Dive Deeper into Data Engineering on Databricks
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- Опубліковано 25 сер 2024
- To derive value from data, engineers need to collect, transform, and orchestrate data from various data types and source systems. However, today’s data engineering solutions support only a limited number of delivery styles, involve a significant amount of hand-coding, and have become resource-intensive. Modern data engineering requires more advanced data lifecycle for data ingestion, transformation, and processing. In this session, learn how the Databricks Lakehouse Platform provides an end-to-end data engineering solution - ingestion, processing and scheduling - that automates the complexity of building and maintaining pipelines and running ETL workloads directly on a data lake, so your team can focus on quality and reliability to drive valuable insights.
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Great explanation of delta live tables.
13:27 begins the demo
hope you enjoyed that demo!
@@frankmunz1659 😄awesome demo!
@@frankmunz1659 is the notebook used in the demo available anywhere? The GitHub link, Thank you!
Rivian dude, did he even say thank you to the presenter before him? 😂