Datadrift helps analytics teams provide auditable metrics to demanding data consumers.

We know providing actionnable and trusted metrics is hard. Here is how Datadrift helps:

Metric changelog and metadata: give data consumers the full context and explain why metrics changed over time.

Drift detection and troobleshooting: solve metric quality issues fast and painlessly.

Open-source: break free from vendor-lock in and shape your own metric store with open-source collaboration.

pip install datagit
datagit dbt run

Check the video

Put the probe in your pipeline.

>>> from datagit import connector
>>> connector.store_metric(dataframe=dataframe, filepath="data/act_metrics_finance/mrr")

For a step-by-step guide on the python installation, see the docs.

We are in development and we would love to do the installation with you. Fill the form on our website so we can do a 15min demo. If the tool solves your problem then the installation require 2*30 min meeting.

Become aware of unknown unknowns in your data quality with metric drift alerting. Monitor the quality and consistency of your metrics over time.

Operationalize your monitoring and solve your underlying data quality issue with lineage drill-down to understand the root cause of the problem.

Migrate without hassle and safely between tools with diff-checks before/after and within/across databases.

We'd love to hear from you if you have any other use case. Just open a new issue to tell us more about it and see how we could help!

We 💚 contributions big and small. In priority order (although everything is appreciated) with the most helpful first:

• Star this repo to help us get visibility
• Open an issue to share your idea or a bug you might have spotted
• Become a Design Partner to co-built a product you & users love

Track planning on Github Projects and help us prioritising by upvoting or creating issues.