mirror of
https://github.com/datahub-project/datahub.git
synced 2025-10-31 02:37:05 +00:00
60 lines
3.3 KiB
Markdown
60 lines
3.3 KiB
Markdown
# DataHub Features
|
|
|
|
DataHub is made up of a [generic backend](what/gma.md) and a [Ember-based UI](../datahub-web). Original DataHub
|
|
[blog post](https://engineering.linkedin.com/blog/2019/data-hub) talks about the design extensively and mentions some of
|
|
the features of DataHub. Our open sourcing [blog post](https://engineering.linkedin.com/blog/2020/open-sourcing-datahub--linkedins-metadata-search-and-discovery-p)
|
|
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.
|
|
|
|
## Data Constructs (Entities)
|
|
|
|
### Datasets
|
|
- **Search**: full-text & advanced search, search ranking
|
|
- **Browse**: browsing through a configurable hierarchy
|
|
- **Schema**: table & document schema in tabular and JSON format
|
|
- **Coarse grain lineage**: support for lineage at the dataset level, tabular & graphical visualization of downstreams/upstreams
|
|
- **Ownership**: surfacing owners of a dataset, viewing datasets you own
|
|
- **Dataset life-cycle management**: deprecate/undeprecate, surface removed datasets and tag it with "removed"
|
|
- **Institutional knowledge**: support for adding free form doc to any dataset
|
|
- **Fine grain lineage**: support for lineage at the field level [*coming soon*]
|
|
- **Social actions**: likes, follows, bookmarks [*coming soon*]
|
|
- **Compliance management**: field level tag based compliance editing [*coming soon*]
|
|
- **Top users**: frequent users of a dataset [*coming soon*]
|
|
|
|
### Users
|
|
- **Search**: full-text & advanced search, search ranking
|
|
- **Browse**: browsing through a configurable hierarchy [*coming soon*]
|
|
- **Profile editing**: LinkedIn style professional profile editing such as summary, skills
|
|
|
|
### Schemas [*coming soon*]
|
|
- **Search**: full-text & advanced search, search ranking
|
|
- **Browse**: browsing through a configurable hierarchy
|
|
- **Schema history**: view and diff historic versions of schemas
|
|
- **GraphQL**: visualization of GraphQL schemas
|
|
|
|
### Jos/flows [*coming soon*]
|
|
- **Search**: full-text & advanced search, search ranking
|
|
- **Browse**: browsing through a configurable hierarchy
|
|
- **Basic information**:
|
|
- **Execution history**: Executions and their status. Link to external service for viewing full info.
|
|
|
|
### Metrics [*coming soon*]
|
|
- **search**: full-text & advanced search, search ranking
|
|
- **Browse**: browsing through a configurable hierarchy
|
|
- **Basic information**: ownershp, dimensions, formula, input & output datasets, dashboards
|
|
- **Institutional knowledge**: support for adding free form doc to any metric
|
|
|
|
### Dashboards [*coming soon*]
|
|
- **search**: full-text & advanced search, search ranking
|
|
- **Basic information**: ownership, location. Link to exzternal service for viewing the dashboard.
|
|
- **Institutional knowledge**: support for adding free form doc to any dashboards
|
|
|
|
## Metadata Sources
|
|
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.
|
|
|
|
We have provided example [ETL ingestion](architecture/metadata-ingestion.md) scripts for:
|
|
- Hive
|
|
- Kafka
|
|
- RDBMS (MySQL, Oracle, Postgres, MS SQL etc)
|
|
- Data warehouse (Snowflake, BigQuery etc)
|
|
- LDAP
|