Zeppelin on MapR

The MapR Data Science Refinery includes a preconfigured Apache Zeppelin notebook, packaged as a Docker container. Apache Zeppelin is an open source web-based data science notebook. You can use it with MapR components to conduct data discovery, ETL, machine learning, and data visualization.

You can run the Zeppelin container either on your laptop or on MapR edge nodes. Out of box, the Zeppelin container image is integrated with open source data processing engines like Apache Spark, Apache Drill, and Apache Hive, as well as with native MapR engines (MapR-FS, MapR-DB, and MapR-ES). Using the notebook simply requires running the Docker image and connecting to the container through your browser.

Zeppelin provides the following benefits for your data engineering and data science use cases:

  • An interactive development environment for writing, testing, and sharing data processing code snippets
  • The ability to run the notebooks in a local client environment, such as on a laptop
  • Support for a variety of interpreters for integrating with different backend components
  • Support for extensible visualization libraries

The Zeppelin notebook included with the Data Science Refinery provides additional benefits:

  • A small footprint, pre-built, certified data science container that is easy to deploy and run
  • An isolated environment where you can experiment with libraries and packages without affecting other users' work
  • Secure authentication at the container level across a secure Web connection
  • Preconfigured JDBC interpreters for accessing query engines like Apache Drill and Apache Hive
  • The MapR FUSE-Based POSIX Client, which you need to access MapR-FS using shell commands
  • All client side services that you need to submit Apache Spark jobs, including jobs that access MapR-ES
  • MapR connectors, which you need to access MapR-DB (both binary and JSON tables)

See Zeppelin Release Notes for release specific information.

For additional information about Zeppelin, you can also refer to the open source documentation.