Pivotal HD Community and Pivotal HD Single Node (VM) Available for Download Today

July 15, 2013 SK (Saravana) Krishnamurthy

hd_iconSince the announcement of Pivotal HD & HAWQ on February 25th, we’ve been encouraged by the positive reception from customers, partners, and the market. Pivotal HD is at the core of the data fabric vision we laid out at EMC World, and we strongly believe that the market is primed for our enterprise-ready Hadoop distribution featuring HAWQ, a powerful SQL query engine. Today, We’re proud to announce that we’re making Pivotal HD available for download.

Pivotal HD is a 100% Apache-compatible Hadoop distribution featuring a fully SQL compliant query engine for processing data stored in Hadoop. By adding rich, mature SQL processing, Pivotal HD allows enterprises to simplify development, expand Hadoop’s capabilities, increase productivity, and cut costs. It has been tested for scale on the 1000 node Pivotal Analytics Workbench to ensure that the stack works flawlessly in large enterprise deployments.

Pivotal HD Community

Boasting enterprise-class features, Pivotal HD enables enterprises to truly take advantage of Hadoop’s scalability and storage capability, while at the same time leveraging tools and skillsets existing in-house. Pivotal HD provides the most stable Apache Hadoop components, as well as a number of enterprise-class components:


Pivotal HD Community users get all the components included in Pivotal HD Enterprise. Pivotal HD Enterprise is a fully supported distribution that includes 24/7 support from EMC/Pivotal. Community users can use all the components included with Enterprise with active support from community forums.

The following Apache Hadoop components are included in this release:


Pivotal HD Community also includes value added components developed by Pivotal, such as Command Center, Data Loader, USS and HVE. Pivotal HD also ships with the already popular Spring Tool Suite.

Pivotal Command Center: A visual interface to track cluster health, system metrics and job monitoring.

Data Loader: A parallel load infrastructure component that uses the MapReduce paradigm to load data into HDFS at wire speed.

Unified Storage Service (USS): An abstraction layer that allows users access to a multitude of storage systems (other HDFS, NFS shares, FTP Site, Isilon, etc) under a single namespace.

Hadoop Virtualization Extension (HVE): A Hadoop enhancement that adds support for virtual node awareness and enables greater cluster elasticity.

Spring Tool Suite (STS): The open source java development framework, which unifies data interactions (HDFS, MapReduce, Pig, Hive, etc.) for application development

Pivotal HD Community can be used to deploy up to 50 node cluster in production with no limit on the time period. Support for Pivotal HD Community is available from the Pivotal Community Forum (link). Please follow this link to download Pivotal HD Community. Please don’t forget to contact us if you desire to experiment with industry leading HAWQ, the most powerful SQL engine available for Hadoop.


In addition, a fully operational Pivotal HD Single Node (VM) is now available for customers to download. This VM contains all the components included in Pivotal HD and HAWQ with tutorials to get you started today. Running the tutorials from the comfort of your laptop users will get the flavor of Pivotal HD and HAWQ. For advanced usage, it is highly recommended to use Pivotal HD Community in a physical server or virtual environment.

Please don’t forget to register for our new webinar series “What You Can Do with Hadoop” on the first Thursday of every month. The first webinar on August 1st, 2013 will provide in-depth details about the features and tutorials included in the Pivotal HD Single Node (VM).

We are proud to bring Pivotal HD and HAWQ to the market at such an exciting time for the Big Data industry. With this groundbreaking release, Pivotal steps closer to the “consumer-grade enterprise” vision laid out by our CEO Paul Maritz, offering enterprises the capability to store, process, reason over, and act upon large amounts of data.

Useful links:

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