Hortonworks Data Platform V3.0 enhances its suite of solutions, delivering agile data management capabilities and optimized support for deep learning applications to enable faster, smarter, and hybrid data

IBM United States Software Announcement 218-419
August 14, 2018

Table of contents
OverviewOverviewTechnical informationTechnical information
Key prerequisitesKey prerequisitesOrdering informationOrdering information
Planned availability datePlanned availability dateTerms and conditionsTerms and conditions
Program numberProgram numberOrder nowOrder now

(Corrected on February 13, 2019)

Revised Software Subscription and Support applies content in the Terms and conditions section.


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Hortonworks Data Platform is a massively scalable, enterprise-ready, and 100% open source platform for storing, processing, and analyzing large volumes of data-at-rest. It is a key component of the modern data architecture and can be deployed both on-premises and on the cloud. Enhancements in Hortonworks Data Platform V3.0 are based on Apache Hadoop 3.1 and include containerization, GPU support, erasure coding, and namenode federation.

Hortonworks Data Platform V3.0 consists of the following essential set of Apache Hadoop projects:

  • Apache Accumulo 1.7.0
  • Apache Atlas 1.0.0
  • Apache Calcite 1.2.0
  • Apache DataFu 1.3.0
  • Apache Hadoop 3.1.0
  • Apache HBase 2.0.1
  • Apache Hive 3.0.0
  • Apache Kafka 1.0.1
  • Apache Knox 1.0.0
  • Apache Livy 0.5
  • Apache Oozie 4.3.1
  • Apache Phoenix 5.0.0
  • Apache Pig 0.16.0
  • Apache Ranger 1.1.0
  • Apache Solr 7 (this component is not supported as part of Hortonworks Data Platform 3.0)
  • Apache Spark 2.3.1
  • Apache Sqoop 1.4.7
  • Apache Storm 1.2.1
  • Apache TEZ 0.9.1
  • Apache Zeppelin 0.8.0
  • Apache ZooKeeper 3.4.6

Version 3.0 of Hortonworks Data Platform is available for download from Passport Advantage®.

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Key prerequisites

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For details, see the Software requirements section.

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Planned availability date

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September 4, 2018

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Hortonworks Data Platform V3.0 is faster, smarter, and hybrid. It provides users with:

  • Agile application deployment through containerization, enabling applications to be launched quickly. This saves time and resources for users and helps them to achieve faster time to market for services and increased developer productivity.
  • Support for deep learning applications, enabling users to run workloads such as machine learning and deep learning that require substantial GPU resources. GPU pooling enables the sharing of GPU resources with more workloads for cost effectiveness.
  • Optimization for the cloud, ensuring automated cloud provisioning to simplify big data deployments while optimizing the use of cloud resources.

New features in Hortonworks Data Platform V3.0 components


Core capabilities:

  • Capability to show attributes for time-bound classification or business catalog mapping (ATLAS-2457)
  • Support for business terms and categories
  • Capability to migrate Atlas data in Titan graph DB to JanusGraph DB
  • Atlas HBase hook to capture metadata and lineage
  • Capability to tag propagation from object to child object or derivative asset (ATLAS-1821)
  • Storm Atlas hook compatibility with Storm 1.2


  • Metadata security enhancement that enables authorization based on Classification or Business Catalog mapping (ATLAS-2459)



  • Kafka-Druid ingest. A Kafka topic can now be mapped to a Druid table. The events will be automatically ingested and available for querying in near real-time.


Core capabilities:

  • Capability to balance utilization of disks (varied capacities) inside a data node (HDFS-1312)
  • Capability to reduce storage overhead of HDFS with Directory Level Reed Solomon Erasure Coding Encoding (HDFS-7285)
  • Support for two Standby Name Nodes for NameNode High Availability
  • NFS Gateway needs to work in front of ViewFS (file access to the unified namespace)
  • Capability to expose encrypted zones and erasure coded zones by WebHDFS API (HDFS-11394, HDFS-13512)
  • Hive support on Erasure Coded Directories (HDFS-7285)
  • Cloud testing for HDFS; cloud failure modes and availability status
  • Cloud capability to connect or reattach to Elastic Block Volume, enabling the use of centralized block storage for better TCO (versus local disks)


Core capabilities:

