HPE Ezmeral Data Fabric 6.2 is In Maintenance and transitions to "End of Maintenance" in June 2024. Please see the latest documentation.

About Release 6.2
This site contains documentation for HPE Ezmeral Data Fabric release 6.2 including installation, configuration, administration, and reference content, as well as content for the associated bundled ecosystem components and drivers.
6.2 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.
- Planning the Cluster
  Describes information and factors used in planning your cluster.
  - Select Services
    This section describes some of the services that can be run on a node.
  - Cluster Design Objectives
    This section describes some of the work that your cluster performs, and identifies key design considerations.
  - Minimum Cluster Size
    All data-fabric production clusters must have a minimum of four data nodes except for HPE Ezmeral Data Fabric Edge.
  - Cluster Hardware
    Describes important hardware architecture considerations for your cluster.
  - Service Layout in a Cluster
    Provides an overview of segregating services on different nodes.
    - Node Types
      Depending on the size of your cluster, nodes may or may not perform specialized work.
    - Service Layout Guidelines for Large Clusters
      Describes how to install and segregate services on large clusters.
    - Service Layout Guidelines for Replication
      Based on the use case, replicating HPE Ezmeral Data Fabric Database tables and HPE Ezmeral Data Fabric Streams may require the installation of Data Fabric Gateways and the HBase client on one or more nodes.
    - Data Fabric Monitoring Storage Options
      Describes various storage options for Data Fabric Monitoring. The Control System relies on data-fabric monitoring components to display metrics, but can function without the monitoring components. Using data-fabric monitoring to store logs is optional.
    - Example Cluster Designs
      Describes how to design a data-fabric cluster for maximum availability, fault-tolerance, and performance.
    - Plan Initial Volumes
      Describes why it is important to define volumes.
  - Security Considerations
    Planning for security will help you identify security shortcomings and address them before you go into production.
  - User Accounts
    This section identifies how to organize authorized users of the cluster.
  - Next Step
    After you have a complete cluster plan, you are ready to prepare each node.
- Installing Core and Ecosystem Components
  Describes how to install HPE Ezmeral Data Fabric software and ecosystem components with or without the Installer.
- Installing the File Migration Service on the Edge Cluster
  Describes how to install the File Migration service on CentOS nodes.
- Installing the HPE Ezmeral Data Fabric File Store
  Describes how to install File Store software with or without the Installer.
- Installing Kubernetes Interfaces for Data Fabric
  This section describes how to plan for and install the Container Storage Interface (CSI) Storage Plugin and the Kubernetes Interfaces for Data Fabric FlexVolume Driver.
- Upgrading Core or EEP Components
  Depending on your current configuration, you may choose to upgrade the release version (core), ecosystem components, clients, or monitoring components.
- Setting Up Clients and Services
  Describes how to set up and use interfaces to an HPE Ezmeral Data Fabric cluster from a client computer.
- Setting Up the Control System
  Describes how to configure and access the Control System.
- Migrating to the HPE Ezmeral Data Fabric
  Provides instructions for migrating business-critical data and applications from an Apache Hadoop cluster to an HPE Ezmeral Data Fabric cluster.
- Applying a Patch
  You can apply a patch by using the Installer, by using the command line (a manual process), or by using an Installer Stanza.
6.2 Data Fabric
HPE Ezmeral Data Fabric is the industry-leading data platform for AI and analytics that solves enterprise business needs.
6.2 Administration
This section describes how to manage the nodes and services that make up a cluster.
6.2 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.
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.

Data Fabric Monitoring Storage Options

Describes various storage options for Data Fabric Monitoring. The Control System relies on data-fabric monitoring components to display metrics, but can function without the monitoring components. Using data-fabric monitoring to store logs is optional.

The following installation options are available for metric storage with OpenTSDB and log storage with Elasticsearch. You can store logs and metrics on a non-data-fabric cluster but this scenario is not supported by data-fabric.

Store Metrics and Logs on the Monitored Cluster

You can store metrics and logs on the nodes in the same data-fabric cluster that you want to monitor. Note that installing Grafana is optional.

Store Metrics and Logs on a Storage Cluster

You can store metrics and logs for the data-fabric cluster that you want to monitor, on nodes in a different data-fabric cluster.

In this case, Kibana and Grafana can be installed on either cluster.

Use a Single Cluster to Store Monitoring Data for Multiple Clusters

You can store metrics and logs for more than one data-fabric cluster on a shared set of nodes. With this option, a single dashboard can monitor multiple clusters.

In this case, Kibana and Grafana can be installed on any of these clusters.