MapR's Spyglass keeps an eye on big data deployments
"This is putting some focus on the user and operator side," says Dale Kim, senior director of Product Marketing at MapR. "It provides deep visibility and full control."
MapR calls it the Spyglass Initiative — a comprehensive, open and extensible approach to centralized monitoring of big data deployments.
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Kim says the first phase of the Spyglass Initiative will be included in the upcoming release of the MapR Converged Data Platform. The new features include the following:
MapR also moved to simplify planning and maintaining big data deployments with the MapR Ecosystem Pack (MEP), a program through which it decouples its platform releases from open source project releases and certifies Hadoop ecosystem components for interoperability.
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"This is about making sure our customers have quick access to the latest projects," Kim says. "you can have the latest version of Spark and Drill, but also the right versions of other projects to ensure interoperability. We do extensive testing so customers don't need to figure out which versions of projects work with other projects."
MapR has already been giving customers monthly updates to new project versions. Kim says MEP is the next evolution of that program, providing full certification on cross-project interoperability. With MEP, MapR will release certified packs of open source projects on a quarterly basis. It will also continue delivering updates on a monthly basis to address open source project bug fixes.
"When we have a major platform release, we'll release a major MEP release along with it," Kim says. "We're giving [customers] this certified and blessed package of what projects work together. The end result is it will make it a lot easier for customers to decide what to upgrade and when to upgrade."
The new MapR Platform will also include performance and JSON enhancements to MapR-DB. Advanced multi-master JSON replication provides mission-critical disaster recovery for JSON documents and a global view of enterprise-wide data in local deployments.