M7 Enterprise Database Edition for Hadoop
Realize the full power of Hadoop: MapR M7 provides a complete distribution for Apache Hadoop with the best in-Hadoop database to reliably run both online and analytical processing on one platform.
MapR CEO, John Schroeder, talks about M7, the Enteprise Database Edition for Hadoop
Unlike other distributions for Hadoop, the MapR M7 architecture is optimized for deployments that depend on high throughput, low latency, high reliability, and no additional administration to ensure production success and significantly lower enterprise data architecture costs.
Unified Big Data Platform
Create a complete picture of all data, including high-velocity, real-time data, to find previously unidentifiable insights. Process more data types with the schemaless flexibility and the high-velocity read/write capabilities of the integrated in-Hadoop online database platform. Ensure optimal resource coordination between online and analytical jobs with features like ExpressLane, data placement, and job placement control. And leverage the innovation and the expertise of the large Hadoop and HBase communities to further complement the technologies in your big data platform.
Proven Production Readiness
Get continuous value from your data with the technology proven in production to meet strict service level agreements. Deploy 24x7 online applications with enterprise-grade capabilities to achieve zero downtime. Run HBase-compatible applications with zero database administration. And use the comprehensive integrated security, including Kerberos integration and built-in native authentication, to meet your stringent security requirements.
Consistent High Performance at Any Scale
Get faster results on larger data sets to respond more quickly to more complete data. Achieve quicker application responsiveness for an enhanced user experience. Easily load and process high volumes and high velocities of incoming data. Get low 95th and 99th percentile latencies to ensure consistent performance without bottlenecks due to compactions/defragmentation. And get extreme database scalability with millions of columns across billions of rows on one trillion tables.