  • Procedure V2. Procedure V2, or procv2, is an updated framework for executing multistep HBase administrative operations. When there is a failure, this framework can implement all of the master operations and remove the need for tools such as HBaseFsck (hbck) in the future. Procedure V2 can be used to create, modify, and delete tables. Other functions, such as new Assignment Manager, are implemented using Procedure V2.
  • Fully off-heap read/write path. When data is written into HBase through the Put operation, the cell objects do not enter JVM heap until the data is flushed to disk in an HFile. This helps to reduce total heap usage of a RegionServer, and it improves efficiency by copying less data.
  • Use of Netty for RPC layer and Async API. This replaces the previous Java™ NIO RPC server with a Netty RPC server. Netty provides the capability to easily develop an Asynchronous Java client API.
  • In-memory compactions. Periodic reorganization of the data in the Memstore can result in a reduction of overall I/O. Data is written and accessed from HDFS. The net performance increases when more data is stored in memory for a longer period of time.
  • Better dependency management. HBase now internally shades commonly-incompatible dependencies to prevent problems for downstream users. Shaded client jars can be used to reduce the burden on the existing applications.
  • Coprocessor and Observer API rewrite. Minor changes made to the API to remove ambiguous, misleading, and dangerous calls.


Core capabilities:

  • Workload management for LLAP. LLAP can now run in a multitenant environment without resource competition.
  • ACID V2 and ACID are on by default. ACID V2 has performance improvements in both storage format and execution engine, enabling equal or better performance to be achieved when compared to non-ACID tables. ACID on is enabled by default to enable full support for data updates.
  • Materialized view navigation. The Hive query engine now supports materialized view. The query engine can automatically use materialized view (when available) to speed up queries.
  • Information schema. Hive now exposes the metadata of the database (tables, columns, and so on) through the use of the Hive SQL interface directly.


  • Hive Warehouse Connector for Spark. Hive Warehouse Connector enables you to connect Spark application with Hive data warehouses. The connector automatically handles ACID tables.
  • JDBC storage connector. Any JDBC database tables can now be mapped into Hive and queried in conjunction with other tables.


Core capabilities:

  • Kafka has been upgraded from 1.0.0 to 1.0.1 with some critical bug fixes.


  • The lastEntry in TimeIndex can be cached to avoid unnecessary disk access (KAFKA-6172).
  • AbstractIndex caches index file to avoid unnecessary disk access during resizing (KAFKA-6175).
  • SSLTransportLayer continues to read from socket until either the buffer is full or the socket has no more data (KAFKA-6258).



  • Admin UI and service discovery and topology generation feature for simplifying and accelerating Knox configuration


  • SSO support for Zeppelin, YARN, MR2, HDFS, and Oozie
  • Knox Proxy support for YARN, Oozie, SHS (Spark History Server), HDFS, MR2, Livy, and SmartSense


Core capabilities:

  • Upgraded Oozie baseline from 4.2 to 4.3.1
  • Disabled Oozie Hive Action


  • Oozie support for Spark2


Core capabilities:

  • Query log. This is a new system table SYSTEM.LOG that captures information about queries that are being run against the cluster (client-driven).
  • Column encoding. This is new to Hortonworks Data Platform. The amount of space can be reduced by applying a custom encoding scheme of data in the HBase table. This increases the performance due to less data to read and thereby reduces the storage and optimizes the performance gain for the sparse tables.
  • Support for GRANT and REVOKE commands. This provides automatic changes to indexes ACLs, if access changed for data table or view.
  • Support for sampling tables.
  • Support for atomic update (on duplicate key).
  • Support for snapshot scanners for MR-based queries.


  • HBase 2.0 support.
  • Python driver for Phoenix Query Server. This Provides Python DB 2.0 API implementation.
  • Hive 3.0 support for Phoenix. This provides updated phoenix-hive StorageHandler for the new Hive version.
  • Spark 2.3 support for Phoenix. This provides updated phoenix-spark driver for new the Spark version.


  • Hardening of both of the secondary indexes, which include Local and Global



  • Time bound authorization policies
  • Hive UDF execution authorization
  • Hive workload management authorization
  • RangerKafkaAuthorizer to support new operations and resources added in Kafka 1.0
  • Read-only Ranger user roles for auditing purposes
  • Auditing for usersync operations
  • HDFS Federation support
  • Support for metadata authorization changes in Atlas 1.0
  • Capability to specify passwords for admin accounts during ranger install
  • Consolidated db schema script for all supported DB flavor

Ease of use:

  • Actual Hive queries can be displayed in Ranger Audit UI.
  • Policies can be grouped by using labels.
  • Ranger and Atlas can be installed and turned on by default in Hortonworks Data Platform V3.0.


