A technical guide on building real-time lakehouse architectures using Apache Flink 2.1 and the Dynamic Iceberg Sink, addressing schema drift, file proliferation, and operational rigidity.
A monthly roundup of data engineering links covering Apache Iceberg, Kafka, Debezium, Spark, and lakehouse architecture.
Explains streaming data fundamentals, how streaming systems work, their use cases, and challenges compared to batch processing.
Explains the differences between batch and streaming data processing, covering OLTP, OLAP, and ETL concepts for data engineers.
A curated collection of Apache Kafka and ksqlDB talks, including recordings, slides, and code examples for building streaming data pipelines.
