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MapR-DB for Rich Data-Intensive Applications and In-Place Analytics

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MapR-DB for Rich Data-Intensive Applications and In-Place Analytics

New innovations bring together operational, analytical, and real-time applications at global scale. Learn how MapR-DB is contributing to this realm of possibility.

· Big Data Zone ·
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Thanks to Jack Norris, Senior Vice President of Data, and Neeraja Rentachintala, Senior Director of Product Management at MapR Technologies, Inc. for sharing their database innovations for data-intensive applications. The new advancements for developers enable rich applications, in-place and continuous machine learning/AI and SQL capabilities, and global real-time data integration and microservices support. This harnesses analytics to ingest applications with intelligence to mitigate risk.

MapR-DB enables faster time-to-market, always-on intelligent business processes and contextual user experiences. MapR-DB supports multiple data models (document, wide column, key value, etc.) and unifies operations and analytics for in-place and continuous real-time business insights.  MapR-DB can operate thousands of mission-critical next-gen applications with consistent ultra-low latencies and effortless scale.

“Retro-fitting legacy databases in today’s big data environments present challenges to organizations such as poor data performance, scale limitations, and data access restrictions,” says John L Myers, managing research director, Enterprise Management Associates, a Boulder, Colorado, based industry analyst firm. “Leveraging a one platform architecture, MapR Converged Data Platform enables organizations to efficiently process all data types bringing analytics and operations together with consistent scale, reliability and performance.  Specifically, MapR-DB capabilities help companies build a global multi-master database extending from the cloud to the edge and on-premises, accelerating data iterations and integrations for deploying next-generation online and mobile apps.”  

Bringing Together Operational, Analytical, and Real-Time Applications

New database features in MapR-DB include:

  •  Powerful data access with native secondary indexes

  • Rich application development with OJAI 2.0 APIs and smart query execution

  • In-place self-service SQL data exploration and operational BI with optimized Drill integration

  • Real-time processing and machine learning with native Spark and Hive connectivity

  • Real-time application integration with global change data capture 

“Xactly’s SPM Suite, predictive analytics, and big data intelligence are the foundation for the sales programs of more than a 1,200 customers. Billions of transactions flow through our systems each quarter and we have handled more than $20 billion in incentive compensation payments in the last two years alone,” says Ron Rasmussen, CTO, Xactly Corporation. “If our systems aren’t running at maximum performance, the course of business slows and people don’t get paid. MapR understands that our database architecture is critical to our success and that of our customers. Their system provides the flexibility and integrated analytics required for Xactly to continue to build and scale SPM solutions that are unmatched in performance, scalability, and uptime.”

Anoop Dawar, Vice President, Product Marketing and Product mManagement, MapR Technologies, says:

“The power and flexibility in our new release bring together the best of RDBMS and NoSQL ushering a new era of intelligent data-intensive business applications. The architectural advantages in the MapR Converged Data Platform continue to showcase our forward-thinking. There are no tradeoffs when it comes to scale, performance, and reliability.  Because MapR-DB is part of one platform, large amounts of data can be analyzed immediately regardless of type or source.”

Managing data at scale doesn’t have to be hard. Find out how the completely free, open source HPCC Systems platform makes it easier to update, easier to program, easier to integrate data, and easier to manage clusters. Download and get started today.

big data ,mapr ,data analytics ,real-time data

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