Unlocking Near Real Time Data Replication with CDC, Apache Spark™ Streaming, and Delta Lake
Description
Tune into DoorDash's journey to migrate from a flaky ETL system with 24-hour data delays, to standardizing a CDC streaming pattern across more than 150 databases to produce near real-time data in a scalable, configurable, and reliable manner. During this journey, understand how we use Delta Lake to build a self-serve, read-optimized data lake with data latencies of 15, whilst reducing operational overhead. Furthermore, understand how certain tradeoffs like conceding to a non-real-time system allow for multiple optimizations but still permit for OLTP query use-cases, and the benefits it provides. Talk by: Ivan Peng and Phani Nalluri Here’s more to explore: Big Book of Data Engineering: 2nd Edition: https://dbricks.co/3XpPgNV The Data Team's Guide to the Databricks Lakehouse Platform: https://dbricks.co/46nuDpI Connect with us: Website: https://databricks.com Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com/databricksinc Facebook: https://www.facebook.com/databricksinc
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