re_data is a set of tools (dbt macros & models) that helps you make sure your data pipelines are clean & reliable. 😊
re_data data preparation macros help you clean your data faster, with less code & a smaller chance of errors. Currently, we support four types of data preparation:
- data cleaning
- data filtering
- data normalization
- data validation
re_data metrics & alerts models contain information about data quality which lets you discover bad data much faster. You can:
- use built-in metrics & extend them with your code
- test them as regular dbt models
- visualize them in your favourite BI tool
- trigger external (Slack/Pagerduty/etc.) alerts based on them
Check our docs! 📓 📓
Join re_data community on Slack (we are very responsive there)
As dbt packages currently need to be a seperate github repos, most of source code of re_data can be found here
We support most of the main data warehouses supported by dbt. We plan to add support for Spark (now officially supported by dbt).
| Integration | Status | |
|---|---|---|
 |
BigQuery | Supported |
 |
PostgreSQL | Supported |
 |
Redshift | Supported |
 |
Snowflake | Supported |
 |
Apache Spark | Planned |
re_data is licensed under the MIT license. See the LICENSE file for licensing information.
We love all contributions 😍 bigger and smaller.
Check out the current list of issues here and see if you like anything from there. Also, feel welcome to join our Slack and suggest ideas or set up a live session here.
And if you got this far and like what we are building, support us! Star https://github.com/re-data/re-data on Github 🤩