Thread by @_IlirAliu

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What is Kuwala?

@Kuwala is a tool to build rich features for analytics based on clean data.

It uses #PySpark in combination with #Parquet for data processing.

Florian Grüning got in contact with me, on LinkedIn.

Together with Matti Grotheer, Co-Founder of Kuwala, he is building out an open-source project to commoditize data science. Right now their open-source projects focus very much on data scientists and spatial data.

They provide data enthusiasts with data pipelines for worldwide scaled data on high-resolution demographics, popular times data, and POI data.

The installation is straightforward and can be configured from a Jupyter Notebook. People build upon this most of the time socio-economical analysis and location analytics.

How can you use Kuwala?

There are basically 3 ways you can work with the clean data at the moment:

1.Preprocessed Parquet files
2.Neo4j graph database queries
3.Jupyter notebooks with convenience functions

Which data pipelines are available right now?
👉 OpenStreetMap (OSM) POIs
👉 Google POIs
👉 High-Resolution Demographic Data
👉 H3 (Spatial Index)

Have a look at their website: kuwala.io
Here is their Github Repo: github.com/kuwala-io/kuwa…

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