5 Top Myths Surrounding Big Data and Hadoop Debunked

While Hadoop brings a number of positives to the table as a technology, it is also surrounded by a lot of myths. Unfortunately, these false notions have effectively kept them from putting the technology to use. This, in turn, has prevented the organisations from achieve the strong ROI on big data which could have been achieved by using it.

If you are also unsure about using Hadoop and big data, here is what you should consider before making the final call.

1. Hadoop is only helpful for batch processing: One of the most prevalent misconceptions surrounding Hadoop is that it can only be used for batch processing. Though it is true that both Hadoop and big data can be used in tandem to process larger quantities of historical data, it can also be used for real-time data with the same degree of effectiveness. In fact, it offers a powerful and practical solution for the latter. Hadoop also works well in collaboration with big data solutions such as Spark.

2. Hadoop is not secure: There exists a misconception that Hadoop fares poorly in terms of security. It is important to acknowledge that Hadoop is an open source project. Just like the other projects of the similar nature, it also receives its security improvements in a gradual manner. With larger businesses taking on big data initiatives, there has been a significant improvement in its security over time.

3. Data governance is difficult with Hadoop: Though there were issues with governance in Hadoop at the initial stages, there has been a major enhancement in its governance with the launch of newer versions. The use of the open source technology with Navigator and Cloudera exemplify it in the best possible manner.

4. Big Data and Hadoop are useful to handle only unstructured and unusual data: Because it was touted to be a worthwhile technology to deal manage large and unstructured data, most users started perceiving it as a technology which was only good for managing unstructured data. However, in practical terms, Hadoop can be as much useful for analysing structured data as it is for unstructured data.

5. Numerous data scientists and programmers are necessary to operate big data and Hadoop: While it is true that Hadoop needs programming to yield results, it is not necessary to have an army of Hadoop data scientists or programmers for the purpose. There are tools and solutions that can be used to perform tasks like the collection of data, storage and analysis of data which generally demands manual labour. With these tools, a handful of programmers and data scientists can accomplish these tasks with ease.