Introduction to Is Hadoop Open Source?
Hadoop is open source that provides space for storage for large datasets and it is stored on groups of software with similarities. Hadoop is a project of Apache and it is used by different users also supported by a large community for the contribution of codes. The license is License 2.0. Free Hadoop is not productive as the cost comes from the operation and maintenance cost rather than the installation cost.
Features of Hadoop
As we have studied above about the introduction to Is Hadoop open source, now we are learning the features of Hadoop:
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Open Source
The most attractive feature of Apache Hadoop is that it is open source. It means Hadoop open source is free. Anyone can download and use it personally or professionally. If at all any expense is incurred, then it probably would be commodity hardware for storing huge amounts of data. But that still makes Hadoop inexpensive.
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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 unit, CPU at the lower cost. Definitely, you can move to such companies.
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Low Cost
As Hadoop Framework is based on commodity hardware and open source software framework. It lowers down the cost while adopting it in the organization or new investment for your project.
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Scalability
It’s the property of a system or application to handle bigger 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 a big connotation is that the adaptation will be some kind of 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 machines in your cluster. You are expecting 6 TB of data next month. But your cluster can handle only 3 TB more. Hadoop provides you with the feature of horizontal scaling – it means you can add any number of the system as per your cluster requirement.
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Highly robust
The fault tolerance feature of Hadoop makes it really popular. Hadoop provides you feature like Replication Factor. It means your data is replicated to other nodes as defined by replication factor. Your data is safe and secure to other nodes. If ever a cluster fail happens, the data will automatically be passed on to another location. This will ensure that data processing is continued without any hitches.
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Data Diversity
Apache Hadoop framework allows you to deal with any size of data and any kind of data. Apache Hadoop framework helps you to work on Big Data. You will be able to store and process structured data, semi-structured and unstructured data. You are not restricted to any formats of data. You are not restricted to any volume of data.
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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 Processing layer is called Map Reduce. On top on HDFS, you can integrate into any kinds of 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.
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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 on the mainframe. The tools for data processing are often on the same servers where the data is located, resulting in much faster data processing. If you’re dealing with large volumes of unstructured data, Hadoop is able to efficiently process terabytes of data in just minutes, and petabytes in hours.
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Easy To Use
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 the algorithm on JAVA itself. If you are working on tools like Apache Hive. It is based on SQL. Any developer having the background of the database can easily adopt Hadoop and can work on Hive as a tool.
Conclusion
2.7 Zeta bytes of data exist in the digital universe today. Big Data is going to dominate the next decade in the data storing and processing environment. Data is going to be center model for the growth of the business. There is the requirement of a tool which is going to fit for 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.
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