The Application of Hadoop in Management of Big Data
Management of big data is quite an uphill task for many organizations. They rely on large data analytic tools to analyze the gigantic data, to make sensible conclusions, and solve various problems facing their day to day operations. Since the handling of data is quite technical, organizations are in a better position when they seek the services of data specialists like Expert Django development and engineering who employ various tools to help them solve the complex issues. One of the tools used in big data management is Hadoop.
What is Hadoop?
Hadoop is an open-source software structure for big data storage and running programs on several linked commodity hardware. It contains an enormous data storage space, great processing power and the ability to take care of innumerable tasks simultaneously.
Why do we and organizations need Hadoop?
The need for Hadoop arises from the demand to process volumes of big data which many entities have. Hadoop has evolved from its initial use in web indexing. It is now employed in various industries for many different tasks that have the similarities of a wide range, enormous size, and quickly generated data that is both structured and unstructured. It is greatly used in various industries such as the retail, healthcare, and government departments, media, and many other industries that deal with big data.
What problems are organizations generally facing, and how does Hadoop help solve them?
Organizations and individuals are facing a problem of managing big data, and more and more data is being generated daily. Hadoop comes in handy by dealing with big data. It was built to process huge amounts of data, ranging from terabytes to petabytes and even bigger. Such enormous data can hardly fit on the hard drive of a single computer, as its memory is much smaller than that. The beauty of Hadoop is that it is set up to conveniently work with big data by linking many commodity computers to function concurrently.
Why should an organization choose Hadoop over other frameworks?
Hadoop is the best framework to use for handling big data, despite there being other frameworks in the market, for the following reasons;
- Economical : Hadoop, compared to the traditional frameworks, allows accommodation of large data at a relatively lower cost, and is expandable at a lower cost to handle extra data.
- Scalability : It is a highly expandable platform for storage of huge data by distributing the data across many affordable servers operating simultaneously.
- Speedy : In the network of several machines, the data is located on the same server as the analytic tools, leading to faster processing of data.
- Flexibility : It allows entities using it to have easy access to new sources of data and to make use of varying types of data, including structured and unstructured.
- Failure resistant : data sent to one terminal is replicated to the others. Hence, in case of a failure of the initial recipient, there are copies of the same data in other terminals
What is the Usability of Hadoop?
The usability of a framework when dealing with voluminous data is as crucial as its performance. The focus when considering the usability of a large data framework is based on how management of data is done, the ease of interaction with the data, and the need to facilitate impromptu analysis of the data. Hadoop meets these three criteria, thus its usability is guaranteed.