Kuwala is the data workspace for BI analysts and engineers enabling you to build powerful analytics workflows together. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt and Prefect together in one intuitive interface built with React Flow.

Do you want to discuss your first contribution, want to learn more in general, or discuss your specific use-case for Kuwala? Just book a digital coffee session with the core team here.

Kuwala stands for extendability, reproducibility, and enablement. Small data teams build data products fastly and collaboratively. Analysts and engineers stay with their strengths. Kuwala is the tool that makes it possible to keep a data project within scope while having fun again.

• Kuwala Canvas runs directly on a data warehouse = Maximum flexibility and no lock-in effect
• Engineers enable their analysts by adding transformations and models via dbt or new data sources through Airbyte
• The node-based editor enables analyst to build advanced data workflows with many data sources and transformations through simple drag-and-drop
• With models-as-a-block the BI analyst can launch advanced Marketing Mix Models and attributions without knowing R or Python

Extract and Load with Airbyte

Currently we support the following databases and data warehouses

• [x] Postgres
• [x] BigQuery

• [x] Snowflake

  For connecting and loading all your tooling data into a data warehouse, we are integrating with Airbyte connectors. For everything related to third-party data, such as POI and demographics data, we are building separate data pipelines.

Transform with dbt

To apply transformations on your data, we are integrating dbt which is running on top of your data warehouses. Engineers can easily create dbt models and make them reusable for the frontend. We have already a catalog of several transformations that you can use on the canvas. The complete documentation can be found here: https://docs.kuwala.io/

Run a Data Science Model

We are going to include open-source data science and AI models as blocks (e.g., Meta's Robyn Marketing Mix Modeling).

Report

You can easily connect your preferred visualization tool and connect it to a saved table on the canvas in the future. We will make the results exportable to Google Sheets and also available in a Medium-style markdown editor.

How can I use Kuwala?

Canvas

With the canvas you can connect to your data warehouse and start building data pipelines. To start the canvas, simply run the following command from inside the root directory:

docker-compose --profile kuwala up

Now open http://localhost:3000 in your browser, and you are good to go. 🚀

Third-party data connectors

We currently have five pipelines for different third-party data sources which can easily be imported into a Postgres database. The following pipelines are integrated:

• Admin Boundaries
• Google POIs
• Google Trends
• OSM POIs
• Population Density

Using Kuwala components individually

To use Kuwala's components, such as the data pipelines or the Jupyter environment, individually, please refer to the instructions under /kuwala.

Use cases

• How to build an Uber-like analytics system with Kuwala
• Perform location analytics for a grocery store with Kuwala
• Querying the most granular demographics data set with Kuwala

How can I contribute?

Every new issue, question, or comment is a contribution and very welcome! This project lives from your feedback and involvement!

Be part of our community

The best first step to get involved is to join the Kuwala Community on Slack. There we discuss everything related to our roadmap, development, and support.

Contribute to the project

Please refer to our contribution guidelines for further information on how to get involved.

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