Updated March 15, 2023
Introduction to Features of Hadoop
This is an outline on the Features of Hadoop. It is formally called Apache Hadoop. Apache Hadoop is the top-level project of the Apache Community. Apache Hadoop is an Apache Software Foundation project and open source software platform. Apache Hadoop is designed for scalable, fault tolerance, and distributed computing. Hadoop can provide fast and reliable analysis of both structured and unstructured data. Open source software is code anyone can inspect, modify, and enhance. Open Source is a certification standard issued by the Open Source Initiative (OSI) that indicates that a computer program’s source code is made free of charge to the general public. Open source software is distributed with the source code under an open source license. The open source code is typically created as a collaborative effort in which programmers improve upon the code and share the changes within the community. Software gets updated very fast under Apache Community. Any programmer or company can modify source code per their requirement and release a new software version to the Apache Community platform.
Most Popular Features of Hadoop
Below are the top 9 Features of Hadoop:
- Open Source – The most attractive feature of Apache Hadoop is its open source. It means this framework of the software is free. Anyone can download and use it personally or professionally. If any expense is incurred, it would probably be commodity hardware for storing vast data. But that still makes Hadoop inexpensive.
- Commodity Hardware – Apache Hadoop runs on commodity hardware. Commodity hardware means you are not sticking to any single vendor for your infrastructure. Any company providing hardware resources like Storage units and CPUs at a lower cost. You can move to such companies.
- Low Cost – The Hadoop Framework is based on commodity hardware and open-source software. It lowers the cost while adopting it in an organization or new investment for your project.
- Scalability – It’s the property of a system or application to handle more significant amounts of work, or to be easily expanded, in response to increased demand for network, processing, database access, or file system resources. Hadoop is a highly scalable storage platform. Scalability is the ability of something to adapt over time to changes. The modifications usually involve growth, so an extensive connotation is that the adaptation will be some expansion or upgrade. Hadoop is horizontally scalable. It means you can add any number of nodes or machines to your existing infrastructure. Let’s say you are working on 15 TB of data and 8 devices in your cluster. You are expecting 6 TB of data next month. But your cluster can handle only 3 TB more. Hadoop provides a horizontal scaling feature- you can add any number of a system per your cluster requirement.
- Highly robust- The fault tolerance features of Hadoop make it popular. Hadoop provides you with features like Replication Factor. It means your data is replicated to other nodes as the replication factor defines. Your data is safe and secure to other nodes. If a cluster fails, the data will automatically be passed on to another location. This will ensure that data processing is continued without any hitches.
- Data Diversity- Apache Hadoop framework allows you to deal with any size and kind of data. Apache Hadoop framework helps you to work on Big Data. You will be able to store and process semi-structured and unstructured data. You are not restricted to any formats of data. You are not restricted to any volume of data.
- Multiple Frameworks for Big Data – There are various tools for various purposes. Hadoop framework has a wide variety of tools. Hadoop framework is divided into two layers. Storage Layer and Processing Layer. The storage layer is called the Hadoop Distributed File System, and the Processing layer is called Map Reduce. On top of HDFS, you can integrate any tools supported by Hadoop Cluster. Hadoop can be integrated with multiple analytic tools to get the best out of it, like Mahout for Machine-Learning, R and Python for Analytics and visualization, Python, Spark for real-time processing, MongoDB,, and HBase for NoSQL database, Pentaho for BI, etc. It can be integrated into data processing tools like Apache Hive and Apache Pig. It can be integrated with data extraction tools like Apache Sqoop and Apache Flume.
- Fast processing – While traditional ETL and batch processes can take hours, days, or even weeks to load large amounts of data, the need to analyze that data in real-time is becoming critical day after day. Hadoop is extremely good at high-volume batch processing because of its ability to do parallel processing. Hadoop can perform batch processes 10 times faster than on a single thread server or the mainframe. The tools for data processing are often on the same servers where the data is located, resulting in the much faster data processing. If you’re dealing with large volumes of unstructured data, Hadoop can efficiently process terabytes of data in minutes and petabytes in hours.
- Easy To Use – The Hadoop framework is based on Java API. There is not much technology gap as a developer while accepting Hadoop. Map Reduce framework is based on Java API. You need code and write an algorithm on JAVA itself. If you are working on tools like Apache Hive. It is based on SQL. Any developer with a database background can easily adopt Hadoop and work on Hive as a tool.
2.7 Zeta bytes of data exist in the digital universe today. Big Data will dominate the data storage and processing environment next decade. Data is going to be the center model for the growth of a business. There is a requirement for a tool that will fit all these. Hadoop suits well for storing and processing Big Data. All the above features of Big Data Hadoop make it powerful for the widely accepting Hadoop. Big Data is going to be the center of all the tools. Hadoop is one of the solutions for working on Big Data.
This is a guide to Features of Hadoop. Here we discuss the different features of Hadoop like Low Cost, Scalability, Data Diversity, etc. You may also look at the following article to learn more –