Apache Spark has become the most widely-used engine for executing data engineering, data science and machine learning on single-node machines or clusters. The number of monthly maven downloads of Spark has rapidly increased to 20 million.

We will talk about the higher-level features and improvements in Spark 3.2 and 3.3. The talk also dives deeper into the following features + Introducing pandas API on Apache Spark to unify small data API and big data API. + Completing the ANSI SQL compatibility mode to simplify migration of SQL workloads. + Productionizing adaptive query execution to speed up Spark SQL at runtime. + Introducing RocksDB state store to make state processing more scalable

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