Quick Decisionbeginner5 min
Pick an SCD pattern
Pick the dimension strategy that preserves historical product prices for downstream revenue analysis.
Success signal
Commit to a decision, then compare the reasoning.
Practice data modeling
See what one schema decision does to the whole pipeline.
SchemaLab is the hands-on way to build data-modeling judgment. Change one thing on the canvas and watch the SQL, the checks, and the downstream pipeline react, the exact reasoning senior data interviews probe for.
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- Round
- Schema design
- Pressure
- Judgment, not syntax
- Signal
- Can you defend it?
live_schema_canvas.erd
Flip one decision. Watch the SQL and the downstream pipeline react.
generated_sql.sql · regenerates from the canvas
CREATE TABLE "dim_customer" ("customer_key" UUID NOT NULL PRIMARY KEY,"customer_id" VARCHAR(64) NOT NULL,"customer_segment" TEXT);CREATE TABLE "dim_product" ("product_key" UUID NOT NULL PRIMARY KEY,"sku" VARCHAR(64) NOT NULL);CREATE TABLE "fact_order_items" ("order_item_key" UUID NOT NULL PRIMARY KEY,"customer_key" UUID NOT NULL,"product_key" UUID NOT NULL,"net_revenue" NUMERIC NOT NULL);ALTER TABLE "fact_order_items" ADD FOREIGN KEY ("customer_key") REFERENCES "dim_customer" ("customer_key");ALTER TABLE "fact_order_items" ADD FOREIGN KEY ("product_key") REFERENCES "dim_product" ("product_key");
- History
- Latest value only
- Storage
- 1 row per customer
- Query
- Current state only
Valid star schema
Practice loop
The canvas is the source of truth.
SchemaLab teaches causality in structures. Change the schema and the downstream readings change with it.
Model the grain
Start where interviewers start: what one row means, which dimensions explain it, and where history belongs.
Read the instrument
The ERD drives generated SQL, lifecycle readings, storage trade-offs, and deterministic checks from one state.
Defend the trade-off
Layer 3 reviews judgment after the deterministic checks, so feedback stays grounded in the model you built.
1. ERD
fact_order_items -> dimensions
Foreign-key direction stays visible on the canvas.
2. SQL
regenerates from state
Postgres output follows the model, not a separate editor.
3. Checks
keys, grain, history
Layer 1 and 2 are deterministic instrument readings.
4. Readiness
evidence over guesswork
Progress is tied to solved concepts and verdict history.
Challenge previews
Real problem sets, not trivia.
Start with constrained decisions, move into schema builds, then take open scenarios where the answer is a defensible model.
Concept Challengeintermediate20 min
Price history with SCD2
Extend a starter fact table with a dimension that supports accurate historical price analysis.
Success signal
Build the model before opening any authored approach.
Concept Challengeintermediate20 min
Campaign enrollment bridge
Resolve a subscriber-to-campaign many-to-many relationship without losing enrollment detail.
Success signal
Build the model before opening any authored approach.
Starter concept coverage
Ten concepts that become visible on the canvas.
Primary Keys
Learners identify stable row identity and mark the correct column or column set as the primary key.
Foreign Keys
Learners connect entities through explicit referenced keys instead of informal naming conventions.
Relationship Cardinality
Learners show the expected one-to-one, one-to-many, or many-to-many shape for a given business relationship.
Many-to-Many Modeling
Learners recognize when a direct relationship needs an associative structure rather than a naive single FK.
Normalization
Learners restructure tables so each fact is stored once, and judge when denormalization is the right trade.
Fact vs Dimension Roles
Learners separate measurable events from descriptive context instead of collapsing everything into one table.
Fact Table Grain
Learners make the row-level meaning of a fact table explicit before adding measures and relationships.
Surrogate Keys
Learners use synthetic identifiers where versioned dimensional history or stable warehouse joins require them.
Degenerate Dimensions
Learners keep transaction-level identifiers on the fact when a separate dimension adds no analytical value.
Slowly changing dimensions: Type 2
Learners preserve historical dimensional states by versioning rows rather than overwriting them.