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Difference between revisions of "Data Catalog Application Evaluation Rubric"

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Read the Data-as-a-Service Execution plan [https://docs.google.com/document/d/1Klr3j44VTV1oAm5e0YxZGg4DZS8VGZe3cV9zpLIc7AM/edit#heading=h.yl6ufk9yx9xk here].  
Read the Data-as-a-Service Execution plan [https://docs.google.com/document/d/1Klr3j44VTV1oAm5e0YxZGg4DZS8VGZe3cV9zpLIc7AM/edit#heading=h.yl6ufk9yx9xk here].  
Click on each header name to see the in-depth evaluation for each application as a separate article.


{| class="wikitable"
{| class="wikitable"
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!'''''Name'''''  
!'''''Name'''''  
! Amundsen !! Altas !! DataHub  
! [[Data Catalog Application Evaluation Rubric/Amundsen|Amundsen]]!! [[Data Catalog Application Evaluation Rubric/Atlas|Atlas]]!! [[Data Catalog Application Evaluation Rubric/DataHub|DataHub]]
!Egeria
![[Data Catalog Application Evaluation Rubric/Egeria|Egeria]]
!Marquez!! Mediawiki
![[Data Catalog Application Evaluation Rubric/Marquez|Marquez]]!! [[Data Catalog Application Evaluation Rubric/Mediawiki|Mediawiki]]
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|Tagline
|Tagline
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* uses [https://openlineage.io/ OpenLineage]
* uses [https://openlineage.io/ OpenLineage]
|The dogfooding approach: run our data catalong on wikitech. Mediawiki by itself could be used and manually updated, and any programmatic data access could be accomplished using mediawiki extensions.
|The dogfooding approach: run our data catalong on wikitech. Mediawiki by itself could be used and manually updated, and any programmatic data access could be accomplished using mediawiki extensions.
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|Risks
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* Dependency on other Kafka ecosystem tools like [https://datahubproject.io/docs/metadata-jobs/mae-consumer-job Schema Registry], KafkaStreams, etc.  These may not be tightly coupled but LinkedIn doesn't have any incentive to stay away from the Confluent licenses that we can't use, so at any point we could run into a problem here.
* [https://datahubproject.io/docs/rfc/active/access-control/access-control Authorization] seems to be just in the RFC phase, with no LDAP support in the first planned phase.
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Revision as of 17:51, 17 November 2021

Evaluating potential data catalogs for https://phabricator.wikimedia.org/T293643.

Read the Data-as-a-Service Execution plan here.

Click on each header name to see the in-depth evaluation for each application as a separate article.

Name Amundsen Atlas DataHub Egeria Marquez Mediawiki
Tagline Open source data discovery and metadata engine Atlas is a scalable and extensible set of core foundational governance services – enabling enterprises to effectively and efficiently meet their compliance requirements within Hadoop and allows integration with the whole enterprise data ecosystem. The Metadata Platform for the Modern Data Stack Open metadata and governance for enterprises - automatically capturing, managing and exchanging metadata between tools and platforms, no matter the vendor. An open source metadata service for the collection, aggregation, and visualization of a data ecosystem’s metadata. It maintains the provenance of how datasets are consumed and produced, provides global visibility into job runtime and frequency of dataset access, centralization of dataset lifecycle management, and much more. MediaWiki is a collaboration and documentation platform brought to you by a vibrant community.
Release Date 2019 2015 2019 2018 2018 2003
Website https://www.amundsen.io https://atlas.apache.org https://datahubproject.io https://odpi.github.io/egeria-docs/ https://marquezproject.github.io/marquez/ https://mediawiki.org
Repository https://github.com/amundsen-io/amundsen https://github.com/apache/atlas https://github.com/linkedin/datahub
Author Lyft Authored by Hortonworks, managed by Apache LinkedIn LFAI WeWork Wikimedia
License Apache 2.0 Apache 2.0 Apache 2.0 Apache 2.0 Apache 2.0
UX Very in depth UI
Robustness (criteria TBD)
Comment Sits on top of Atlas The dogfooding approach: run our data catalong on wikitech. Mediawiki by itself could be used and manually updated, and any programmatic data access could be accomplished using mediawiki extensions.
Risks
  • Dependency on other Kafka ecosystem tools like Schema Registry, KafkaStreams, etc. These may not be tightly coupled but LinkedIn doesn't have any incentive to stay away from the Confluent licenses that we can't use, so at any point we could run into a problem here.
  • Authorization seems to be just in the RFC phase, with no LDAP support in the first planned phase.

General Considerations

  • Ingestion is going to be a big deal. For the parts of our data platform that do not expose metadata in a convenient way, we need to build custom metadata ingestion. Some of the tools above make this easier than others.
  • We should carefully survey the list of connectors. Everyone says "we have a flexible connector architecture", "the community builds lots of high quality connectors", etc, but when you look closer you might find poor support for something we need. Like this lack of support for Spark in Atlas.
  • Give extra points for tight integrations like using DataHub as a Lineage backend for AirFlow.

Other Candidates

With reasons they were not more seriously considered:

Name Metacat Select Star
Tagline Metacat is a unified metadata exploration API service. You can explore Hive, RDS, Teradata, Redshift, S3 and Cassandra. Metacat provides you information about what data you have, where it resides and how to process it. Metadata in the end is really data about the data. So the primary purpose of Metacat is to give a place to describe the data so that we could do more useful things with it. Beyond a data catalog, Select Star is an intelligent data discovery platform that helps you understand your data.
Link https://github.com/Netflix/metacat https://www.selectstar.com/
Disqualifying Reasons Documentation is still in the "TODO" phase, no references to community or the kind of organization that Apache projects enjoy, and somewhat limited scope. Closed Source, useful for comparisons