Today tons of Companies are adopting Hadoop Big Data tools to solve their Big Data queries and their customer market segments. There are lots of other tools also available in the Market like HPCC developed by LexisNexis Risk Solution, Storm, Qubole, Cassandra, Statwing, CouchDB, Pentaho, Openrefine, Flink, etc. Then why Hadoop is so popular among all of them. Here we will discuss some top essential industrial ready features that make Hadoop so popular and the Industry favorite.
Hadoop is a framework written in java with some code in C and Shell Script that works over the collection of various simple commodity hardware to deal with the large dataset using a very basic level programming model. It is developed by Doug Cutting and Mike Cafarella and now it comes under Apache License 2.0. Now, Hadoop will be considered as the must-learn skill for the data-scientist and Big Data Technology. Companies are investing big in it and it will become an in-demand skill in the future. Hadoop 3.x is the latest version of Hadoop. Hadoop consist of Mainly 3 components.
- HDFS(Hadoop Distributed File System): HDFS is working as a storage layer on Hadoop. The data is always stored in the form of data-blocks on HDFS where the default size of each data-block is 128 MB in size which is configurable. Hadoop works on the MapReduce algorithm which is a master-slave architecture. HDFS has NameNode and DataNode that works in a similar pattern.
- MapReduce: MapReduce works as a processing layer on Hadoop. Map-Reduce is a programming model that is mainly divided into two phases Map Phase and Reduce Phase. It is designed for processing the data in parallel which is divided on various machines(nodes).
- YARN(yet another Resources Negotiator): YARN is the job scheduling and resource management layer in Hadoop. The data stored on HDFS is processed and run with the help of data processing engines like graph processing, interactive processing, batch processing, etc. The overall performance of Hadoop is improved up with the Help of this YARN framework.
Features of Hadoop Which Makes It Popular
Let’s discuss the key features which make Hadoop more reliable to use, an industry favorite, and the most powerful Big Data tool.
1. Open Source:
Hadoop is open-source, which means it is free to use. Since it is an open-source project the source-code is available online for anyone to understand it or make some modifications as per their industry requirement.
2. Highly Scalable Cluster:
Hadoop is a highly scalable model. A large amount of data is divided into multiple inexpensive machines in a cluster which is processed parallelly. the number of these machines or nodes can be increased or decreased as per the enterprise’s requirements. In traditional RDBMS(Relational DataBase Management System) the systems can not be scaled to approach large amounts of data.
3. Fault Tolerance is Available:
Hadoop uses commodity hardware(inexpensive systems) which can be crashed at any moment. In Hadoop data is replicated on various DataNodes in a Hadoop cluster which ensures the availability of data if somehow any of your systems got crashed. You can read all of the data from a single machine if this machine faces a technical issue data can also be read from other nodes in a Hadoop cluster because the data is copied or replicated by default. By default, Hadoop makes 3 copies of each file block and stored it into different nodes. This replication factor is configurable and can be changed by changing the replication property in the hdfs-site.xml file.
4. High Availability is Provided:
Fault tolerance provides High Availability in the Hadoop cluster. High Availability means the availability of data on the Hadoop cluster. Due to fault tolerance in case if any of the DataNode goes down the same data can be retrieved from any other node where the data is replicated. The High available Hadoop cluster also has 2 or more than two Name Node i.e. Active NameNode and Passive NameNode also known as stand by NameNode. In case if Active NameNode fails then the Passive node will take the responsibility of Active Node and provide the same data as that of Active NameNode which can easily be utilized by the user.
Hadoop is open-source and uses cost-effective commodity hardware which provides a cost-efficient model, unlike traditional Relational databases that require expensive hardware and high-end processors to deal with Big Data. The problem with traditional Relational databases is that storing the Massive volume of data is not cost-effective, so the company’s started to remove the Raw data. which may not result in the correct scenario of their business. Means Hadoop provides us 2 main benefits with the cost one is it’s open-source means free to use and the other is that it uses commodity hardware which is also inexpensive.
6. Hadoop Provide Flexibility:
Hadoop is designed in such a way that it can deal with any kind of dataset like structured(MySql Data), Semi-Structured(XML, JSON), Un-structured (Images and Videos) very efficiently. This means it can easily process any kind of data independent of its structure which makes it highly flexible. It is very much useful for enterprises as they can process large datasets easily, so the businesses can use Hadoop to analyze valuable insights of data from sources like social media, email, etc. With this flexibility, Hadoop can be used with log processing, Data Warehousing, Fraud detection, etc.
7. Easy to Use:
Hadoop is easy to use since the developers need not worry about any of the processing work since it is managed by the Hadoop itself. Hadoop ecosystem is also very large comes up with lots of tools like Hive, Pig, Spark, HBase, Mahout, etc.
8. Hadoop uses Data Locality:
The concept of Data Locality is used to make Hadoop processing fast. In the data locality concept, the computation logic is moved near data rather than moving the data to the computation logic. The cost of Moving data on HDFS is costliest and with the help of the data locality concept, the bandwidth utilization in the system is minimized.
9. Provides Faster Data Processing:
Hadoop uses a distributed file system to manage its storage i.e. HDFS(Hadoop Distributed File System). In DFS(Distributed File System) a large size file is broken into small size file blocks then distributed among the Nodes available in a Hadoop cluster, as this massive number of file blocks are processed parallelly which makes Hadoop faster, because of which it provides a High-level performance as compared to the traditional DataBase Management Systems.