![synapse 3 synapse 3](http://hishinobi.synapse-blog.jp/hishichublog/images/2012/12/04/dsc01088.jpg)
![synapse 3 synapse 3](https://wowlazymacros.com/uploads/default/original/2X/9/9069f86dac8db2349fa6619c49bc308d2703505f.png)
Python type hints supported in Apache Spark 3.0 simplify expressing Panda UDFs and Pandas Function APIs.Better compliance with ANSI SQL standard in Apache Spark 3.0 option improves developer experience for data engineers familiar with standard SQL variants, and ensures Spark SQL follows the standard for arithmetic functions, type conversion, SQL functions, and SQL parsing.Thanks to Dynamic Partition Pruning, the join queries on large fact tables and smaller dimension tables can be optimized to reduce the number of rows to be scanned and improve performance.It optimizes joins (skew join, soft merge to broadcast join) and helps with shuffle partitions tuning. With Adaptive Query Execution enabled, Apache Spark improves Spark SQL to use the runtime statistics to take the most efficient query execution plan.Apache Spark 3.0 support enables Adaptive Query Execution, Dynamic Partition Pruning, ANSI SQL compliance option, Pandas User Defined Functions (UDFs) APIs and types, accelerator-aware scheduling, and the most recent version of Delta Lake: It is based on the open-source version of Apache Spark and includes optimizations added by Microsoft. Picture Credit: What is Azure Synapse Analytics? Apache Spark 3.0 runtime in Azure SynapseĪzure Synapse now offers Apache Spark 3.0 runtime in public preview.