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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.

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

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.

End-to-end 'wikipedia monitor' Docker Compose application using Kafka, Spark, RxJS/ECharts

Aggregation of indices values over Nuts3 into database

End-to-end 'aircraft tracking' Docker Compose application using Kafka, Elasticsearch, Grafana (and optionally ksqlDB)

Bundle merlin-{docs,text,metadata,tasks,paula,annis,exmaralda,solr,relannis} data (as submodules)

End-to-end 'news tag cloud' Docker Compose application with Kafka Connect, Schema Registry, a Kafka producer/consumer processor, and a Java web-app frontend

Docker for the operational calculation of the soil moisture anomalies (SMA)

vhi operational

Examples for Docker / Docker Compose lecture

Dockerfile and customizations for CTAP

Applying Lexical Diversity (LD) measures for DaZ/DaF research.

Based on https://nightlies.apache.org/flink/flink-docs-release-1.14/docs/learn-flink/overview/ and https://github.com/apache/flink-training/ (with added exercises)

Adapted from https://gist.github.com/staltz/868e7e9bc2a7b8c1f754