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Hadoop Course Online

Hadoop is an open source framework that uses Java programming. Hadoop is primarily used to process and store the large set of data in a distributing computing environment.

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Hadoop Course Online

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  1. Hadoop Course Online BESTONLINECOURSESCOUPON.COM

  2. The Contents About Hadoop 01 Types of data comes under big data 02 Benefits of big data 03 Solution to process big data 04

  3. The Contents Hadoop architecture 05 MapReduce 06 Hadoop Distributed File System 07 Working with Hadoop  08

  4. The Contents Advantages of Hadoop 09 Hadoop Environment setup 10 Overview of HDFS 11 Features of HDFS 12

  5. The Contents Architecture of HDFS 13 Operations of HDFS 14 Hadoop MapReduce 15 Big Data And Hadoop For Beginners 16

  6. About Hadoop Hadoop is an open source software framework which allows the user to store and process a large amount of data.  Hadoop consists of computer clusters which are built from commodity software.  Hadoop framework was designed by Apache software foundation, and it was originally released on 25th Dec 2011. The storage section is often referred as Hadoop Distributed File System (HDFS), and the processing part is performed by using MapReduce programming model. 

  7. Types of data comes under big data The big data has data generated by various applications and devices. Different types of data which come under the category of big data black box data,  social media data,  power grid data,   search engine data,  transport data,  stock exchange data.

  8. Benefits of big data In hospitals, big data plays a vital role, and the data analytics are storing the patient’s medical history using big data technologies This will help the doctors to provide quick service to the patients. The main challenges of big data are capturing data, storage, curation, searching, transfer, sharing, analysis and presentation.

  9. Solution to process big data If we have the small amount of data, we can store and process the data using the traditional approach. In this approach, the data is typically stored in RDBMS like MS SQL Server, Oracle database, etc. While dealing with huge amount of data, it is not possible for storing and processing the data using the traditional database server.

  10. Hadoop architecture Hadoop framework has four modules such as Hadoop YARN, Hadoop common, Hadoop MapReduce, and Hadoop Distributed File System (HDFS).

  11. MapReduce Hadoop MapReduce is a software framework which is used to write applications for processing the vast amount of data with the help of thousands of nodes.

  12. Hadoop Distributed File System HDFS provides the distributed file system that is used to run massive clusters of small computer machines in fault tolerant and reliable manner. This distributed system is based on the Google file system (GFS).

  13. Working with Hadoop  The application or user submits the job to the Hadoop client. Then the Hadoop client sends the job and its configuration to the job tracker which is responsible for splitting and distributing the configuration to the slaves. Job tracker is also responsible for scheduling the works and monitored them only then it can provide the status to the job client. After completing this process, the task trackers on different nodes perform the job using MapReduce algorithm, and finally, the output files are stored in the file system.

  14. Advantages of Hadoop Since Hadoop is an open source framework, so it is compatible with all the platforms. We can add or remove the servers dynamically. This process doesn’t interrupt the Hadoop in any way. The users can write and test the distributed systems quickly using Hadoop framework.

  15. Hadoop Environment setup Linux operating system supports the Hadoop framework. The Linux users can easily setup the Hadoop environment on their computers. Before starting to install the Hadoop, the users have to setup the Linux using Secure Shell (SSH). If the users have the OS other than Linux, then they need to install the software called Virtualbox that have the Linux OS inside it.

  16. Overview of  HDFS HDFS stores huge amount of data and provides easier access. This distributed file system is fault tolerant, and it is designed with low-cost hardwares.

  17. Features of HDFS It provides file authentication and permissions. Interaction with HDFS is possible using common interface system which is provided by Hadoop. HDFS is perfectly suitable for distributed storage and processing purposes.

  18. Architecture of HDFS Hadoop distributed file system follows the master-slave architecture which has the following components such as namenode, datanode, and block. Intention of HDFS Process the large datasets efficiently Fault detection and recovery Hardware at data

  19. Operations of HDFS Firstly the users have to format the configured HDFS file system and start the distributed file system. Then listing files in HDFS which means loading information into the server. After that, the users have to insert data into Hadoop Distributed File System. Retrieve the data from HDFS. Finally shut down the HDFS.

  20. Operations of HDFS Firstly the users have to format the configured HDFS file system and start the distributed file system. Then listing files in HDFS which means loading information into the server. After that, the users have to insert data into Hadoop Distributed File System. Retrieve the data from HDFS. Finally shut down the HDFS.

  21. Hadoop MapReduce MapReduce is used to process the enormous amount of data, and it is otherwise known as processing technique. This algorithm performs two tasks to process the data completely. The works include the map and reduce. Here map is used to convert a set of data into another set of data.

  22. Hadoop MapReduce The individual elements are splitting into tuples. Then the output of the map is taken as the input by the reduce task which combines the data tuples into the smaller set of tuples. MapReduce program executes the process in three stages comprises of map stage, shuffle stage and reduce stage.

  23. Big Data And Hadoop For Beginners Beginner, Students, Manager, and Developer, can take this course if you are interested in learning Big Data. This 3 hours hadoop course online has six sections with 1 article and six supplemental resources. The prime motto of this course is making you understand the Hadoop components and its complex architectures.

  24. Hadoop Course Online 2017 Linkable link Hadoop Tutorial Learn Big Data Learn Hadoop And MapReduce For Big Data Problems Big Data And Hadoop For Beginners Hadoop Course Online

  25. Follow us BESTONLINECOURSESCOUPON @BEST_COURSESS BESTCOURSES BESTONLINECOURSESCOUPON.COM

  26. THANKS FOR YOUR TIME! BESTONLINECOURSESCOUPON.COM

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