Have you decided to wait on big data, because it doesn’t offer real value to your organization or because the technologies used for big data, such as Hadoop, haven’t yet reached the pinnacle of usefulness? If so, then you probably need to rethink that. There are a lot of myths surrounding Hadoop and big data, and many of these myths are keeping good organizations from getting a strong ROI on big data. Have you fallen into one of these common misconceptions.
1. Hadoop is Batch Only
Perhaps the most prevalent myth is that Hadoop is only useful for batch processing. Batch processing is the act of processing large quantities of historical data and is generally a process that is slow, or at least not fast enough for real time intelligence.
The reality is that it’s also a practical, powerful solution for real time analytics. Hadoop also works well with other real time big data solutions, including Spark, and is capable of handling immediate processing jobs like online customer transactions where there is no room for errors or latency. (Related: Databricks’ Reynold Xin on Structured Streaming, Apache Kafka and the Future of Spark)
2. Security is No Good with Hadoop
The reality is that (like many open source projects) security was built into Hadoop gradually. In the beginning, it was hard to achieve enterprise level security in Hadoop, but over the past few years, as many large businesses took on big data initiatives, Hadoop has come a long, long way in terms of security. Hadoop ecosystem leaders have built enterprise grade security into their Hadoop Platforms, including Cloudera in Cloudera Enterprise, Hortonworks in the Hortonworks Data Platform, and MapR in its Converged Data Platform. You can definitely achieve the level of security you need for your big data operations in Hadoop.
3. There is No Good Way to Achieve Data Governance in Hadoop
This is closely related to the issue of security. While in the beginning there wasn’t a lot of mechanism built into Hadoop for governance, that issue has been resolved with more recent versions. There are now excellent governance capabilities, with Cloudera Navigator,
Hortonwork’s integration with the Apache Falcon and Apache Atlas, and on the MapR Converged Data platform as well, built specifically to provide governance in the Hadoop environment.
4. Big Data and Hadoop are Only Useful for Strange, Unstructured Data
It’s kind of easy to see where this myth came from. Hadoop was widely and loudly touted as the solution when it comes to managing and analyzing unstructured data, so naturally, it became widely assumed that unstructured data was all it’s good for. However, the same powerful analytics capabilities that are so useful on unstructured data work just as well for the structured data sets, and the semi-structured data, as well. Hadoop doesn’t discriminate when it comes to munching enormous sets of data.
5. You Need an Army of Programmers and Data Scientists to Get Meaningful Answers Out of Big Data & Hadoop
Finally, there is no need to have an army of Hadoop programmers and data scientists on hand to get use out of big data and Hadoop. With the right tools and solutions, you can collect data, store data, and analyze data with just a handful of folks. What tools do you need? Syncsort offers all the tools you need to offload your mainframe data into Hadoop and get started on your big data initiative right away. Syncsort has Big Data solutions to simplify and accelerate the on-ramp for you to get there.
Get Syncsort’s eBook Bringing Big Data to Life: Overcoming Challenges of Legacy Data in Hadoop to understand the challenges associated with integrating mainframe data into Hadoop and how to solve them.