Hadoop Tutorial for Beginners | Learn Hadoop from A to Z

1. Hadoop Tutorial

The Hadoop tutorial is a comprehensive guide on Big Data Hadoop that covers what is Hadoop, what is the need of Apache Hadoop, why Apache Hadoop is most popular, How Apache Hadoop works?

Apache Hadoop is an open source, Scalable, and Fault tolerant framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is not only a storage system but is a platform for large data storage as well as processing. This Big Data Hadoop tutorial provides a thorough Hadoop introduction.

We will also learn in this Hadoop tutorial about Hadoop architecture, Hadoop daemons, different flavors of Hadoop. At last, we will cover the introduction of Hadoop components like HDFS, MapReduce, Yarn, etc.

Hadoop Tutorial for Beginners

Hadoop Tutorial for Beginners

2. What is Hadoop Technology?

Hadoop is an open-source tool from the ASF – Apache Software Foundation. Open source project means it is freely available and we can even change its source code as per the requirements. If certain functionality does not fulfill your need then you can change it according to your need. Most of Hadoop code is written by Yahoo, IBM, Facebook, Cloudera.
It provides an efficient framework for running jobs on multiple nodes of clusters. Cluster means a group of systems connected via LAN. Apache Hadoop provides parallel processing of data as it works on multiple machines simultaneously. Lets see a video Hadoop Tutorial to understand what is Hadoop in a better way.
Learn: How Hadoop Works?

Big Data Hadoop Tutorial Video

Hope the above Big Data Hadoop Tutorial video helped you. Let us see further.

By getting inspiration from Google, which has written a paper about the technologies. It is using technologies like Map-Reduce programming model as well as its file system (GFS). As Hadoop was originally written for the Nutch search engine project. When Doug Cutting and his team were working on it, very soon Hadoop became a top-level project due to its huge popularity. Let us understand Hadoop definition and meaning.

Apache Hadoop is an open source framework written in Java. The basic Hadoop programming language is Java, but this does not mean you can code only in Java. You can code in C, C++, Perl, Python, ruby etc. You can code the Hadoop framework in any language but it will be more good to code in java as you will have lower level control of the code.

Big Data and Hadoop efficiently processes large volumes of data on a cluster of commodity hardware. Hadoop is for processing huge volume of data. Commodity hardware is the low-end hardware, they are cheap devices which are very economical. Hence, Hadoop is very economic.

Hadoop can be setup on a single machine (pseudo-distributed mode, but it shows its real power with a cluster of machines. We can scale it to thousand nodes on the fly ie, without any downtime. Therefore, we need not make any system down to add more systems in the cluster. Follow this guide to learn Hadoop installation on a multi-node cluster.

Hadoop consists of three key parts –

In this Hadoop tutorial for beginners we will all these three in detail, but first lets discuss the significance of Hadoop.

3. Why Hadoop?

Let us now understand in this Hadoop tutorial that why Big Data Hadoop is very popular, why Apache Hadoop capture more than 90% of big data market.

Apache Hadoop is not only a storage system but is a platform for data storage as well as processing. It is scalable (as we can add more nodes on the fly), Fault tolerant (Even if nodes go down, data processed by another node).
Following characteristics of Hadoop make it a unique platform:

  • Flexibility to store and mine any type of data whether it is structured, semi-structured or unstructured. It is not bounded by a single schema.
  • Excels at processing data of complex nature. Its scale-out architecture divides workloads across many nodes. Another added advantage is that its flexible file-system eliminates ETL bottlenecks.
  • Scales economically, as discussed it can deploy on commodity hardware. Apart from this its open-source nature guards against vendor lock.

Learn Hadoop features in detail.

4. What is Hadoop Architecture?

After understanding what is Apache Hadoop, let us now understand the Big Data Hadoop Architecture in detail in this Hadoop tutorial.

Hadoop Architecture

Hadoop Architecture

Hadoop works in master-slave fashion. There is a master node and there are n numbers of slave nodes where n can be 1000s. Master manages, maintains and monitors the slaves while slaves are the actual worker nodes. In Hadoop architecture, the Master should deploy on good configuration hardware, not just commodity hardware. As it is the centerpiece of Hadoop cluster.

Master stores the metadata (data about data) while slaves are the nodes which store the data. Distributedly data stores in the cluster. The client connects with master node to perform any task. Now in this Hadoop for beginners tutorial, we will discuss different components of Hadoop in detail.

5. Hadoop Components

There are three most important Apache Hadoop Components. In this Hadoop tutorial, you will learn what is HDFS, what is Hadoop MapReduce and what is Yarn Hadoop. Let us discuss them one by one-

5.1. What is HDFS?

Hadoop HDFS or Hadoop Distributed File System is a distributed file system which provides storage in Hadoop in a distributed fashion.

