Big Data with Few Drawbacks

As nothing has been perfect in the world, Hadoop also has its set of limitations. Hadoop has become popular and has been used by big IT companies in a very short span of time. Big giants like Facebook, Twitter, eBay etc are taking full advantage of it.  But the industry experts has declared that Hadoop is still in its adolescent stage (its still a baby), which means its still needs maturity and functionality before it should get fully channelized.

The major drawback of Hadoop is related to Security. Hadoop also miss the encryption at the storage and network levels, which is a major selling point for government agencies and others that prefer to keep their data under wraps. Also its Programming model is very restrictive due to which central data cannot be preventive. The fundamentals of Hadoop were not designed to facilitate highly interactive analytics.

Hadoop is open source software which means it is made by the contributions of the various developers who continue to work on the project. While improvements are constantly being made for instance all open source software, Hadoop has had its fair share of stability issues. To avoid these issues, organizations are strongly  recommended to make sure they are running the latest stable version, or run it under a third-party vendor equipped to handle such problems.

Big data is not exclusively made for big businesses. Infact not all big data platforms are suited for small data needs. Unfortunately, Hadoop happens to be one of them. Due to its high capacity design, the Hadoop Distributed File System or HDFS, lacks the ability to efficiently support the random reading of small files. As a result, it is not recommended for organizations with small quantities of data. Also, Hadoop is still a single master which requires care and may limit scaling.

By nature Big Data Hadoop is little week or we can say it is vulnerable. The reason of this weakness is that the framework of Hadoop is written almost entirely in Java.  We all know that Java programming language is one of the most widely used yet the most controversial programming languages in existence. Infact it has been heavily exploited by cybercriminals and as a result, implicated in numerous security breaches. For this reason, several experts have suggested dumping it and adopting more efficient alternatives.

Being an open source software, Hadoop is not free. Companies which are implementing Hadoop, they generally choose a commercial distributor of the big data framework, which pose maintenance and support costs. As a result, prospective users have to hire experienced programmers or train existing employees on working not only with Hadoop, but also with MapReduce and related technologies such as Hive, HBase, Spark and Pig.

Because of its few drawbacks Hadoop adoption remains relatively low. For example, only 10% of the 284 respondents to a 2015 Gartner survey, said that their organizations were using Hadoop in production applications; another 16% said they were running pilot projects or experimenting with Hadoop, but 54% had no plans to use the technology.