The Leading Standards-Based Hadoop Distribution For Advanced Analytics

100% Apache compliant Hadoop Distribution based on ODPi core and fully leveraging the ecosystem of related Apache components

  • Benefits
  • Features
  • Use Cases

Build the Foundation for an Enterprise-Grade Business Data Lake

Hadoop eliminates data silos and provides businesses with a more holistic view of their customers and operations, leading to quicker and more effective business insights. Pivotal HD is an enterprise-ready, Hadoop distribution that ensures you can harness the massive data being driven by new apps, systems, machines and the torrent of customer sources.

Realize Business Value from Large-Scale Data Analytics

Hadoop has become an economic way to leverage the huge amounts of data produced by new sources – such as social media, mobile sensors and Internet of Things (IoT) devices. Pivotal HD is a commercially-supported Hadoop distribution that delivers exponential business value when combined with Pivotal Big Data Suite.

Rapidly Innovate on Industry-Standardized Hadoop

ODPi reduces fragmentation in the Hadoop ecosystem, accelerates the development, testing and certification of innovative tools and applications over Hadoop and makes them more transportable across Hadoop distributions that are based on a common, industry-standardized ODP core.


Enterprise-ready Hadoop distribution, focused on advanced analytics, built on a common ODPi core
and a foundational component of Pivotal Big Data Suite.

Compliant with Apache and Related Components

Pivotal HD is 100% Apache Hadoop compliant and supports all Hadoop Distributed File System (HDFS) file formats. Pivotal HD incorporates the common, industry-standardized ODP core containing components such as HDFS, Yarn and Ambari. Other standard Hadoop components for scripting, non-relational database, workflow orchestration, security, monitoring and data processing (including Spark) are included as well.

Multiple Deployment Models

Pivotal HD supports multiple deployment models to suite the enterprise needs around security, policy, operational and budgetary constraints. As part of the Big Data Suite, Pivotal HD can be deployed on commodity hardware nodes, in an appliance form factor, public cloud, and in virtualized IaaS environments with truly elastic scalability.

Optimized for Advanced Analytics

Pivotal HD integrates seamlessly with value added analytics stack components. Pivotal HD provides out-of-the-box integration with Pivotal HDB®, an MPP-based native analytic SQL engine that provides analytical processing of structured data in Hadoop.

Use Cases

Business Data Lake

Enterprises ingest structured and unstructured data generated from IT and line-of-business processes and Internet of Things. This data resides on HDFS in Pivotal HD. Enterprises that have terabytes of structured and unstructured data can significantly benefit from a business data lake.

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Large-Scale ETL

Enterprises often implement massively-parallel extract, transform and load (ETL) processing using MapReduce jobs. This is usually done to convert unstructured or semi-structured data into a more structured representation for storage in Pivotal HD. Once stored, this data is further analyzed using SQL on Hadoop and parallel machine learning tools executing on Pivotal HD.

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Batch-Mode Processing and Interactive SQL Queries

Most enterprises use Hive for batch-mode analytic workloads over structured and semi-structured data in Pivotal HD. For interactive SQL queries, enterprises often use SQL on Hadoop engines designed to work on data in-place, in Pivotal HD.

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Large-Scale, Parallel Machine Learning

Enterprises are building advanced analytics models to address use cases – ranging from fraud and outlier detection, to customer sentiment analytics, to predictive inventory management and component failure. Often, they use parallel machine learning capabilities embedded in SQL on Hadoop engines executing in Pivotal HD.

Read the example

Case Study

Supporting Rapid Launch of Game-Changing Market Intelligence Service for Retailers with Hadoop

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