About Release 7.6.1
This site contains documentation for HPE Ezmeral Data Fabric release 7.6.1, including installation, configuration, administration, and reference content, as well as content for the associated ecosystem components and drivers.
7.6.1 Installation
This section contains information about installing and upgrading HPE Ezmeral Data Fabric software. It also contains information about how to migrate data and applications from an Apache Hadoop cluster to a HPE Ezmeral Data Fabric cluster.
7.6.1 Data Fabric
HPE Ezmeral Data Fabric is the industry-leading data platform for AI and analytics that solves enterprise business needs.
7.6.1 Administration
This section describes how to manage the nodes and services that make up a cluster.
7.6.1 Development
This section contains information related to application development for Ezmeral ecosystem components and HPE Ezmeral Data Fabric products, including the file system, Database (Key-Value and JSON), and Event Streams.
- Application Development Process
  Before you start developing applications on the HPE Ezmeral Data Fabric 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.
- File Store and Apps
  The following sections provide information about accessing the File Store with C and Java applications.
- HPE Ezmeral Data Fabric Database and Apps
  This section contains information about developing client applications for JSON and key-value tables.
- Apache Kafka Wire Protocol Service
  HPE Ezmeral Data Fabric Streams supports Apache Kafka Wire Protocol Service. Apache Kafka Wire Protocol Service is a TCP/IP service that emulates a Kafka cluster backed by HPE Ezmeral Data Fabric Streams. The service makes it possible for Apache Kafka clients written in any programming language to access topics in HPE Ezmeral Data Fabric Streams.
- HPE Ezmeral Data Fabric Streams and Apps
  HPE Ezmeral Data Fabric Streams brings integrated publish and subscribe messaging to HPE Ezmeral Data Fabric.
- MapReduce and Apps
  This section contains information associated with developing YARN applications.
- Kubernetes Interfaces for Data Fabric
  This section describes how to leverage the capabilities of the Kubernetes Interfaces for Data Fabric.
- Ecosystem Components
  The following sections provide information about each open-source project that is supported by the HPE Ezmeral Data Fabric.
  - Ecosystem Packs
  - Apache Airflow
    This topic provides an overview of Apache Airflow on HPE Ezmeral Data Fabric.
  - AsyncHBase
  - Cascading
  - Apache Drill
  - Hadoop
  - HBase
  - HBase Client and HPE Ezmeral Data Fabric Database Binary Tables
  - HCatalog
  - Hive
  - HttpFS
  - Hue
  - Livy
    Apache Livy is primarily used to provide integration between Hue and Spark.
  - HPE Ezmeral Data Fabric Streams Clients and Tools
    Describes the supported HPE Ezmeral Data Fabric Streams tools and clients.
    - KSQL
      KSQL is an open-source streaming SQL engine that implements continuous, interactive queries.
    - Kafka Streams
      Kafka Streams is a programming library used for creating Java or Scala streaming applications and, specifically, building streaming applications that transform input topics into output topics.
    - Kafka REST Proxy
      The Kafka REST Proxy provides a RESTful interface to HPE Ezmeral Data Fabric Streams clusters to consume and produce messages and to perform administrative operations.
    - Kafka Connect
      Kafka Connect is a utility for streaming data between HPE Ezmeral Data Fabric Streams and other storage systems.
      - Architecture of Kafka Connect
        Kafka Connect for HPE Ezmeral Data Fabric Streams has the following major models in its design: connector, worker, and data.
      - Connectors, Tasks, and Workers
        Describes how Kafka Connect for HPE Ezmeral Data Fabric Streams works and how connectors, tasks, offsets, and workers are associated.
        Configuring in Standalone Mode
        The section describes how to configure and execute workers in Kafka connect standalone mode.
        Configuring in Distributed Mode
        This section describes how to configure and run workers in Kafka Connect distributed mode.
      - Connector Configuration
        This section describes how and where connectors are configured.
      - Worker Configuration
        This section describes how and where to configure workers.
      - JDBC Connector
        The topics describes the JDBC connector, drivers, and configuration parameters.
      - Managing Kafka Connect Services
        Lists the commands you use to start, stop, or restart Kafka Connect Services
      - HDFS Connector
        These topics describe the Kafka Connect for HPE Ezmeral Data Fabric Streams HDFS connector, driver, and configuration parameters.
      - REST API
        The Kafka Connect REST API for HPE Ezmeral Data Fabric Streams manages connectors.
      - Saving Kafka Connect Configurations
        Describes how Kafka Connect configurations are saved during an upgrade.
    - Kafka Schema Registry
      Kafka Schema Registry provides a RESTful interface for storing and retrieving schemas.
    - Structured Streaming in Spark
      Starting in EEP 5.0.0, structured streaming is supported in Spark.
  - NiFi
    This topic provides an overview of Apache NiFi on HPE Ezmeral Data Fabric.
  - OTel
    This topic provides an overview of OpenTelemetry on HPE Ezmeral Data Fabric.
  - Ranger
  - Apache Spark
  - YARN
  - Zeppelin
- Maven and the HPE Ezmeral Data Fabric
  This section discusses topics associated with Maven and the HPE Ezmeral Data Fabric.
- Developer's Reference
  This section contains in-depth information for the developer.
- API Documentation
  HPE Ezmeral Data Fabric supports public APIs for file system, HPE Ezmeral Data Fabric Database, and HPE Ezmeral Data Fabric Streams. These APIs are available for application-development purposes.
Other Docs
This section contains release-independent information, including: Installer documentation, Ecosystem release notes, interoperability matrices, security vulnerabilities, and links to other data-fabric version documentation.
Glossary
Definitions for commonly used terms in MapR Converged Data Platform environments.

Configuring in Standalone Mode

The section describes how to configure and execute workers in Kafka connect standalone mode.

Standalone mode is the simplest mode, where a single process is responsible for executing all connectors and tasks. Since it is a single process, it requires minimal configuration.

Configuring and running in standalone mode, involves configuring the standalone properties and connector parameters before executing the standalone shell command along with the properties files on the command line.

The following parameters must be provided in the connect-standalone.properties file.

offset.storage.file.filename - Storage for connector offsets which are stored on the local filesystem in standalone mode. Using the same file leads to offset data being deleted or overwritten with different values.
rest.port - Port the REST interface listens on for HTTP requests. If you run multiple standalone instances on the same host, this parameter must have different values for each instance.

NOTE If you are running multiple standalone instances on the same host, these parameters must be different for each instances. Therefore, an additional properties file is created for the instance with different parameter values.

To run a worker in standalone mode:

Edit the ./config/connect-standalone.properties file and add the name of the local file that will store the connector offsets.
Edit the quickstart-sqlite.properties file (JDBC connector configuration file).
Run the ./bin/connect-standalone.sh command along with the properties files on the command line.

For example:

cd /opt/mapr/kafka/kafka-<version>
./bin/connect-standalone.sh
./config/connect-standalone.properties
/opt/mapr/kafka-connect-jdbc/kafka-connect-jdbc-<version>/etc/kafka-connect-jdbc/quickstart-sqlite.properties

The first parameter is always a configuration file for the worker. This configuration gives you control over settings such as which cluster to use and the serialization format. See JDBC Connector for more information. All additional parameters should be connector configuration files. Each file contains a single connector configuration.