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 and Hadoop is a good choice, but there are some challenges as well that come with Hadoop. In this blog, I m discussing some major concerns with Hadoop for Big Data
But first have a quick look why Hadoop is touted as a standard platform for Big Data
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High data storage and processing speed
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Scalability
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Flexibility
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Free open-source framework
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Gives protection to data and application processing against hardware failure
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High computing power
Now let’s talk about negative points of Big Data Hadoop implementation
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Not suitable for small data: Big data analytics 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.
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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 cybercriminals having exploited Java in the past.
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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.
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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.
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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.
Those indeed are the challenges to use Hadoop for big data, but Hadoop can significantly help boost your business growth when handled by experts. Evon Technologies is one such experienced company which offers AWS data integration and deployment services using Hadoop, and makes it easier for clients to access large amounts of computing power to run data-intensive tasks. Please get in touch with us here to get started.