I explored the ANALYTICS database in Snowflake, found the sales fact table, and ran the revenue query for last quarter.
Top 5 products by Q1 2026 revenue
- Aurora Pro Headset. $1.84M across 12,300 orders, up 22% from the prior quarter.
- Nimbus Standing Desk. $1.51M, the highest average order value at $612.
- Vertex Mechanical Keyboard. $980K, with the strongest repeat-purchase rate of the five.
How I found it
- Located the data. Used SALES_PROD schema, then the FACT_ORDER_LINES table joined to DIM_PRODUCT.
- Filtered to last quarter. Summed NET_REVENUE where ORDER_DATE fell in 2026-01-01 to 2026-03-31, grouped by product.
What you can do with Snowflake
Map your warehouse before you query
Walk from databases down to schemas, tables, and column types so the agent grounds every question in the right object. No more guessing which table holds the data or how it is structured.
Answer business questions in SQL
Run real SELECT statements against your warehouse to pull revenue, usage, or cohort numbers, then get them back summarized in plain language instead of a raw result grid.
Query through your semantic layer
Inspect Snowflake semantic views to read the governed metrics, dimensions, and join paths your team defined, so answers follow approved business logic rather than ad-hoc SQL.
Supported actions in Snowflake
Read & Search
- List Databases
- List Schemas
- List Tables
- Describe Table
- Describe Semantic View
- Query
6 total actions available
Frequently asked questions about Snowflake
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