ASF GitHub Bot commented on NIFI-3709:

Github user ijokarumawak commented on the issue:

    @joewitt Added L&N. I hope I've done it correctly. Please check.
    BTW, [SmartBear has acquired Reverb 
and its copyright has been changed to [SmartBear 
nifi-assembly and nifi-framework-nar still have 'Copyright 2015 Reverb 
Technologies, Inc.' in their NOTICE file. Should we update those, too?

> Export NiFi flow dataset lineage to Apache Atlas
> ------------------------------------------------
>                 Key: NIFI-3709
>                 URL: https://issues.apache.org/jira/browse/NIFI-3709
>             Project: Apache NiFi
>          Issue Type: Improvement
>          Components: Extensions
>            Reporter: Koji Kawamura
>            Assignee: Koji Kawamura
>             Fix For: 1.2.0
> While Apache NiFi has provenance and event level lineage support within its 
> data flow, Apache Atlas also does manage lineage between dataset and process 
> those interacting with such data. 
> It would be beneficial for users who use both NiFi and Atlas and if they can 
> see end-to-end data lineage on Atlas lineage graph, as some type of dataset 
> are processed by both NiFi and technologies around Atlas such as Storm, 
> Falcon or Sqoop. For example, Kafka topics and Hive tables.
> In order to make this integration happen, I propose a NiFi reporting task 
> that analyzes NiFi flow then creates DataSet and Process entities in Atlas.
> The challenge is how to design NiFi flow dataset level lineage within Atlas 
> lineage graph.
> If we just add a single NiFi process and connect every DataSet from/to it, it 
> would be too ambiguous since it won't be clear which part of a NiFi flow 
> actually interact with certain dataset.
> But if we put every NiFi processor as independent process in Atlas, it would 
> be too granular, too. Also, we already have detailed event level lineage in 
> NiFi, we wouldn't need the same level in Atlas.
> If we can group certain processors in a NiFI flow as a process in Atlas, it 
> would be a nice granularity.

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