What is Big Data Hadoop?

 

What is Big Data Hadoop? IsIt Easy To Learn?

Data has grown enormously in the past few years, making it impossible for traditional storage systems to manage it. Hadoop is the first choice for organizations that look for inexpensive and efficient ways to store and process large data sets. Developed by Michale J. and Doug Cutting, it is a Java-based, open-source framework that stores and processes data in a distributed environment.

Instead of using a single system, Hadoop uses multiple systems that work in parallel, allowing faster storage and retrieval of data as well as concurrent processing. It is currently managed by the Apache software foundation and comes under the Apache license 2.0 Hadoop. Companies of all sizes are using it to make better business decisions.

Learning Hadoop is not very difficult. However, to know how it works, you need to have a grip on its architecture. Let me take you through the Hadoop architecture to help you understand it.

Hadoop architecture comprises three components, namely HDFS, YARN, and Map Reduce. Actually, Hadoop architecture is based on a master-slave topology, where the master node manages resources and assigns tasks to the slave nodes.  Slave nodes have the real data, and they are the ones that do the computing.

Components of Hadoop Architecture

HDFS: The Hadoop distributed file system takes care of the storage front of the Hadoop ecosystem. It breaks a file into data blocks and creates multiple replicas to store them in slave nodes in a cluster. By default, the size of block is 128 MB, which can be configured as needed.  It consists of two daemons: Name Node and Data Node.

·         Name Node: The filesystem metadata is stored in the Name Node, including the names of the files, information about blocks of a file, permissions, location of blocks, etc. It manages the Data nodes.

·         Data Node: The actual data is stored in the slave nodes known as Data Nodes. It works based on the instructions of the Name Node and takes care of the client read/write requests.

Map Reduce: It handles the processing of data. In this software, you can write programs that can run on Hadoop and process large amounts of data in parallel stored on the community hardware clusters.  The processing is done in two stages: Map and Reduce. The Map function works on small chunks of data spread across clusters while the reduce function aggregates the data.

YARN: Yet Another Resource Negotiator manages the cluster and scheduling of tasks. It has a Resource Manager, Node Manager, and Application Master.

So, these were the basic modules in a Hadoop architecture. If you want to go deep and know how they work and how to implement it, you should consider Hadoop Training in California. Training will help you focus on the most relevant technologies related to Hadoop, such as

·         HDFS, Map Reduce, YARN for storing and processing

·         Pig, HBase, and Hive concepts

·         Tools such as Apache Flume, Sqoop, Spark, Oozie, Mahout,

·         Spark and Scala for data processing

·         Python and R programming for data science and analytics

·         Kafka for real-time streaming or processing

·         Cassandra, MongoDB, Hbase, for NoSQL databases

·         Elastic Search(ELK Stack) for real-time search and reporting

You should also remember that as Hadoop is a java framework, you need to be strong in the advanced java concepts such as

·         OOPS concept

·         Collections (Hash Maps, Arrays, binary trees, Linked List) and generics, auto boxing

·         Concurrency, multi-threading, and data structures

·         Inheritance and Polymorphism

·         Conditional and looping statements

How To learn Hadoop?

You can approach learning Hadoop in two ways. You can learn it on your own if you have prior knowledge of advanced java concepts. But self-learning way can take a lot of time. Also, it will depend upon your intellect and learning skills. I would suggest you choose the BestHadoop Course Training in California as you will be able to learn it in a systematic manner.

As the training courses are designed by professionals, they know which topic to address first and what topics to include. With technology like Hadoop, you need to understand the basics and essentials quickly. Taking a course will help you avoid mistakes and let you dive deeper into the concepts that can otherwise take you months to learn on your own. There is no substitute for a domain expert, systematic training where you can get your doubts cleared and get clarifications.