Posit and the modern data stack: Using Posit, Snowflake, and Databricks
summary
Demonstrates connecting Python and R IDE workflows to remote warehouses: uses Positron Assistant with Claude 4 Sonnet and ibis to query Snowflake, and uses Databot with sparklyr against Databricks, running SQL on a cluster rather than pulling data locally. Works on a Tour de France table (1903–2019, ~250k rows, 106 editions), auto-generates exploration code and plots, even builds a Shiny QueryChat interface that applies Snowflake row‑level filters (Lance Armstrong shows 24 stages in raw data but 0 for restricted users).
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chapters (8)
- 0:00Posit and modern data stack
- 1:43Quarto and Snowflake setup
- 3:57Tour de France data with Positron
- 9:21Databricks and Databot connection
- 13:58Remote data exploration challenges
- 16:06Enterprise AI tools with QueryChat
- 18:36Cost and security concerns
- 20:00Q&A
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