Hi! One of the creators here. Very proud to finally be able to show you what we've been working on for over a year now. Curious to hear your thoughts!
Objectiv is open-source (APLv2) product analytics infrastructure. It's built around a generic but strict event taxonomy, open/common data- and infra tools (currently PG, snowplow, working on bigquery with more to come), and the analyses are done using our pandas-like, SQL speaking modeling library called Bach. As a result, we’re moving towards a vision wherein models can be shared openly, independent of product, platform[1] or data platform[2].
How?
- Fully assist the dev doing the instrumentation using tools for ide support, run-time validation, ci integration. No auto capture, but very low-effort[1] instrumentation.
- Scale using proven tech: a single collector writing to PG for small or snowplow to anything[2] for big.
- Feed data into your own data warehouse after validation.
- No tracking plan, but an open event taxonomy designed for modeling, that fits most user interfaces out of the box or can be extended otherwise.
- Take some of our pre-built models off the shelf or use Bach directly to model on the raw data in a notebook.
- Bach generates SQL for the target platform: productionize without lock-in, feed to a BI system, dbt, etc.
Where?
Github: https://github.com/objectiv/objectiv-analytics
Docker demo: https://objectiv.io/docs/home/quickstart-guide/
Website/docs: https://objectiv.io/
Let me know what you think and what platform/SQL backend you’d like to see supported first.
[1] Currently on plain js, react, angular, react native. Future: vue, native android/ios.
[2] Once we’re done building Bach support for all SQL platforms. Now: PG, bigquery in development. Future: redshift, clickhouse, athena, etc.
Objectiv is open-source (APLv2) product analytics infrastructure. It's built around a generic but strict event taxonomy, open/common data- and infra tools (currently PG, snowplow, working on bigquery with more to come), and the analyses are done using our pandas-like, SQL speaking modeling library called Bach. As a result, we’re moving towards a vision wherein models can be shared openly, independent of product, platform[1] or data platform[2].
How?
- Fully assist the dev doing the instrumentation using tools for ide support, run-time validation, ci integration. No auto capture, but very low-effort[1] instrumentation.
- Scale using proven tech: a single collector writing to PG for small or snowplow to anything[2] for big.
- Feed data into your own data warehouse after validation.
- No tracking plan, but an open event taxonomy designed for modeling, that fits most user interfaces out of the box or can be extended otherwise.
- Take some of our pre-built models off the shelf or use Bach directly to model on the raw data in a notebook.
- Bach generates SQL for the target platform: productionize without lock-in, feed to a BI system, dbt, etc.
Where?
Github: https://github.com/objectiv/objectiv-analytics
Docker demo: https://objectiv.io/docs/home/quickstart-guide/
Website/docs: https://objectiv.io/
Let me know what you think and what platform/SQL backend you’d like to see supported first.
[1] Currently on plain js, react, angular, react native. Future: vue, native android/ios.
[2] Once we’re done building Bach support for all SQL platforms. Now: PG, bigquery in development. Future: redshift, clickhouse, athena, etc.