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Enterprise Integration of Big Data and PaaS

Dan Domkowski

By Dan Domkowski4.10.14

It would be difficult to find two areas of enterprise IT that have been more disruptive in recent years than Big Data and cloud computing. One could argue that the emergence of cloud computing, specifically IaaS, has led to the rise in enterprise adoption of Big Data solutions. The ability to scale IT resources and the emergence of pay-as-you-go services have allowed enterprise organizations to easily deploy the infrastructure necessary to run Big Data applications. In fact, it was nearly a year ago that Amazon reported that there were more than 2 trillion objects stored on S3. It’s no secret that IaaS and Big Data are tied closely together; PaaS and Big Data should be equally integrated.

Strategic-Value-PropositionOne of the key value propositions that PaaS (and specifically Apprenda) brings to the table today is enabling the Software-Defined Enterprise (SDE). Enterprises across a variety of industries have turned cloud into a reality through PaaS after realizing that the benefits include faster application development, reduced costs, and the ability to turn software into a revenue-generating business. Introducing Big Data to the Apprenda platform enables enterprises to take advantage of the many benefits that Apprenda provides for their existing application portfolio.

Summing up the challenges of implementing Big Data in the enterprise
Enterprises understand there’s a need, yet many have yet to figure out how to efficiently execute Big Data solutions in a way that their discoveries pay off. Everyone wants their own diapers and beer story, but finding the right people (devs, dedicated data scientists, BI consumers) and the right technologies (Hadoop, HBase, Cassandra) are problems that enterprise IT is finding increasingly harder to solve.

IDCIn January 2014, IDG published a Big Data enterprise survey which found that 70% of enterprise organizations are already implementing Big Data projects or are in the process of doing so. The study also found that organizations are facing challenges with Big Data initiatives that involve hiring the right people to manage the configuration, programming, and analytics associated with Big Data platforms.

How can enterprises alleviate these concerns?
The answer to this question lies in building Big Data solutions directly into an enterprise’s Private PaaS. MapReduce, for instance, is a programming model that targets large data sets often residing in the cloud. Why segregate your Hadoop MapReduce applications away from your regular enterprise app portfolio? Building Big Data applications on the same platform that you use to build your regular enterprise applications streamlines configuration processes, minimizes programming for cloud deployments and scales adoption. Thus, data analysts can easily absorb Big Data programs.

Apprenda now has the ability to deploy Hadoop Mapreduce steps to Amazon’s Elastic MapReduce service on Amazon Web Services (AWS). Enterprise Big Data projects require devs to build apps that consume and act on large data sets for their BI and analytics consumer base. With Apprenda’s new Add-On that connects Platform apps to Hadoop clusters on Amazon, devs can now deploy Hadoop apps on the same platform as their regular enterprise apps. In this way, developers and IT can take advantage of the same benefits for Big Data that Apprenda offers to all enterprise apps: built-in authentication and authorization, self-service dev / IT portals, management of cloud resources through metering, and enhanced application deployment policies.

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Dan Domkowski
Dan Domkowski

Dan Domkowski is a Product Manager at Apprenda specializing in PaaS Security. Dan is also an Adjunct Professor at the University at Albany where he teaches Information Security and Assurance. Before Apprenda, Dan spent eight years in the U.S. Intelligence Community, mostly at the National Security Agency. Dan has a MS in Computer Science from The George Washington University and a BA in International Relations and Political Science from Syracuse University.

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