Core

Schema Evolution and Data Contracts

Schemas change as products evolve, and adding, altering, or dropping a column can break every downstream consumer at once. The safe approach is backward and forward compatible changes via expand-then-contract migrations, plus data contracts that make producer and consumer expectations explicit and enforceable in CI. Applied-AI interviews probe it because a single careless column rename can take down dashboards, jobs, and model features silently, and the engineer who plans the migration is the one who has been burned before.

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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

Data Quality and Contracts Change Data Capture Backfills and Reprocessing Dimensional Modeling and Star Schemas

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringHow do you handle schema evolution in a data pipeline or lakehouse without breaking consumers?→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 EngineeringYou have a wide table with one column per month. How do you unpivot it into tidy (key, month, value) rows?→SQL & Data EngineeringWhat is a data contract, and how does it fit into a data quality framework?→

COMPANIES THAT ASSUME THIS

Scale AI Palantir Databricks Snowflake Zendesk Clay