In Hadoop Architecture on the master node, a daemon called namenode run for HDFS. On all the slaves a daemon called datanode run for HDFS. Hence slaves are also called as datanode. Namenode stores meta-data and manages the datanodes. On the other hand, Datanodes stores the data and do the actual task.

HDFS Architecture

HDFS Architecture

HDFS is a highly fault tolerant, distributed, reliable and scalable file system for data storage. First Follow this guide to learn more about features of HDFS and then proceed further with the Hadoop tutorial.

HDFS is developed to handle huge volumes of data. The file size expected is in the range of GBs to TBs. A file is split up into blocks (default 128 MB) and stored distributedly across multiple machines. These blocks replicate as per the replication factor. After replication, it stored at different nodes. This handles the failure of a node in the cluster. So if there is a file of 640 MB, it breaks down into 5 blocks of 128 MB each (if we use the default value).

5.2. What is MapReduce?

In this Hadoop Basics Tutorial, now its time to understand one of the most important pillars of Hadoop, i.e. Hadoop MapReduce. The Hadoop MapReduce is a programming model. As it is designed for large volumes of data in parallel by dividing the work into a set of independent tasks. MapReduce is the heart of Hadoop, it moves computation close to the data. As a movement of a huge volume of data will be very costly. It allows massive scalability across hundreds or thousands of servers in a Hadoop cluster.

Hence, Hadoop MapReduce is a framework for distributed processing of huge volumes of data set over a cluster of nodes. As data stores in a distributed manner in HDFS. It provides the way to MapReduce to perform parallel processing.

5.3. What is YARN Hadoop?

YARN – Yet Another Resource Negotiator is the resource management layer of Hadoop. In the multi-node cluster, as it becomes very complex to manage/allocate/release the resources (CPU, memory, disk). Hadoop Yarn manages the resources quite efficiently. It allocates the same on request from any application.

On the master node, the ResourceManager daemon runs for the YARN then for all the slave nodes NodeManager daemon runs.

Learn the differences between two resource manager Yarn vs. Apache Mesos. Next topic in the Big Data Hadoop for beginners is a very important part of Hadoop i.e. Hadoop Daemons

6. Hadoop Daemons

Daemons are the processes that run in the background. There are mainly 4 daemons which run for Hadoop.

Hadoop Daemons

Hadoop Daemons

  • Namenode – It runs on master node for HDFS.
  • Datanode – It runs on slave nodes for HDFS.
  • ResourceManager – It runs on master node for Yarn.
  • NodeManager – It runs on slave node for Yarn.

These 4 demons run for Hadoop to be functional. Apart from this, there can be secondary NameNode, standby NameNode, Job HistoryServer, etc.

7. How does Hadoop works?

Till now in Hadoop training we have studied Hadoop Introduction and Hadoop architecture in detail. Now next let us summarize Apache Hadoop working step by step:
i) Input data breaks into blocks of size 128 Mb (by default) and then moves to different nodes.
ii) Once all the blocks of the file stored on datanodes then a user can process the data.
iii) Then, master schedules the program (submitted by the user) on individual nodes.
iv) Once all the nodes process the data then the output is written back to HDFS.

8. Hadoop Flavors

This section of Hadoop Tutorial talks about the various flavors of Hadoop.

  • Apache – Vanilla flavor, as the actual code is residing in Apache repositories.
  • Hortonworks – Popular distribution in the industry.
  • Cloudera – It is the most popular in the industry.
  • MapR – It has rewritten HDFS and its HDFS is faster as compared to others.
  • IBM – Proprietary distribution is known as Big Insights.

All the databases have provided native connectivity with Hadoop for fast data transfer. Because, to transfer data from Oracle to Hadoop, you need a connector.
All flavors are almost same and if you know one, you can easily work on other flavors as well.

