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This project explores and implements robust relational data models for efficiently storing and querying large-scale genetic Variant Call Format (VCF) data. Leveraging PostgreSQL, we present and evaluate eight distinct schema designs, demonstrating how traditional relational databases can offer competitive performance, scalability, and full database management system (DBMS) features for complex genomic workloads, complementing specialized tools like BCF Tools and TileDB-VCF.

Aggregation of indices values over Nuts3 into database

This project aims to create valid STAC elements given already existing raster datasets.

Code examples and resources for using Kafka with Docker, Java, and Python

Java/Python code examples and PyFlink Docker image used in Flink lectures

End-to-end RSS news classification app using Spark MLlib, Kafka, Flask/RxJS

Parallelized computation, publication and analysis of climate indices from climate projections time-series.