SAN JOSE, Calif.--(BUSINESS WIRE)--MapR Technologies, Inc., provider of the industry’s only converged data platform, today announced it has been named a Visionary in the Gartner 2016 Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics. The MapR Converged Data Platform enables customers to leverage a real-time, reliable analytics platform for global data-driven applications.
The complete report, including the quadrant graphic, is available here
According to Gartner, “Organizations now require data management solutions for analytics that are capable of managing and processing internal and external data of diverse types in diverse formats, in combination with data from traditional internal sources. Data may even include interaction and observational data — from Internet of Things sensors, for example. This requirement is placing new demands on software in this market as customers are looking for features and functions that represent a significant augmentation of existing enterprise data warehouse strategies.”
The MapR Converged Data Platform supports the broadest set of applications and uses, including reliable and real-time applications that are featured in the MapR App Gallery. The MapR Platform integrates Hadoop and Spark, real-time database capabilities, and global event streaming with web-scale storage, for developing and running innovative data applications in a single cluster. The MapR Platform is powered by the industry’s fastest, most reliable, secure, and open data infrastructure that dramatically lowers TCO and enables global, real-time, data-driven applications.
In a recent webinar, Brad Anderson, vice president, big data informatics, Liaison Technologies commented, “Liaison selected the MapR data platform for several reasons, the first of which is it's an above and beyond enterprise grade distribution of Hadoop that gives you more production-hardened features. Business continuity and disaster recovery—are very important for our customers to be able to mirror data from one data center to the other so that they can have an active-active system. If one goes down from natural disaster, another one takes over immediately. The convergence of the building blocks that we needed-- files, streams, columnar tables, and JSON documents all in one system allows us to simplify our deployments in production.”
Details on Liaison and its MapR deployment can be found in this on-demand webinar titled, “Converged Data in Motion with Data at Rest,” featuring Gartner Research.
Addressing streaming, operational, and analytical use cases, the MapR Platform delivers a comprehensive set of technologies including SQL capabilities via Apache Drill and other SQL-on-Hadoop engines. MapR is comprised of several core components, including:
- Apache Hadoop – an open source framework for processing large volumes of data across many commodity servers, running on the utility-grade MapR Platform Services for a more powerful and protected production big data deployment
- Apache Spark – an increasingly popular open source in-memory data processing engine that enables faster application development and higher performance, with complete support from MapR for the entire stack
- MapR Streams - a global publish-subscribe event streaming system for big data
- MapR-DB - a high performance, in-Hadoop NoSQL database management system
- MapR-FS - the underlying POSIX file system that provides distributed, reliable, high performance, scalable, and full read/write web-scale data storage
“Developers are continually building new and exciting applications today. But unfortunately, many of these applications are deployed as standalone data silos,” said Steve Wooledge, vice president, product marketing, MapR Technologies. “That creates problems from a data management perspective in that someone has to stitch them together to make best use of your data. Instead, imagine having a converged system that provides interfaces for files, tables, and streams in one unified way where it's secured, it's managed, it's highly available, it's completely distributed, and you get the best-of-breed technologies that let you address your business needs. That's the future we're creating at MapR, to bring all that together in one converged data platform.”
Gartner Research Document: Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics, Roxane Edjlali and Mark A. Beyer, 25 February 2016.
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
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About MapR Technologies
MapR provides
the industry's only converged data platform that integrates the power of
Hadoop and Spark with global event streaming, real-time database
capabilities, and enterprise storage, enabling customers to harness the
enormous power of their data. Organizations with the most demanding
production needs, including sub-second response for fraud prevention,
secure and highly available data-driven insights for better healthcare,
petabyte analysis for threat detection, and integrated operational and
analytic processing for improved customer experiences, run on MapR. A
majority of customers achieves payback in fewer than 12 months and
realizes greater than 5X ROI. MapR ensures customer success through
world-class professional services and with free on-demand training that
over 50,000 developers, data analysts and administrators have used to
close the big data skills gap. Amazon, Cisco, Google, HPE, Microsoft,
SAP, and Teradata are part of the worldwide MapR partner ecosystem.
Investors include Google Capital, Lightspeed Venture Partners, Mayfield
Fund, NEA, Qualcomm Ventures and Redpoint Ventures. Connect with MapR on LinkedIn,
and Twitter.