9. Hadoop Ecosystem Components

In this section of Hadoop tutorial, we will cover Hadoop ecosystem components. Let us see what all the components form the Hadoop Eco-System:

Hadoop Tutorial - Hadoop Ecosystem Components

Hadoop Tutorial – Hadoop Ecosystem Components

  • Hadoop HDFS – Distributed storage layer for Hadoop.
  • Yarn Hadoop Resource management layer introduced in Hadoop 2.x.
  • Hadoop Map-Reduce – Parallel processing layer for Hadoop.
  • HBase – It is a column-oriented database that runs on top of HDFS. It is a NoSQL database which does not understand the structured query. For sparse data set, it suits well.
  • Hive – Apache Hive is a data warehousing infrastructure based on Hadoop and it enables easy data summarization, using SQL queries.
  • Pig – It is a top-level scripting language. As we use it with Hadoop. Pig enables writing complex data processing without Java programming.
  • Flume – It is a reliable system for efficiently collecting large amounts of log data from many different sources in real-time.
  • Sqoop – It is a tool design to transport huge volumes of data between Hadoop and RDBMS.
  • Oozie – It is a Java Web application uses to schedule Apache Hadoop jobs. It combines multiple jobs sequentially into one logical unit of work.
  • Zookeeper – A centralized service for maintaining configuration information, naming, providing distributed synchronization, and providing group services.
  • Mahout – A library of scalable machine-learning algorithms, implemented on top of Apache Hadoop and using the MapReduce paradigm.

Refer this Hadoop Ecosystem Components tutorial for the detailed study of All the Ecosystem components of Hadoop.
So, this was all about the Hadoop Tutorial. Hope you like our explanation.

10. Conclusion: Hadoop Tutorial

Hence, in conclusion to this Big Data tutorial, we can say that Apache Hadoop is the most popular and powerful big data tool. Big Data stores huge amount of data in the distributed manner and processes the data in parallel on a cluster of nodes. It provides the world’s most reliable storage layer- HDFS. Batch processing engine MapReduce and Resource management layer- YARN. 4 daemons (NameNode, datanode, node manager, resource manager) run in Hadoop to ensure Hadoop functionality.
If this Hadoop tutorial for beginners was helpful or if you have any queries feel free to comment in the below comment box.
See Also-

For reference

38 Responses

  1. sivan says:

    A very elaborate .informative guide for beginners

    • Data Flair says:

      Hello Sivan,
      Thank you for your feedback. I’m very happy to know that our lesson Hadoop Tutorial for beginners is useful to you.
      This Hadoop Tutorial is designed to be simple for its users so that not only the professionals but even beginners can understand the Hadoop concept.

  2. Jacks says:

    This is a very comprehensive introduction to Hadoop , it covers all the key concepts really well and the tutorial is written in a very easy to understand way without any unnecessary complications which make this a great way to get started with learning Hadoop.

    • Data Flair says:

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  3. Praveen says:

    Inquiry about Hadoop tutorial.

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  4. Mayur kohli says:

    Great and helpful article on hodoop. One simply need to read this on basics of hadoop. You have explained it very nicely.Thanks for sharing.

  5. manaswini vemuri says:

    thankyou so much, This is very very useful and helpful
    and this installation is very clear and having without any mistakes

    • Data Flair says:

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  6. harman says:

    Hi,
    Thanks for sharing the great information about Hadoop… Its useful and helpful information…Keep Sharing.

    • Data Flair says:

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  7. Poonam says:

    Please help me How to Find Mean, Median and Mode Using Python?

  8. Kriti says:

    Very informative Hadoop tutorial. There should be more such Hadoop tutorials for beginners as they need basic level hadoop tutorials on what is Hadoop and similar type of questions in simple terms.

    • Data Flair says:

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  9. Sumeet says:

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  10. Aayush says:

    The Big Data Hadoop Tutorial Video was very helpful. Thanks for creating such video. Even the blog post on Hadoop tutorial was very nicely explained. It helped understand apache hadoop from core.

    • Data Flair says:

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  11. Rinku Singh says:

    The Big Data Hadoop Tutorial Video was very helpful. Thanks for creating such video. Even the blog post on Hadoop tutorial was very nicely explained. It helped understand apache hadoop from core.

    • Data Flair says:

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  12. Avika says:

    Privileged to read this informative blog on Hadoop tutorial which helped me clearly understand what is hadoop. Commendable efforts to put on research the hadoop. Please enlighten us with regular updates on hadoop.

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  13. Dorababu says:

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  14. Paras says:

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  15. Lily L LU says:

    It’s very helpful for me, I’ve got a fast beginning with this tutorial and know more about this Hadoop infrastructure.

    • Data Flair says:

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  16. Shriprasad N Kale says:

    Very Good and happy your site provides such good Knowledge base.

    • Data Flair says:

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  17. Adel says:

    I felt so happy because of the way you described Hadoop big data and all related technology. thank you I really appreciate it.

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  18. yogesh jagdale says:

    Mind blowing stuff ,
    i’m beginner for hadoop and i want become zero to hero in hadoop, please provide another valuable information

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  19. Mandeep says:

    Thank you so much for sharing this post. I appreciate your work and it was worth spending time here 🙂 Lot to learn.
    Thanks again!

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