Core

Dimensional Modeling and Star Schemas

Dimensional modeling organizes an analytics warehouse into fact tables (the measurable events) surrounded by dimension tables (the descriptive context), forming a star schema. Choosing the right grain and denormalizing dimensions is what makes BI queries both fast and legible. Applied-AI interviews probe it because anyone building reporting tables, feature pipelines, or training datasets has to decide what one row means and how to join context to events.

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COURSES COVERING THIS TOPIC

No lesson covers this one directly yet. These teach the surrounding topic from the beginning.

Applied AI Engineering·The interviewPremium12mTurning this course into a study planA concrete four-week plan mapping the seven modules onto the question bank, plus what to do differently if your interview is next week rather than next month.

RELATED CONCEPTS

Slowly Changing Dimensions (SCD)Warehouse vs Lake vs Lakehouse Incremental Models and MERGE/UPSERT SQL Joins

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringExplain dimensional modeling: star vs snowflake schema, facts vs dimensions, and normalize vs denormalize for analytics.→SQL & Data EngineeringSCD Type 2 in Delta Lake: track history with one atomic MERGE.→SQL & Data EngineeringGroup a stream of user events into sessions in SQL (30-minute inactivity gap) using window functions.→SQL & Data EngineeringTop-N per group in SQL: ROW_NUMBER vs RANK vs DENSE_RANK.→SQL & Data EngineeringA SQL query is slow. How do you diagnose and optimize it?→SQL & Data EngineeringHow do you find and remove duplicate rows in SQL, including 'fuzzy' near-duplicates?→

COMPANIES THAT ASSUME THIS

Scale AI Palantir Databricks Snowflake Zendesk Clay

NEXT IN DATA & SQL ENGINEERINGSlowly Changing Dimensions (SCD)→