HPE Ezmeral Data Fabric 6.1.x is In Maintenance and transitions to "End of Maintenance" in June 2024. Please see the latest documentation.

About MapR 6.1
This site contains the main documentation for Version 6.1 of the MapR Converged Data Platform, including installation, configuration, administration, and reference information.
6.1 Installation
This section contains information about installing and upgrading MapR software. It also contains information about how to migrate data and applications from an Apache Hadoop cluster to a MapR cluster.
6.1 MapR Data Platform
MapR Data Platform is the industry-leading data platform for AI and analytics that solves enterprise business needs.
6.1 Administration
This section describes how to manage the nodes and services that make up a cluster.
6.1 Development
This section contains information related to application development for Ezmeral ecosystem components and MapR Data Platform products, including the file system, Database (Key-Value and JSON), and Event Streams.
- Application Development Process
  Before you start developing applications on the MapR Data Platform platform, consider how you will get the data into the platform, the storage format of the data, the type of processing or modeling that is required, and how the data will be accessed.
  - Step 1: Select a Data Storage Format
    Consider the data format options and determine how you want to use to store your data.
  - Step 2: Write Data to MapR Data Platform
    Depending on your use case, move existing data onto the MapR Data Platform platform or write data directly to the platform.
  - Step 3: Explore Ways to Work With the Data
    Once the data is in the MapR Data Platform platform, explore the various features and components available on the platform and determine your path. You may want to access data in its initial format or perform some data modeling or processing prior to accessing the data.
  - Step 4: Set Up the Development Environment
    Before you start building the application, figure out how your the application will connect to the cluster and what the library dependencies and installation requirements are.
  - Step 5: Build the Application
    Start building an application! This section lists a few of the sample applications available in MapR Data Platform.
- MapR XD and Apps
  The following sections provide information about accessing the MapR XD with C and Java applications.
- MapR Database and Apps
  This section contains information about developing client applications for JSON and key-value tables.
- MapR Event Store For Apache Kafka and Apps
  MapR Event Store For Apache Kafka brings integrated publish and subscribe messaging to MapR Data Platform.
- MapReduce and Apps
  This section contains information associated with developing YARN applications.
- MapR Data Science Refinery
  The MapR Data Science Refinery product 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 Data Fabric for Kubernetes
  This section describes how to leverage the capabilities of the MapR Data Fabric for Kubernetes.
- Ecosystem Components
  The following sections provide information about each open-source project that is supported by the MapR Data Platform.
- 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 Data Platform supports public APIs for MapR File System, MapR Database, and MapR Event Store For Apache Kafka. These APIs are available for application-development purposes.
Other Docs
This section contains release-independent information, including: MapR Installer documentation, Ecosystem release notes, interoperability matrices, security vulnerabilities, and links to other MapR version documentation.
Glossary
Definitions for commonly used terms in MapR Converged Data Platform environments.

Application Development Process

Before you start developing applications on the MapR Data Platform platform, consider how you will get the data into the platform, the storage format of the data, the type of processing or modeling that is required, and how the data will be accessed.

At a high-level, building an application comes down to the following steps: