Comparisons anchored on Apache Spark, the Apache-2.0 engine for data engineering, SQL, streaming and machine learning on clusters.
Batch-first against streaming-first: Spark's unified batch and streaming processing against Flink's event-time, exactly-once stream processing, with release lines and LTS, Kubernetes operators for both, state handling and which suits ETL, real-time pipelines or both.
Choosing a cluster manager for self-hosted Spark: the standalone manager, Hadoop YARN for existing Hadoop estates, and Kubernetes with the Apache Spark Kubernetes Operator. Compares isolation, autoscaling, dependency packaging, Spark Connect and the skills each needs.
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