Why Your Choice of Hadoop Infrastructure Is Important

The Big Data debate is over. Vast data pools being generated every day are in reality treasure troves of information that organizations can leverage through analytics to obtain valuable insights, drive innovation, boost ROI and create competitive advantage.

The Big Data debate is over. Vast data pools being generated every day are in reality treasure troves of information that organizations can leverage through analytics to obtain valuable insights, drive innovation, boost ROI and create competitive advantage. To meet the formidable challenge of analyzing data of massive volume, variety and velocity, Hadoop has emerged as the go-to scalable software solution for processing Big Data. The challenge then for organizations and IT, is to procure, deploy and effectively integrate all of the elements that constitute the Hadoop ecosystem. To facilitate the process, author Robert Schneider has just released the Hadoop Buyer’s Guide. This eBook, sponsored by Ubuntu, presents a series of guidelines organizations can use in their search for the essential Hadoop infrastructure.

Based on those guidelines, here’s a look at why your choice of Hadoop infrastructure is important.

As pointed out in the eBook, the comprehensive distributions that a number of vendors are currently offering fall into one of three models:

Model #1: Open source Hadoop and support

As the title implies, this model combines basic open source Hadoop with support and services provided by paid professionals. An example of this model is Hortonworks, a data platform that utilizes open source Apache Hadoop.

Model #2: Open source Hadoop, support, and management innovations

This strategy takes open source Hadoop to the next level by combining it with tools and utilities designed to make things easier for mainline IT organizations. A vendor known for offering this model is Cloudera.

Model #3: Open source Hadoop, support, and architectural innovations that add value

According to the eBook, in this instance, “Hadoop is architected with a component model down to the file system level.” This strategy allows innovators to replace one or more components while packaging the rest of the open source components and maintaining compatibility with Hadoop. MapR’s open source enterprise-grade Apache Hadoop Distribution serves as an example of this model.

Now that the Big Data debate has settled Hadoop as the de-facto implementation, more and more enterprises are turning to this framework as a key technological tool for performing mission-critical applications that drive core business operations. As such, organizations choosing a Hadoop infrastructure should exercise the same level of due diligence that they expend when choosing application servers, storage, databases and other vital assets. Becoming acquainted with each of the above distributions is essential for any enterprise looking to make a more informed decision as to which model will best meet their Big Data demands.

If you’re interested in learning how to select the right Hadoop platform for your business and best practices for successful implementations you can attend Robert’s upcoming webinar titled, Hadoop or Bust: Key Considerations for High Performance Analytics Platform and download the ebook here.

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