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60 lines
3.3 KiB
Markdown
60 lines
3.3 KiB
Markdown
# DataHub Features
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DataHub is made up of a [generic backend](what/gma.md) and a [Ember-based UI](../datahub-web). Original DataHub
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[blog post](https://engineering.linkedin.com/blog/2019/data-hub) talks about the design extensively and mentions some of
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the features of DataHub. Our open sourcing [blog post](https://engineering.linkedin.com/blog/2020/open-sourcing-datahub--linkedins-metadata-search-and-discovery-p)
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also provides a comparison of some features between LinkedIn production DataHub vs open source DataHub. Below is a list of the latest features that are available in DataHub, as well as ones that will soon become available.
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## Data Constructs (Entities)
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### Datasets
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- **Search**: full-text & advanced search, search ranking
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- **Browse**: browsing through a configurable hierarchy
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- **Schema**: table & document schema in tabular and JSON format
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- **Coarse grain lineage**: support for lineage at the dataset level, tabular & graphical visualization of downstreams/upstreams
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- **Ownership**: surfacing owners of a dataset, viewing datasets you own
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- **Dataset life-cycle management**: deprecate/undeprecate, surface removed datasets and tag it with "removed"
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- **Institutional knowledge**: support for adding free form doc to any dataset
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- **Fine grain lineage**: support for lineage at the field level [*coming soon*]
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- **Social actions**: likes, follows, bookmarks [*coming soon*]
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- **Compliance management**: field level tag based compliance editing [*coming soon*]
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- **Top users**: frequent users of a dataset [*coming soon*]
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### Users
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- **Search**: full-text & advanced search, search ranking
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- **Browse**: browsing through a configurable hierarchy [*coming soon*]
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- **Profile editing**: LinkedIn style professional profile editing such as summary, skills
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### Schemas [*coming soon*]
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- **Search**: full-text & advanced search, search ranking
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- **Browse**: browsing through a configurable hierarchy
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- **Schema history**: view and diff historic versions of schemas
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- **GraphQL**: visualization of GraphQL schemas
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### Jos/flows [*coming soon*]
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- **Search**: full-text & advanced search, search ranking
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- **Browse**: browsing through a configurable hierarchy
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- **Basic information**:
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- **Execution history**: Executions and their status. Link to external service for viewing full info.
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### Metrics [*coming soon*]
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- **search**: full-text & advanced search, search ranking
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- **Browse**: browsing through a configurable hierarchy
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- **Basic information**: ownershp, dimensions, formula, input & output datasets, dashboards
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- **Institutional knowledge**: support for adding free form doc to any metric
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### Dashboards [*coming soon*]
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- **search**: full-text & advanced search, search ranking
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- **Basic information**: ownership, location. Link to exzternal service for viewing the dashboard.
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- **Institutional knowledge**: support for adding free form doc to any dashboards
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## Metadata Sources
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You can integrate any data platform to DataHub easily. As long as you have a way of *Extracting* metadata from the platform and *Transform* that into our standard [MCE](what/mxe.md) format, you're free to *Load*/ingest metadata to DataHub from any available platform.
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We have provided example [ETL ingestion](architecture/metadata-ingestion.md) scripts for:
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- Hive
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- Kafka
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- RDBMS (MySQL, Oracle, Postgres, MS SQL etc)
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- Data warehouse (Snowflake, BigQuery etc)
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- LDAP
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