Core capabilities:

  • Spark 2.3.1 GA on Hortonworks Data Platform V3.0
  • Structured streaming support for ORC
  • Security and ACLs can be enabled in History Server
  • Support for running Spark jobs in a Docker Container
  • Capability to upgrade Spark, Zeppelin, or Livy from Hortonworks Data Platform V2.6 to Hortonworks Data Platform V3.0
  • Cloud capability enabling Spark testing with S3Guard or S3A Committers
  • Certification for the Staging Committer with Spark
  • Capability to integrate with new Metastore Catalog feature
  • Beeline support for Spark thrift server
  • Capability to configure LLAP mode in Ambari


  • Support per notebook interpreter configuration
  • Livy to support ACLs
  • Knox to proxy Spark History Server UI
  • Structured Streaming support for Hive Streaming library
  • Transparent write to Hive warehouse
  • Spark-LLAP connector GA for Ranger


Core capabilities:

  • Storm has been upgraded from 1.1.0 to 1.2.1. Storm 1.2.1 now supports all Hortonworks Data Platform V3.0 components including Hadoop/HDFS 3.0, HBase 2.0, and Hive 3.


Core capabilities:

  • Support for intra-queue preemption for balancing between apps from different users and priorities in the same queue
  • Support for async-scheduling (versus per node-heartbeat) for better response time in a large YARN cluster
  • Support for generalized resource-placement in YARN; option for affinity or anti-affinity
  • Support for application priority scheduling in Capacity Scheduler
  • Capability to expose a framework for more powerful apps to queues mappings (YARN-8016), (YARN-3635)
  • Support for application timeout feature in YARN
  • Support for GPU scheduling/isolation on YARN
  • Support for docker containers running on YARN
  • Support for assemblies on YARN (YARN-6613)
  • YARN Service framework; Slider functionality in YARN
  • Support for a simplified services REST API on Slider or YARN
  • Simplified discovery of services through the use of DNS
  • Support for Hortonworks Data Platform on (YARN + Slider + Docker/YCloud) through CloudBreak integration
  • NodeManager support for automatic restart of service containers
  • Support for auto-spawning of administrator-configured system services
  • Capability to migrate LLAP-on-Slider to LLAP-on-YARN-Service-Framework
  • Support for dockerized Spark jobs on YARN

Ease of use:

  • Timeline Service V2
  • Capability to enable resource manager with web UI authorization control (while users can only see their own jobs)
  • Support for better classpath isolation for users; capability to remove guava conflicts from user runtime
  • Improved user-friendly and developer-friendly YARN web UI
  • ARN/MapReduce integration with SSO/Proxy (by using Knox)

Enterprise readiness:

  • CS preemption enabled by default
  • Cgroup support for YARN in a non-secure cluster, with Linux® Container Executor always on by default
  • Cgroups and CPU scheduling for YARN containers enabled by default
  • Support for deleting queues without requiring an RM restart
  • Enhancements to STOP queue handling capability
  • Support for side-by-side HDFS tarball-based install of multiple Spark auxiliary services in YARN (YARN-1151)
  • Create a log aggregation tool (HAR files) to reduce NameNode load (YARN-4086)
  • ARN queue ACL support when doAs=false
  • Capability to provide an API in YARN to get queue mapping result before application submitted


Core capabilities:

  • Updated to 0.8 release of Zeppelin
  • Capability to change Zeppelin UI across the board to not display stack traces
  • Option to select user name case conversion (ZEPPELIN-3312)

Ease of use:

  • Zeppelin UI features Knox SSO for Quicklinked Web UIs

Accessibility by people with disabilities

A US Section 508 Accessibility Compliance Report containing details on accessibility compliance can be found on the Product accessibility information website.

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Reference information

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For more information about Hortonworks Data Platform, see Software Announcement 218-187, dated March 20, 2018.

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Program number

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Program number VRM Program name
5737-H46 3.0.0 Hortonworks Data Platform

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Offering Information

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Product information is available on the IBM® Offering Information website.

More information is also available on the Passport Advantage and Passport Advantage Express® website.

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Technical documentation can be found in IBM Knowledge Center.

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Technical information

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Specified operating environment

Software requirements

Operating System Version
CentOS (64-bit) CentOS 7.2, 7.3, and 7.4

Browsers Version
Internet Explorer 10,11
Safari 10.0.1 and 10.0.3

JDK Version
Oracle JDK JDK8

Planning information


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Product: Hortonworks Data Platform (5737-H46)

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Definitions of the charge metric for this licensed product can be found in the following License Information document:

Program name PID number License Information document number
Hortonworks Data Platform 5737-H46 L-MLOY-AW5TJY

Select your language of choice and scroll down to the Charge Metrics section.

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Terms and conditions

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Program identifier License Information document title License Information document number
5737-H46 Hortonworks Data Platform V3.0 L-MLOY-AW5TJY

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