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Top 5 Concerns of Big Data Hadoop Implementation
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Apache Hadoop is an open-source, java-based programming framework meant to process large data sets in a distributed environment. Hadoop has created a lot of Big Data hype in digital arena, with many viewing it as the best platform for handling high volume data infrastructures. Sorting of Big Data offers great opportunities to companies to increase their ROI by targeting or retargeting right customers. They can figure out what customers do not like about their products or services, therefore can go for a quick fix and improve their brand value. Moreover, they can provide personalized experiences and grow their list of loyal customers.

Big Data has a lot of virtues, but many companies are wary of Big Data Hadoop implementation. In this blog, I have discussed major constraints that make Hadoop inappropriate for Big Data

But first have a quick look why Hadoop is touted as a standard platform for Big Data

  • High data storage and processing speed

  • Scalability

  • Flexibility

  • Free open-source framework

  • Gives protection to data and application processing against hardware failure

  • High computing power

Now let’s talk about negative points of Big Data Hadoop implementation

  • Not suitable for small data: Big Data benefits are not restricted only to large organizations since small businesses also have a treasure trove of opportunities to skyrocket their sales by using it. But Hadoop has a high capacity design and is not fit for small data. Hadoop Distributed File System(HDFS) is incapable of reading small files randomly, thereby making Hadoop incompatibile with small data. This is a big setback factor in Big Data Hadoop implementation.  

  • Security and Vulnerability: Hadoop’s security model is not well-designed for complex applications and lacks encryption feature for storing and networking. Owing to these inadequacies, data sets are always at risk of being compromised. No organization wants its vital data to be leaked and become available to its competitors to pre-empt business strategies. Hadoop is also not secured against data breach as its framework is written in Java, which is vulnerable to cyber attacks. Many cases of cyber criminals having exploited Java in the past make Hadoop not to be completely trusted as far as data security is concerned.

  • Stability Issues:Being an open-source platform, Hadoop is surrounded by stability issues. Many developers have developed it and iterations are being continuously made, but its stability always remains one of top concerns. It’s very important for a company to ensure that they have put into use the latest stable version of Hadoop. Another way is to go for a third-party vendor who takes the responsibility of running it and fixing stability issues. But still, stability issues always make organizations uncertain over using Hadoop for processing of important big data sets.

  • Problems with Pig and Hive: Pig does not entertain Hive UDFs and Hive does the same to Pig too. Both can’t be used in one another. Pig script also does not offer any help whenever any requirement arises for extra functionality in Hive. If you want to access Hive tables in Pig, you need to use HCatalog.

  • Repository Functionality: Installation from Hadoop repository is not an easy task. It often takes a lot of effort because of mismanagement and improper act. Another flaw with Hadoop repository is that it does not keep a check on compatibility while installing any new application. As a result, compatibility issues emerge at later stages and cause annoyance.


There are other problems with Hadoop as well, like unrefined documentation, problems with Ambari installation and Oozie not behaving well when not properly distributed. But I have discussed the top five major concerns with Big Data Hadoop implementation.

I hope this blog helps you understand top challenges Hadoop is facing to become the best framework for Big Data. If you want to add more about what Hadoop needs to improve to become a best fit for big data, please share your views in the comment box below.
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