Agile Data Platforms : Innovate at the speed of thought
summary
Argues that true agility means delivering more sophisticated use cases and that AI needs a solved data strategy first, then shows a working Coalesce pipeline on Snowflake that uses templated nodes and Cortex LLMs to translate German and French to English, union staged tables, auto-generate SQL and flatten arrays, and extract sentiment and customer name into analytics-ready fields. Claims the demo takes about five minutes to build and can be operationalized instantly, reducing technical burden so teams focus on business outcomes.
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chapters (17)
- 0:00Welcome
- 1:34Enterprise data engineering platform
- 2:35Automation in data engineering
- 4:39Time as data innovation limit
- 6:44Data engineering evolution
- 9:11Modern tools and data development
- 10:35Visualization in data work
- 12:24Collaboration and lineage
- 13:48Generative AI and productivity
- 14:50Data engineers as value creators
- 15:52Data modeling founders
- 17:14Data mesh and socio-technical systems
- 19:00Agile data platform definition
- 20:44Call to action for data engineers
- 21:07Live demo
- 21:28LLM-powered data pipeline
- 26:42Closing and Q&A
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