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What are the features of Hadoop ?

Hadoop supports the storage and processing of big data. It is the best solution for handling big data challenges. Some important features of Hadoop are –

What are the features of Hadoop

Open Source

  • Hadoop is an open source framework which means it is available free of cost.
  • Also, the users are allowed to change the source code as per their requirements.

Distributed Processing

  • Hadoop supports distributed processing of data i.e. faster processing.
  • The data in Hadoop HDFS is stored in a distributed manner and MapReduce is responsible for the parallel processing of data.

Fault Tolerance

  • Hadoop is highly fault-tolerant. It creates three replicas for each block at different nodes, by default.
  • This number can be changed according to the requirement. So, we can recover the data from another node if one node fails.
  • The detection of node failure and recovery of data is done automatically.

Reliability

  • Hadoop stores data on the cluster in a reliable manner that is independent of machine.
  • So, the data stored in Hadoop environment is not affected by the failure of the machine.

Scalability

  • Another important feature of Hadoop is the scalability. It is compatible with the other hardware and we can easily ass the new hardware to the nodes.
Scalability in hadoop

Economic

  • Apache Hadoop is not very expensive as it runs on a cluster of commodity hardware.
  • Hadoop also provides huge cost saving also as it is very easy to add more nodes on the fly here. So if requirement increases, then you can increase nodes as well without any downtime and without requiring much of pre-planning.
Economic in hadoop

Easy to use

  • No need of client to deal with distributed computing, the framework takes care of all the things. So this feature of Hadoop is easy to use.

Data Locality

  • This one is a unique features of Hadoop that made it easily handle the Big Data. Hadoop works on data locality principle which states that move computation to data instead of data to computation.
  • When a client submits the MapReduce algorithm, this algorithm is moved to data in the cluster rather than bringing data to the location where the algorithm is submitted and then processing it.
Data Locality

High Availability

  • The data stored in Hadoop is available to access even after the hardware failure. In case of hardware failure, the data can be accessed from another path.
High Availability
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Why Hadoop used for Big Data Analytics ?

  • Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
  • Hadoop is a framework to store and process big data. Hadoop specifically designed to provide distributed storage and parallel data processing that big data requires.

Hadoop is the best solution for storing and processing big data because:

Hadoop stores huge files as they are (raw) without specifying any schema.

  • High scalability – We can add any number of nodes, hence enhancing performance dramatically.
  • High availability – In hadoop data is highly available despite hardware failure. If a machine or few hardware crashes, then we can access data from another path.
  • Reliable – Data is reliably stored on the cluster despite of machine failure.
  • Economic – Hadoop runs on a cluster of commodity hardware which is not very expensive.

What is Hadoop ?

  • Hadoop is an open source project from Apache Software Foundation.
  • It provides a software framework for distributing and running applications on clusters of servers that is inspired by Google’s Map-Reduce programming model as well as its file system(GFS).
  • Hadoop was originally written for the nutch search engine project.
  • Hadoop is open source framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware.
  • Hadoop can be setup on single machine , but the real power of Hadoop comes with a cluster of machines , it can be scaled from a single machine to thousands of nodes. Hadoop consists of two key parts,
    • Hadoop Distributes File System(HDFS)
    • Map-Reduce.
Hadoop Overview

Hadoop Distributed File System(HDFS)

  • HDFS is a highly fault tolerant, distributed, reliable, scalable file system for data storage.
  • HDFS stores multiple copies of data on different nodes; a file is split up into blocks (Default 64 MB) and stored across multiple machines.
  • Hadoop cluster typically has a single namenode and number of datanodes to form the HDFS cluster.

Map-Reduce

  • Map-Reduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks.
  • It is also a paradigm for distributed processing of large data set over a cluster of nodes.
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