Exceptional Adoption Of Hadoop


Big Data is certainly the main area of focus in the digital era. There is a huge amount of data created and collected from tons of resources and through several processes. This data could include useful insights that can help the company to improve and grow, hence it is extremely significant for businesses to make good use of this data. Hadoop is one such fantastic piece of technology that helps companies to make good use of Big Data. The tool has the ability to analyze the data in order to churn out plentiful valuable information.

Hadoop is an all-encompassing batch processing set-up. Hadoop can be easily used in a single machine, however, it has the capability to scale to a large number of workstations, each with numerous processor cores. It is also intended to efficiently allocate huge amounts of work through a bunch of machines. Hadoop is known to be one of the front-runners in the big data development services field and it has already proved its caliber of smoothly resolving a host of the Big Data problems.

There's been an ample amount of buzz about the functionalities of Hadoop, including the way it stores, processes or analyzes colossal files and enormous volumes of data like no other solution. Hadoop was initially gestated in a big company to manage and analyze huge amounts of data in a cost-effective and streamlined manner. However, a complete network of complementary solutions have been grown around Hadoop, which allow better and better analytics of the unstructured data etc.

Though, the software is being embraced at a rapid pace, but there are certain shortcomings that lead to the lower adoption of this software and some of them are listed below:

1. Security concerns

Hadoop is a little vulnerable to cyber-attacks simply because it is built totally on Java. Java being one of the most extensively used programming languages, is also linked to numerous controversies as some of the cyber criminals find it easy to exploit the frameworks which are built completely on Java. And, this can lead to unexpected damages.

Also, it has its own set of challenges in handling intricate applications. Hadoop doesn't have any encryption and it is connected with a hard to manage, Kerberos authentication.

2. Proven Problems with smaller files

Hadoop is majorly used, and is known to be perfect for a huge quantity of data, and larger files. It is not apt for small data. Hadoop distributed file system doesn't have the capability to smoothly support the unsystematic reading of files that are smaller in size simply because of its great capacity design. A small file is considerably smaller than even the HDFS block size becomes difficult to be managed, and HDFS can’t manage a plenty of these small files as well.

3.Incapable of processing streamed data

Hadoop is incapable of processing streamed data as it only supports batch processing. This results in an overall lower rate of. The main reason behind this is that the MapReduce framework does not utilize or make use of the memory of Hadoop clusters to its full potential.

4. Not that fast

In this fast paced environment, we want everything to be quick however, the processing speed of Hadoop is on a slower side. The main reason behind the lower processing speed is the use of MapReduce. In Hadoop, data is dispersed and processed above the cluster in MapReduce and this leads to a greater time, and a lower speed of processing. Processing a large volume of data, along a parallel and distributed algorithm, through MapReduce takes a lot of time because there have to be multiple tasks that need to be executed, Map and Reduce hence, the latency is increased.

5. Real-time Data Processing is not possible

As Hadoop is developed for batch processing, hence, it requires a large amount of data in input. Processing of a huge amount of data to produce the results takes time, and the result can be delayed. Hence, Hadoop is not considered apt for Real-time data processing.


Hadoop has enjoyed its glory for years, but there were, and are a few shortcomings that the tool needs to overcome in order to become the best in every sense. There are a couple of substantial drawbacks of Hadoop. One of the major challenges is that Hadoop operations call for highly skilled resources. Also, cluster management is a little tough, plus debugging is hard as well. However, the advantages of using Hadoop solutions are mostly overriding the drawbacks hence, the demand of Hadoop is relatively good. However, it needs to up the game in order to keep the demand higher and higher.

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