evaluate-Print Loop) interactive shell is not supported by Java. This is a deal breaker for many developers because, with REPL, developers can access their data set and prototype their application without going through the entire development cycle. Scala is faster than Python, so, the time required to execute a code is less and improves the performance of the application considerably. If you are proficient in Scala, you can check the source code if something doesn’t work the way you want, considering that Apache Spark is built on Scala. It is because of this most of the latest features are first accessible by Scala and then later ported to Python and Java.
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Learning Large Scale Data Processing 50 Learning Large Scale Data Processing Blog
Susan May 17 May 2016
Large scale data processing is one of the most important aspects of growing any business in a strategic manner. It is necessary to collect and analyze all the data collected from customers,social medi...
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Here’s How Learning Python Saves Time

According to Python.org, Python is an interpreted object-oriented, high-level programming language with dynamic semantics. To put it in simpler terms, Python is an object-oriented programming language which is clear and powerful and is an asset to every programmer. It can be compared to Java, Perl, Ruby or Scheme. It is popular among programmers all over the world as it is easy to use and takes only a few days to learn python course and start developing in Python. There are also many career options which require Python developers. Python’s Distinguished Features It can be embedded into an application to administer a programmable interface. Many operating systems can support Python - Windows, MacOS, Unix etc. It is an open source software, so you can download it at zero cost and redistribute or modify it. Python supports OOP by using classes and inheritance. It has an automatic memory management system which will allocate memory in your code on its own. Errors are caught sooner in Python as mixing incompatible data types will cause an exception. A variety of basic data types are available, numbers (unlimited-length long integers, complex, and floating point), lists, strings (both ASCII and Unicode), and dictionaries. Learning Python Can Save You Time When Compared to Other Languages Java It is quicker to code in Python than in Java due to a fundamental difference between the two, Static and Dynamic typing. Java uses static typing which means that once you define the type of the variable when you first declare it, you can not change it throughout the program. Python, on the other hand, uses dynamic typing and allows you to change the type of variable anytime during the program. This makes the programs written in Python 3 to 5 times shorter than its equivalent in Java. The next difference is the use of indentation (>>>) and curly braces ({ }) to define the beginning and end of each class definition or any function. Java,like most programming languages, makes use of curly braces whereas, Python uses indentation. The advantage of using indentation is that it makes your program easy read and reduces the probability of an error due to a missing brace. In Java,if you have 10 classes in your application, then you will have to define each class in its own file. In Python, multiple classes can be defined in a single file. Hence, all the 10 classes can be saved in a single file which saves you time. JavaScript JavaScript is suitable only for small programs. If you useJavaScript to code a complex program, you will have a hard time keeping track of the various functions and classes used in the program. Python can be used to code small as well as complex programs as it is easy to keep track of all functions and classes declared by the developer.  C++ The differences mentioned in Java also apply for C++. The only difference is that Python is 5 to 10 times faster than C++ as the Python code 5 to 10 times shorter. So, it would take a C++ programmer over a year to complete a code but, a Python programmer could complete the same code in less than 2 months.  Perl Perl uses keywords that are quite complex and at times, the keywords do not correspond to the task you have at hand. This may be a small issue while coding small programs, but, it is a hassle when coding complex programs. Basically, the code tends to be ambiguous unless you get a hang of it. Python, on the other hand, is intuitive and easy to learn for a novice.
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Here’s How Learning Python Saves Time

Here’s How Learning Python Saves Time

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According to Python.org, Python is an interpreted object-oriented, high-level programming language with dynamic semantics. To put it in simpler terms, Python is an object-oriented programming language...
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Big Data Analytics with Apache Hadoop

Are you confused by terms like Big Data, Apache or Hadoop and their functions and definitions? Fear not, we are going to discuss all this and much more in detail below. What is Big Data? Businesses collect large volumes of data on a daily basis. This data can be structured or unstructured. Structured data refers to the organized form of data, like spreadsheets or similar forms and unstructured data refers to data in the form of text files or multimedia files. The data can be collected from any source and all this data put together is known as Big Data. It is used by many industries like energy, telecom, retail, manufacturing, banking, insurance, public, media, healthcare etc. Importance of Big Data Big Data helps businesses analyze their daily activities which can lead to better strategic actions and decisions to boost the business. It can also help them optimize the launch of a new product and reduce the production time for some products. An added advantage is the root cause of some problems and defects related to a particular product can be determined if Big Data is coupled with a high-powered analytics tool such as Apache Hadoop. What is Apache Hadoop? Apache Hadoop is an open-source software framework for distributed storage and distributed processing of large volumes of data usually written in Java. It can provide quick and steady analysis of large volumes of data, both structured and unstructured.How does Apache Hadoop work? Apache Hadoop comprises of the following modules Hadoop MapReduce: A programming model for extensive data processing.  Hadoop Distributed File System (HDFS): As the name suggests, it is a distributed file system that stores data on main machines which provides high overall bandwidth across the clusters of machines.  Hadoop Common: It is the collection of libraries and utilities required by other Hadoop modules. Hadoop YARN: It is a redesigned resource manager which separates functionalities of resource management and job scheduling into different daemons. Apache Pig, Apache Hive, and Apache HBase are a few projects, apart from the aforementioned ones, which are included in the Apache Hadoop platform. All the modules in Hadoop are designed with the fundamental aspect in mind that hardware problems can occur with any system in the cluster. To combat this, if any system in cluster faces a hardware issue, the tasks assigned to that system are immediately transferred to other systems in the cluster so that the analysis is done within the stipulated time. Activities performed by Apache Hadoop on Big Data Storage: It is necessary to store large volumes of Big Data in order to analyze it. Process: The large volumes of data are processed by enriching, cleansing, transforming, calculating and running algorithms on it. Access: The data which is analyzed is segregated and stored in such a manner that it is easy to retrieve it and also search for it. Examples of How a Module Works Hadoop MapReduce: It consists of JobTracker and TaskTracker, once the client program is copied on each node. JobTracker figures out the number of splits from the input data and identifies a few TaskTrackers based on their proximity to data sources. JobTracker sends task requests to the selected TaskTrackers. Each TaskTracker starts the map phase processing by extracting the input data from the splits. TaskTracker notifies the JobTracker once the map task is complete. The TaskTracker is notified when the task is completed. When all the TaskTrackers are done, the JobTracker informs the appointed TaskTrackers for reduced phase. Each TaskTracker reads the region files and conducts the reduce function, which in turn collects the key value into the output file. After both phases are completed, the JobTracker provides access to the client program.
Rated 4.0/5 based on 20 customer reviews
Big Data Analytics with Apache Hadoop

Big Data Analytics with Apache Hadoop

Blog
Are you confused by terms like Big Data, Apache or Hadoop and their functions and definitions? Fear not, we are going to discuss all this and much more in detail below. What is Big Data? Busines...
Continue reading