Tuesday, January 3, 2017

Limitations of Big Data Hadoop, An overview

The era of ‘big data’ didn’t fail in representing the new challenges to businesses as any new software does. There is a huge explosion in the incoming data volume in complexity, speed, variety, and volume. And definitely, the legacy tools have not kept the pace for this generation of business. Before it’s too late, the developers came up with a wonderful solution for the mess with a new tool on the scene ‘Hadoop’.

It is not a magic! And it really can solve some big data problems. As a result, many companies started to adopt Hadoop in their IT infrastructure. Nowadays it is not a big deal to learn after some effort to start online Hadoop training. For the people who use old Big Data with a strong engineering team, it usually is not a big problem in designing the target system, choosing a technology stack to start implementation.

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The businesses or people with a good amount of experience in this can still sometimes face hitches with all the complexity, but when it comes to Hadoop beginners, they face countless challenges in order to get started. And nowadays you can also learn Hadoop online training in Hyderabad.

So, what are the limitations?

  • In order to achieve desired results on big data, all the businesses must be able to comprehend data in a quick basis, along with that it also must be able to explore the data for the value that allows analysts to ask and iterate their questions related to the business quickly.

  • The purpose of building Hadoop is to facilitate certain configuration of batch-oriented distributed data processing that lends itself to the assimilation process readily. But the fact is, it was built on fundamentals and that could severely limit its ability to act as an analytic database.

  • The rise of the analytic database platform came along with the rise of big data. Even half a decade ago, an organization could leverage a DBMS like Oracle for a data warehouse.

  • But the big difference is Oracle was built in a time when databases exceeded a size of a few gigabytes. And when you enter the analytic platform, it allows analysts to use the existing tools as well as skillsets in order to ask new questions of big as quickly as possible. It is very easy and at scales unseen previously.

  • But nowadays enabling the highly iterative analysis process is simple for many people since they can gain a complete knowledge about to through online Hadoop training.

Why these limitations?

  • As many people know Hadoop is written in Java, and that is slower than the frameworks in C or C++.

  • Hadoop File System is landing data between reducing steps, and that is considered as a huge data constraint.

  • Moreover, Hadoop File System centrally manages an index that is necessary to map all the tasks to the data distributed to the entire node, people say that is a documented bottleneck.

Despite all these issues, Hadoop still ruling the data field and still it holds plenty of future career opportunities. And there are ongoing researches to get rid of these issues. learn Hadoop online training in Hyderabad in order to enlighten your future in the field you love!

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