Apache Spark 4.3 ships in Databricks Runtime 19
The quarterly release cadence is the quiet headline: Spark teams can expect completed features to land sooner instead of waiting on long release cycles. Data engineers running real-time pipelines should evaluate the new streaming aggregation and transformWithState APIs against existing Flink or Kafka Streams setups. Availability comes via Databricks Runtime 19, so upgrades happen through the platform rather than a standalone download.
