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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.

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.
- 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.

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.

The MapR Data Science Refinery offers:

Access to All Platform Assets: The MapR FUSE-based POSIX Client allows app servers, web servers, and other client nodes and apps to read and write data directly and securely to a MapR cluster, like a Linux filesystem. In addition, connectors are provided for interacting with both MapR-DB and MapR-ES via Apache Spark connectors.
Superior Security: The MapR Platform provides enhanced security. Apache Zeppelin on MapR leverages and integrates with this security layer using the built-in capabilities provided by the MapR Persistent Application Container (PACC).
Extensibility: Apache Zeppelin is paired with the Helium framework to offer pluggable visualization capabilities.
Simplified Deployment: A preconfigured Zeppelin Docker container provides the ability to leverage MapR as a persistent data store.

Getting Started Using the Data Science Refinery with Zeppelin

There are multiple ways to deploy the Apache Zeppelin Docker container included in the Data Science Refinery as shown in the following diagram:

NOTE: Starting in version 1.2, you can deploy the Data Science Refinery on a MapR cluster node. Make sure you take into consideration the resource requirements of the Data Science Refinery, if you choose this deployment mode.

If you are already familiar with Apache Zeppelin on MapR and want to skip to the deployment instructions, see Running the Zeppelin Container.

(Topic last modified: 2018-09-19)