PySpark supports many data sources out of the box, such as Apache Kafka, JDBC, ODBC, Delta Lake, etc. However, some older systems, such as systems that use JMS protocol, are not supported by default and require considerable extra work for developers to read from them. One such example is ActiveMQ for streaming. Traditionally, users of ActiveMQ have to use a middle-man in order to read the stream with Spark (such as writing to a MySQL DB using Java code and reading that table with Spark JDBC). With PySpark 4.0’s custom data sources (supported in DBR 15.3+) we are able to cut out the middle-man processing using batch or Spark Streaming and consume the queues directly from PySpark, saving developers considerable time and complexity in getting source data into your Delta Lake and governed by Unity Catalog and orchestrated with Databricks Workflows.

Dropbox, a leading cloud storage platform, is on a mission to accelerate data insights to better understand customers’ needs and elevate the overall customer experience. By leveraging Fivetran’s data movement platform, Dropbox gained real-time visibility into customer sentiment, marketing ROI, and ad performance-empowering teams to optimize spend, improve operational efficiency, and deliver greater business outcomes.Join this session to learn how Dropbox:- Cut data pipeline time from 8 weeks to 30 minutes by automating ingestion and streamlining reporting workflows.- Enable real-time, reliable data movement across tools like Zendesk Chat, Google Ads, MySQL, and more — at global operations scale.- Unify fragmented data sources into the Databricks Data Intelligence Platform to reduce redundancy, improve accessibility, and support scalable analytics.