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6.0 Development
This section contains information related to application development for ecosystem components and MapR products including MapR-DB (binary and JSON), MapR-FS, and MapR Streams.
MapR Data Science Refinery
The MapR Data Science Refinery is an easy-to-deploy and scalable data science toolkit with native access to all platform assets and superior out-of-the-box security.
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.

MapR 6.0 Documentation

6.0 Development
This section contains information related to application development for ecosystem components and MapR products including MapR-DB (binary and JSON), MapR-FS, and MapR Streams.
- Application Development Process
  Before you start developing applications on MapR’s Converged Data Platform, consider how you will get the data onto the platform, the format it will be stored in, the type of processing or modeling that is required, and how the data will be accessed.
- MapR-FS and Apps
  The following sections provide information about accessing MapR-FS with C and Java applications.
- MapR-DB and Apps
  This section contains information about developing client applications for JSON and binary tables.
- MapR-ES and Apps
  MapR-ES brings integrated publish and subscribe messaging to the MapR Converged Data Platform.
- MapReduce and Apps
  This section contains information associated with developing YARN applications.
- MapR Data Science Refinery
  The MapR Data Science Refinery is an easy-to-deploy and scalable data science toolkit with native access to all platform assets and superior out-of-the-box security.
  - 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.
    - Running the Zeppelin Container
      To run the Apache Zeppelin container, you must access the Zeppelin Docker image from MapR’s public repository, run the Docker image, and access the deployed container from your web browser. From your browser, you can create Zeppelin notebooks.
    - Understanding Zeppelin Interpreters
      Apache Zeppelin interpreters enable you to access specific languages and data processing backends. This section describes the interpreters you can use with MapR and the use cases they serve.
    - Configuring Zeppelin Interpreters
      Out-of-box, the interpreters in Apache Zeppelin on MapR are preconfigured to run against different backend engines. You may need to perform manual steps to configure the Livy, Spark, and JDBC interpreters. No additional steps are needed to configure and run the Pig and Shell interpreters.
    - Troubleshooting Zeppelin
      This section describes how to resolve common problems you may encounter when using Apache Zeppelin.
    - Using Visualization Packages in Zeppelin
      Apache Zeppelin includes the Helium framework. Helium allows you to register visualization packages with Zeppelin. Using visualization packages, you can view your data through area charts, bar charts, scatter charts, and other displays. To use a visualization package, you must download it, register it with Zeppelin, and enable it through Helium. Like Zeppelin interpreters, Helium is automatically installed in your Zeppelin container.
    - Using Zeppelin to Access Different Backend Engines
      This section contains examples of how to use Apache Zeppelin interpreters to access the different backend engines. This includes running Apache Pig scripts, Apache Drill queries, Apache Hive queries, and Apache Spark jobs, as well as accessing MapR-DB and MapR-ES.
    - Sharing Zeppelin Notebook Content
      By default, Zeppelin stores notebooks in the local file system in your container. An alternative is to store them in MapR-FS. This allows you to share the notebooks with other users.
- MapR Data Fabric for Kubernetes FlexVolume Driver
  This section describes how to use and troubleshoot the MapR Data Fabric for Kubernetes FlexVolume Driver.
- Ecosystem Components
  The following sections provide information about each open source project that MapR supports.
- Maven and MapR
  This section discusses topics associated with Maven and MapR.
- Developer's Reference
  This section contains in-depth information for the developer.
- API Documentation
  MapR supports public APIs for MapR-FS, MapR-DB, and MapR-ES. These APIs are available for application development purposes.

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.

(Topic last modified: 2018-10-15)