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Unravel Data Now Certified On MapR Converged Data Platform
February 23, 2017 News

 

Unravel Data announced today that its full-stack performance intelligence platform for optimizing Big Data operations (DataOps) has been certified with the MapR Converged Data Platform. The MapR Converged Data Platform is the industry’s only enterprise-grade software solution that unifies big data and open source technologies with fast, native access to global event streaming, real-time database capabilities, and web-scale storage.

For data teams working in a MapR environment, both on-premise and cloud, Unravel demystifies performance issues on a stack that typically hosts a lot of app engines such as MapReduce, Hive, Spark, Oozie and others. Unravel has teamed with MapR to enable data teams to more effortlessly run applications on top of the MapR Converged Data Platform. MapR provides the stack, while Unravel ensures that all of the processes and apps on the stack run optimally, and that budding issues are detected and resolved before they proliferate.

Product certification is part of the MapR Converge Partners Program and makes it easy for customers to identify partner products certified to work with MapR. Customers can use these products and solutions to lower project risks and realize cost savings over custom built solutions. With hundreds of members worldwide, the MapR Converge Partners Program includes best-of-breed partners with the shared commitment to bring the best expertise and business solution for each unique customer need.

The MapR Converged Data Platform is an enterprise-grade solution that enables organizations to use big data in powerful ways to drive innovation through digital transformation. The patented platform is the only solution engineered from the ground up to support breakthrough, next-gen applications that combine operations and analytics in real-time to impact business as it happens.

“Unravel combined with MapR enhances the performance and reliability of Big Data applications and operations by detecting inefficiencies and bottlenecks in real-time,” said Geneva Lake, vice president, worldwide alliances and channels, MapR Technologies. “Our joint customers will benefit from predictive, automated, and actionable insights for managing next gen applications on the MapR Converged Data Platform.”

Ushering in a new paradigm for DataOps
Today’s engineers all too often find themselves leveraging multiple sources, such as MapR Control Systems, CM/Ambari UI, Job History UI, Application Logs, Spark WebUI, etc. in order to gather an end-to-end understanding of their Big Data application behavior and performance challenges. Furthermore, these sources may be insufficient to truly understand the bottlenecks associated with their applications. To add to these challenges, many developers do not have access to these same sources, and find themselves going to their data operations teams, adding significant delays.

These Big Data application and operations challenges present real-roadblocks for enterprise customers aiming to make their applications production ready and meet business SLAs. Unravel addresses these broad challenges and more for Big Data applications by providing a full stack performance intelligence and self-service platform that enhances operations, makes applications reliable and improves overall cluster utilization, all from one single platform.

According to Bala Venkatrao, VP Products at Unravel Data, “The MapR Converged Data Platform presents an enterprise grade product most suitable for building innovative Big Data applications. However, on-going application management challenges and effective cluster utilization are stalling the enterprise from getting the most out of their Big Data investments,” “Unravel has applied a unique data science approach that delivers a full-stack performance management paradigm for Big Data applications and operations. The combination of MapR and Unravel greatly accelerates the time-to-value for Big Data applications, while ensuring massive ROI for the enterprise.”

This article was originally publshed on www.digitaljournal.com and can be viewed in full

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