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December 12, 2016
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Announcing the Availability of Hortonworks DataFlow (HDF) 2.1

by
Wei Wang & Haimo Liu

We are pleased to announce that Hortonworks DataFlow (HDFTM) Version 2.1 is now generally available. You can download the latest version here!  

HDF 2.1 (powered by  Apache NiFi, Apache Kafka and Apache Storm) brings enterprise readiness, platform stability and ease of use to the next level.

  • Apache NiFi for dynamic, configurable data pipelines, through which all sources, systems and destinations communicate.
  • Apache Kafka for high throughput distributed messaging with pub sub semantics to operate at speed on big data volumes that adapt to differing rates of data creation and delivery
  • Apache Storm for real-time streaming analytics to create immediate insights at  massive scale

Here are the 3 top features we would like to highlight within HDF 2.1

1.Extend HDF support in the cloud

In the release of HDF 2.1, users can now interact with AWS Kinesis, Microsoft Azure WASB (Windows Azure Storage Blob) and ADLS (Azure Data Lake Storage). This significantly extends user’s flow management capability in the cloud.

2. Ease of Use Enhancement

In the release of HDF 2.1, users now can further simplify its policy management task with a user centric policy view, further enhancing across enterprise collaborations in a multi-tenancy manner. Please see the following graph as an example:

screen-shot-2016-12-11-at-12-47-41-pm

3. New policy to restrict processor (potentially dangerous) access

screen-shot-2016-12-11-at-12-48-42-pm

In the release of HDF 2.1, data flow administrators within the enterprise can identify that in order for certain potential processors to be added to a working data flow system, additional authorization would be required.  

In addition, HDF 2.1 supports over 180 processors including newly introduced Connect/Listen/PutWebSocket, Put/FetchElasticsearch5, ValidateCsv, etc.

We would like to thank Apache NiFi, Kafka and Storm community to help us make this a very successful release.

To learn more:

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