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Hadoop Tutorial

Apache Hadoop is a Big Data ecosystem consisting of open source components that essentially change the way large datasets are analyzed, stored, transferred and processed. Contrasting to traditional distributed processing systems, Hadoop facilitates multiple kinds of analytic workloads on same datasets at the same time.

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Following qualities make Hadoop stand out of the crowd:

  • Single namespace by HDFS makes content visible across all the nodes
  • Easily administered using High Performance Computing (HPC)
  • Querying and managing distributed data are done using Hive
  • Pig facilitates analyzing the large and complex datasets on Hadoop
  • HDFS is designed specially to give high throughput instead of low latency.

Comparison of Hadoop 1 and Hadoop 2 architectures

While Hadoop is the foundation for most of the big data structures, its different versions came up with varied improvisations. It is always better to have a good grasp about the functionalities offered by the successor versions of any technology. Let’s find out the same for Hadoop 1 and Hadoop 2:

Hadoop 1 Hadoop 2
Components are- HDFS (V1), MapReduce (V1) Components are- HDFS (V2), YARN (MR V2), MapReduce (V2)
Only one namespace Multiple namespaces
Only one programming model Multiple programming models
Has fixed-sized slots Has variable sizes of containers
Supports maximum of 4,000 nodes per cluster Supports maximum of 10,000 nodes per cluster

The most widely and frequently used framework to manage massive data across a number of computing platforms and servers in every industry, Hadoop is rocketing ahead in enterprises. It lets organizations store files that are bigger than what you can store on a specific node or server. More importantly, Hadoop is not just a storage platform, it is one of the most optimized and efficient computational frameworks for big data analytics. The right Hadoop training helps you understand the real world scenarios of working with Big Data.

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This Hadoop tutorial is an excellent guide for students and professionals to gain expertise in Hadoop technology and its related components. With the aim of serving larger audiences worldwide, the tutorial is designed for Hadoop Developers, Administrators, Analysts and Testers on this most commonly applied Big Data framework. Right from Installation to application benefits to future scope, the tutorial provides explanatory aspects of how learners can make the most efficient use of Hadoop and its ecosystem. It also gives insights into many of Hadoop libraries and packages that are not known to many Big data Analysts and Architects.

Together with, several significant and advanced big data platforms like MapReduce, YARN, HBase, Impala, ETL Connectivity, Multi-Node Cluster setup, advanced Oozie, advanced Flume, advanced Hue and Zookeeper are also explained extensively via real-time examples and scenarios, in this learning package.

For many such outstanding technological-serving benefits, Hadoop adoption is expediting. Since the number of business organizations embracing Hadoop technology to contest on data analytics, increase customer traffic and improve overall business operations is growing at a rapid rate, the respective number of jobs and demand for expert Hadoop Professionals is increasing at an ever-faster pace. More and more number of individuals are looking forward to mastering their Hadoop skills through Hadoop online training that could prepare them for various Cloudera Hadoop Certifications like CCAH and CCDH. Get to know more about Your Career in Big Data and Hadoop that can help you grow in your career.

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After finishing this tutorial, you can see yourself moderately proficient in Hadoop ecosystem and related mechanisms. You could then better know about the concepts so much so that you can confidently explain them to peer groups and will give quality answers to many of Hadoop questions asked by seniors or experts.

If you find this tutorial helpful, we would suggest you browse through our Big Data Hadoop training.

Recommended Audience 

  • Intellipaat’s Hadoop tutorial is designed for Programming Developers and System Administrators
  • Project Managers eager to learn new techniques of maintaining large datasets
  • Experienced working professionals aiming to become Big Data Analysts
  • Mainframe Professionals, Architects & Testing Professionals
  • Entry-level programmers and working professionals in Java, Python, C++, eager to learn the latest Big Data technology.


  • Before starting with this Hadoop tutorial, it is advised to have prior programming language experience in Java and Linux Operating system.
  • Basic command knowledge of UNIX and SQL Scripting can be beneficial to better understand the Big data concepts in Hadoop applications.

Hadoop Tutorial Video

"11 Responses on Hadoop Tutorial"

  1. Akshita says:

    Really Good tutorial for the Beginners

  2. Akash says:

    In hadoop where does the data get stored ?

    • Moderator says:

      Hadoop is a highly scalable analytics platform for processing large volumes of structured and unstructured data. By large scale, we mean multiple petabytes of data spread across hundreds or thousands of physical storage servers or nodes.

  3. Deva says:

    Thanks for sharing the proper explanation with this tutorial. It’s useful to learn basic fundamentals of Hadoop.

  4. Anuradha says:

    With this Hadoop tutorial, I got to know the enough knowledge on Hadoop. Thanks a lot.

  5. Urmila says:

    Great tutorial. This is covering Hadoop & its Ecosystem, Map Reduce,HDFS,Yarn,Pig,Hive with the examples. Thanks a lot.

  6. Nisha says:

    Very nice informative Hadoop tutorial.Thanks for sharing such a great content to my vision.

  7. saki says:

    this very helpful to know about basic hadoop concepts. and i found its really helpful to my institute students. keep sharing more.

  8. Khusboo says:

    Excellent Stuff!! Keep it up. I am a hadoop developer. I want to enhance my Hadoop skills therefore I am looking to work on some real – time projects. Willl you please suggest me good platform to work on real-time projects

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