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Maximize the Value of Data-at-Rest to Deliver Big Data Analytics

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Hortonworks Data Platform (HDP®)

Hortonworks Data Platform (HDP®)

HDP is the industry's only true secure, enterprise-ready open source Apache™ Hadoop® distribution based on a centralized architecture (YARN). HDP addresses the complete needs of data-at-rest, powers real-time customer applications and delivers robust big data analytics that accelerate decision making and innovation.

Powering the Future of Data


Powerful Insights Whether in the Data Center or in the Cloud

Flexible, Efficient and Portable Data Insights across environments.

Hortonworks provides you with the flexibility to run the same industry-leading, open source platform to gain data insights in the data center as well as on the public cloud of your choice. Whether it is Microsoft Azure, Amazon Web Services or Google Cloud Platform, your organization can harness the agility of the cloud, while at the same time run your on-premise workloads to become a hybrid, data-driven enterprise. With our enterprise ready modern data architecture, customers will have a consistent experience across both the data center and the cloud.


Data Lifecycle & Governance

Data Workflow


Provisioning, Managing, & Monitoring



Administration Authentication Authorization Auditing Data Protection


Hadoop Distributed File System


HDP Downloads

Try out the latest HDP features and functionality and set HDP up for a production environment.

HDP Add-Ons

Check out HDP add-ons for connecting with popular BI tools.

HDP Platform Security

Check out the process that we have put in place to secure HDP.

Cornerstone of Hortonworks Data Platform

YARN and Hadoop Distributed File System (HDFS) are the cornerstone components of Hortonworks Data Platform (HDP) for data-at-rest. While HDFS provides the scalable, fault-tolerant, cost-efficient storage for your big data lake, YARN provides the centralized architecture that enables you to process multiple workloads simultaneously. YARN provides the resource management and pluggable architecture for enabling a wide variety of data access methods.

More Info:

Data Management

Data streaming, processing and analytics engines for a variety of workloads

Hortonworks Data Platform includes a versatile range of processing engines that empower you to interact with the same data in multiple ways, at the same time. This means applications for big data analytics can interact with the data in the best way: from batch to interactive SQL or low latency access with NoSQL. Emerging use cases for data science, search and streaming are also supported with Apache Spark, Storm and Kafka.

Data Access

Load and manage data according to policy

HDP extends data access and management with powerful tools for data governance and integration. They provide a reliable, repeatable, and simple framework for managing the flow of data in and out of Hadoop. This control structure, along with a set of tooling to ease and automate the application of schema or metadata on sources is critical for successful integration of Hadoop into your modern data architecture.

Hortonworks has engineering relationships with many leading data management providers to enable their tools to work and integrate with HDP.

Data Governance & Integration

Authentication, authorization, and data protection

Security is woven and integrated into HDP in multiple layers. Critical features for authentication, authorization, accountability and data protection are in place to help secure HDP across these key requirements. Consistent with this approach throughout all of the enterprise Hadoop capabilities, HDP also ensures you can integrate and extend your current security solutions to provide a single, consistent, secure umbrella over your modern data architecture.

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Take the guesswork out of operating Hadoop

Operations teams deploy, monitor and manage a Hadoop cluster within their broader enterprise data ecosystem. Apache Ambari simplifies this experience. Ambari is an open source management platform for provisioning, managing, monitoring, and securing the Hortonworks Data Platform. It enables Hadoop to fit seamlessly into your enterprise environment.

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Provision and manage Hadoop clusters in any cloud environment

Cloudbreak, as part of Hortonworks Data Platform and powered by Apache Ambari, allows simplified provisioning and Hadoop cluster management in any cloud environment including; Amazon Web Services, Microsoft Azure, Google Cloud Platform and OpenStack. It optimizes your use of cloud resources as workloads change.

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What's New in Hortonworks Data Platform 2.6


Innovation & Performance

  • Access to Latest Data Science Functionality. Extensive support for machine learning algorithms available in Spark 2.1, Spark 1.6.3, Zeppelin 0.7 and Livy REST API
  • Hive LLAP for Production. Gain 10x faster join performance with dynamic runtime filtering
  • ACID Compliance. Greatly speed up and enable micro-batch/ streaming changes to Hive data warehouse through incremental updates
  • Sub-second Query Performance for BI tools. Customers no longer need to replicate data in Hadoop by first storing it in a SQL-based analytic database


Enterprise Ready

  • Export/ Import of Ranger Security Policies. Enhance productivity by moving security policies in bulk from one environment to another
  • Extend Atlas Tag-based Policy Support Across the Ecosystem. Enable classification based security workflows coverage for HDFS, Kafka and HBase
  • Row / Column Security. Implement granular data access control at every level of the Hadoop stack including Spark and Hive
  • SSL Support for Spark Streaming Connections to Kafka. Provide secure environments for Spark Streaming & Kafka


Ease of Use

  • Service Auto Start. Easily configure the services and components that should be automatically started if a cluster node restarts, or if the daemon exits unexpectedly
  • Simplified Log Rotation Configuration. Quickly configure the number and size of backup files for all components
  • HDFS TopN User & Operation Visualization. Gain visibility into the most frequent operations being performed on the NameNode, and who’s performing those operations
  • Package support for PySpark (Spark Python API) & SparkR: Data scientists using Spark with R language can now deploy their favorite R package with their Spark job