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How Microservices Contribute to Agile Application Development


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MapR Technologies recently announced support for event-driven microservices in the MapR Converged Data Platform. The goal, the company says, is to leverage continuous analytics, automated actions, and faster response to impact business as it happens. 

At Strata + Hadoop World in NYC, Jack Norris, SVP at MapR, expanded on how MapR’s support for microservices  enables more agile applications.

Observing that, traditionally, analytic applications and operational applications are separated, Norris said that the customers getting the most significant results from analytics are taking advantage of the intersection of analysis and operations and incorporating automated adjustments into their process to determine, for example, during a credit card wipe, if it is fraudulent or not, or, during a web page load, the right product to put in front of the customer. “We have seen this cross-industry with different aspects for revenue, cost reduction, or for risk mitigation.”

The new capabilities in the MapR Converged Data Platform range from microservices application monitoring and management to integrated support for agile microservices application development.

“There is this Converged Data Platform - this message fabric - that can coordinate the microservices, including bidirectional publish and subscribe, with the shared data layer underneath so it really frees up the administrator and developer to do applications that they hadn’t thought of before,” said Norris. “That is the real significance of this release. It makes the applications they are doing easier, but it also allows them to do things they couldn’t before.”

Looking at the horizon 4 years out, budgets are projected to be flat for IT “but underneath that is a steady decrease in legacy spend and a corresponding increase in next-gen technologies,” said Norris, who also cited predictions that 4 years from now, 90% of the data will be on next-gen technology, including big data technologies, NoSQL, and cloud.

“At least from the data side, it is pretty clear. The leverage point to be able to do those simultaneously is the data layer. If you focus on freeing up the data and making things more agile you can decrease cost because you are going from things that are $10,000, $40,000 per terabyte, or even more, to a few hundred dollars a terabyte - so the factor is orders of magnitude.”

For additional information on the platform update, read a new blog post by MapR’s Rachel Silver.


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