Join this session for a concise tour of Apache Spark™ 4.0’s most notable enhancements: SQL features: ANSI by default, scripting, SQL pipe syntax, SQL UDF, session variable, view schema evolution, etc. Data type: VARIANT type, string collation Python features: Python data source, plotting API, etc. Streaming improvements: State store data source, state store checkpoint v2, arbitrary state v2, etc. Spark Connect improvements: More API coverage, thin client, unified Scala interface, etc. Infrastructure: Better error message, structured logging, new Java/Scala version support, etc. Whether you’re a seasoned Spark user or new to the ecosystem, this talk will prepare you to leverage Spark 4.0’s latest innovations for modern data and AI pipelines.

Building a custom Spark data source connector once required Java or Scala expertise, making it complex and limiting. This left many proprietary data sources without public SDKs disconnected from Spark. Additionally, data sources with Python SDKs couldn't harness Spark’s distributed power. Spark 4.0 changes this with a new Python API for data source connectors, allowing developers to build fully functional connectors without Java or Scala. This unlocks new possibilities, from integrating proprietary systems to leveraging untapped data sources. Supporting both batch and streaming, this API makes data ingestion more flexible than ever. In this talk, we’ll demonstrate how to build a Spark connector for Excel using Python, showcasing schema inference, data reads/writes and streaming support. Whether you're a data engineer or Spark enthusiast, you’ll gain the knowledge to integrate Spark with any data source — entirely in Python.