Core

Slowly Changing Dimensions (SCD)

Slowly changing dimensions are the patterns for handling dimension attributes that change over time, such as a customer moving cities or a product changing category. Type 1 overwrites history, Type 2 keeps versioned rows with effective dates and a current flag, and Type 3 keeps a prior-value column. Applied-AI interviews probe it because answering what something looked like at the time of an event requires deliberate history tracking, and most analysts only know how to overwrite.

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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

Dimensional Modeling and Star Schemas Change Data Capture Incremental Models and MERGE/UPSERT SQL Joins

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringSCD Type 2 in Delta Lake: track history with one atomic MERGE.→SQL & Data EngineeringWhat is a MERGE (upsert), and how do you use it for incremental loads and SCDs?→SQL & Data EngineeringGiven rows with start and end timestamps, merge all overlapping intervals per user in SQL.→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?→

COMPANIES THAT ASSUME THIS

Scale AI Palantir Databricks Snowflake Zendesk Clay

NEXT IN DATA & SQL ENGINEERINGIncremental Models and MERGE/UPSERT→