Keeping top 10 leaderboards up to date as game scores arrive: four Flink SQL jobs read the scores from Kafka and keep the rankings in PostgreSQL, and a web dashboard shows them as they change.

Capturing every insert and update in PostgreSQL without changing the application: Debezium streams the changes to Kafka, and an S3 sink connector saves them as files.

Small data engineering, stream processing, machine learning and MLOps projects that run on a laptop from a fresh clone, each explaining the system it builds.

Learning MLOps hands-on: the three projects from Jim Dowling's feature store book, rebuilt with open source tools that run locally.

Stopping a language model from inventing SQL over a lakehouse: a semantic layer between the model and Iceberg tables, built with Strands, WrenAI and Trino.

Running Kafka, Flink, Spark, Trino, Iceberg and Airflow together on a laptop: one CLI starts them as a single local stack, with MLOps and observability tools.

Flink Table API in Kotlin states the supplier statistics as a declarative windowed aggregation over a DataStream, with late rows routed by hand.

Flink DataStream API in Kotlin computes the same supplier statistics, using watermarks for event time and side outputs to collect late order events.

Kafka Streams in Kotlin aggregates Avro order events into tumbling window supplier statistics and handles late records with a custom extractor.

Avro and Schema Registry replace hand written JSON codecs in a Kotlin Kafka producer and consumer, with generated classes and graceful shutdown.