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Data Catalog Application Evaluation Rubric
Evaluating potential data catalogs for https://phabricator.wikimedia.org/T293643.
Read the Data-as-a-Service Execution plan here.
|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.|
|Author||Lyft||Authored by Hortonworks, managed by Apache||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.|
- 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.
With reasons they were not more seriously considered:
|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.|
|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|