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Schema Visualizer
Click a dimension and watch its relationship to the fact table light up, its columns appear, and the join that uses it run for real. Then snowflake dim_product and see the same query grow two more joins.
- 01
fact_sales holds measures and foreign keys, nothing else. Every label lives in a dimension.
- 02
The grain of fact_sales is one row per delivered order line. Declare the grain before you model anything.
- 03
Star means one join per dimension. Snowflake normalises the dimension, which saves space and costs joins.
Fact rows
0
one per delivered order line
Distinct orders
0
matches fact rows — grain is clean
Total revenue
₹0
SUM(amount) across the fact table
Star schema
Every dimension is one join from the fact table.
Click a table to inspect it below · drag to arrange · ⌘/ctrl + scroll to zoom
- Foreign key
- Inferred by key name
- ETL lineage
dim_customer
0 rows · click any card on the canvas
The query this table serves
1 join in star form
SELECT dc.segment, dc.state,
COUNT(DISTINCT dc.customer_key) AS customers,
SUM(f.amount) AS revenue
FROM fact_sales f
JOIN dim_customer dc ON dc.customer_key = f.customer_key
GROUP BY dc.segment, dc.state
ORDER BY revenue DESC;Conformed dimension check
Every fact row must resolve to exactly one row in each dimension — otherwise the join silently drops or duplicates revenue.
Run a query to see rows here.
Run something and the rows land here.