MEP 3.0 : A New Version of MapR Ecosystem Pack released

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31st May, 2022
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18th Apr, 2017
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MEP 3.0 : A New Version of MapR Ecosystem Pack released

On 10th April 2017, MapR Technologies Inc. introduced a new version of their Ecosystem pack called MEP 3.0. Along with Spark connectors for HBase and MapR-D, they feature intensified security for Apache Spark.

MapR Ecosystem Pack (MEP) allows the developer to upgrade their open source ecosystem stack individually from the converged Data Platform. In future, with the quarterly updates, the new MEP 3.0  will help developers to be updated in a similar manner as projects like Apache Drill and Apache Spark. Spark is widely used Big Data Processing Engine while  Drill is an open-source software framework that supports data-intensive SQL query engine, allowing  MapR to explore Big Data.

“The adoption of Spark and Drill continues to advance at a fast pace with enterprises worldwide,” said MapR exec Will Ochandarena in a news release yesterday. “With a regular cadence of ecosystem updates that make it easier to adopt for production use, our customers immediately benefit from rapid open source innovation with the reliability, scale and performance of the Converged Data Platform.”

In the blog post, MapR’s Rachel Silver explained  “This Native Spark Connector for MapR-DB JSON is a new API that makes it easier to build real-time or batch pipelines between your data and MapR-DB and to leverage Spark or Spark Streaming within the pipeline,” she said. “Compared to other connectors for MapR-DB — such as the JDBC connector — the Native Spark Connector is more efficient, and the code is simpler to write.”

MEP 3.0 supports Spark 2.1.0 which basically focuses on Security and Stability. This new version includes many updates like:

  • Apache Spark 2.1.0 –  Improvement in Stability and security.
  • Apache Drill 1.10 – Improvement in the BI tool Integration, performance, and end-to-end security.
  • Apache Hive 2.1.1 – Improvement in Querying and Data Processing.
  • MapR Streams – New APIs for Python application and C language
  • Native Spark Connector for MapR-DB JSON – High integration with greater efficiency for MapR-DB.
  • MapR Installer – Have included features to simplify add-ons and upgrades.
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