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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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 EngineeringImplement Slowly Changing Dimension Type 2 history tracking in a Delta lakehouse.→SQL & Data EngineeringGroup a stream of user events into sessions in SQL (30-minute inactivity gap) using window functions.→SQL & Data EngineeringFind the top-N records per group and a running total per group in SQL.→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