5 Big Disadvantages of Hadoop for Big Data

5 Big Disadvantages of Hadoop for Big Data

As the backbone of so many implementations, Hadoop is almost synomous with big data. Offering distributed storage, superior scalability, and ideal performance, many view it as the standard platform for high volume data infrastructures. But as an article on Google’s big data expertise suggests, Hadoop isn’t necessarily the end all be all to big data.

1. Security Concerns

Just managing a complex application such as Hadoop can be challenging. A classic example can be seen in the Hadoop security model, which is disabled by default due to sheer complexity. If whoever’s managing the platform lacks the knowhow to enable it, your data could be at huge risk. Hadoop is also missing 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.

2. Vulnerable By Nature

Speaking of security, the very makeup of Hadoop makes running it a risky proposition. The framework is written almost entirely in Java, one of the most widely used yet controversial programming languages in existence. Java has been heavily exploited by cybercriminals and as a result, implicated in numerous security breaches. For this reason, several experts have suggested dumping it in favor of safer, more efficient alternatives.

3. Not Fit for Small Data

While big data isn’t exclusively made for big businesses, 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.

4. Potential Stability Issues

Hadoop is an open source platform. That essentially means it is created by the contributions of the many developers who continue to work on the project. While improvements are constantly being made,

like 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.

5. General Limitations

One of the most interesting highlights of the Google article referenced earlier mentions that when it comes to making the most of big data, Hadoop may not be the only answer. The article introduces Apache Flume, MillWheel, and Google’s own Cloud Dataflow as possible solutions. What each of these platforms have in common is the ability to improve the efficiency and reliability of data collection, aggregation, and integration. The main point the article stresses is that companies could be missing out on big benefits by using Hadoop alone.

Now that the flaws of Hadoop have been exposed, will you continue to use it for your big data initiatives, or swap it for something else?

5 Big Disadvantages of Hadoop for Big Data
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