MapR Spark

Here’s some information about MapR’s integration with Apache Spark:

1. MapR’s Distribution: MapR’s Hadoop distribution included a fully integrated version of Apache Spark. This integration allowed users to run Spark on the MapR platform seamlessly.

2. Benefits of MapR with Spark:

   • High Performance: MapR’s platform was designed to provide high-performance storage, which complemented Spark’s in-memory processing capabilities.
   • Scalability: Users could scale both storage and processing resources independently to meet their big data needs.
   • Multi-Model Capabilities: MapR also offered support for various data models, including NoSQL databases and message streaming, which could be integrated with Spark for comprehensive data processing.

3. Integration with Hadoop Ecosystem: MapR’s distribution included not only Spark but also other components of the Hadoop ecosystem, such as HDFS (Hadoop Distributed File System), Hive, Pig, and more.

4. Security and Management: MapR provided features for security, management, and monitoring of Spark jobs and clusters. This included auditing, access control, and centralized cluster management.

5. MapR-FS: MapR used its proprietary file system called MapR-FS, which was fully integrated with Hadoop and Spark. It offered additional features such as POSIX compatibility and data snapshots.

6. Event Streaming: MapR’s platform included Apache Kafka for event streaming, which could be integrated with Spark for real-time data processing and analytics.