Core

Deduplication (Exact and Fuzzy)

Duplicates slip into data through retries, joins, and multiple sources, and they corrupt counts, training sets, and aggregates. Exact dedup is a window-function job: ROW_NUMBER over a key, keep rank 1. Fuzzy/near-duplicate dedup (same content, slightly different) needs similarity, embeddings or MinHash/LSH to surface near-matches at scale without comparing all pairs. AI, ML, and GenAI interviews probe it because deduping training data and pipeline outputs is constant, and naive all-pairs comparison does not scale.

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RELATED CONCEPTS

Window Functions Data Quality and Contracts Idempotent Data Pipelines Embeddings

PRACTICE THIS IN REAL QUESTIONS

SQL & Data EngineeringHow do you find and remove duplicate rows in SQL, including 'fuzzy' near-duplicates?→SQL & Data EngineeringTurn a raw web crawl into a clean trillion-token LLM training corpus. Design the pipeline.→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 EngineeringDeduplicate events exactly-once over a sliding 7-day window in a high-throughput stream without running out of memory.→

COMPANIES THAT ASSUME THIS

Scale AI Palantir Databricks Snowflake

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