Description
Watch how Spark Structured Streaming Real-Time Mode, Lakebase, a Databricks App, and Unity Catalog work together to help manufacturing teams detect a machine component running hot, hold production, and investigate the event before a batch is compromised. In this demo, you’ll follow a temperature reading from a machine component through the message bus, Spark Structured Streaming Real-Time Mode, Lakebase, and Unity Catalog managed tables.
See how the digital twin updates as each reading arrives, how the app triggers an inspection and holds and resumes production, and how Genie helps teams query what happened from governed Unity Catalog tables.
🎯What you’ll learn
Detect operating-limit breaches with Spark Structured Streaming Real-Time Mode
Store live asset state in Lakebase and stream updates to a Databricks App with Server-Sent Events
Preserve device time and catch duplicate or late readings
Move Lakebase changes into Unity Catalog managed tables for history, BI, predictive maintenance, and Genie
Hold production, call an inspection, and resume the line from the Databricks App
⏱️ CHAPTERS:
00:00: Introduction and the plant floor problem
02:40: Intervention vs. delay (reactive vs. real-time)
03:36: Requirements for a useful digital twin
04:32: Databricks stack for a real-time digital twin
08:13: Live demo: CNC machine digital twin app
13:02: Summary & value to the plant
📖 RESOURCES
Learn more about Real-Time Mode in Apache Spark: https://www.databricks.com/product/data-engineering/spark-real-time-mode
Learn more about Lakebase Change Data Feed: https://docs.databricks.com/aws/en/oltp/projects/lakebase-cdf
Learn more about LakeTS: https://databricks-solutions.github.io/lakets/
🤝 STAY CONNECTED:
Website: https://databricks.com
Twitter: https://x.com/databricks/
LinkedIn: https://www.linkedin.com/company/databricks/home/
Facebook: https://www.facebook.com/databricksinc/