MapR Technologies, Inc., provider of a leading distribution for Apache Hadoop, today announced a strategic partnership with Databricks and the addition of the complete Apache Spark technology stack to the MapR Distribution. The Spark in-memory processing framework provides speed, programming ease, and real-time processing advantages.
Organizations are looking for easier and faster ways to derive value from big data. Richer application development toolsets for iterative data workflows are required to expand big data applications to make more complex workflows easier to develop. The MapR Distribution for Hadoop now includes the Spark stack that supports rapid application development allowing for reuse of code across batch, interactive, and streaming applications. Spark also provides a general-purpose execution framework with in-memory pipelining to speed up end-to-end application performance.
With this release, MapR extends its position in the Hadoop market for high performance by enabling Spark applications to run on a leading distribution for Hadoop which allows streaming writes directly to the data platform. This means that customers get not only get low-latency Spark applications, but that these applications are operating on more real-time data which ultimately enables faster fraud detection, better personalization of media, higher quality from manufacturing processes, and other operational analytic use cases.
The open source community is developing tremendous technology innovations at a rapid pace,” said John Schroeder, CEO and cofounder, MapR Technologies. “MapR provides a future-proof investment for our customers with the most open distribution to give them flexibility to pick the right solution with the widest range of compute frameworks and libraries.”
Developers can quickly and easily build applications that combine batch, interactive, and stream processing and deliver faster end-to-end performance for multi-step algorithms and workflows.
With the introduction of the complete Spark stack, the MapR Distribution now includes more than 20 Apache open source projects for batch, streaming, graph, machine learning and other categories. MapR has a monthly release cadence for open source software patches that enables customers to upgrade components without upgrading the entire cluster, enabling faster adoption of open source innovation while reducing the risk of disruption to the existing cluster services.
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