This tripped me up more than it should have.
Start by clarifying the requirements: what defines a duplicate, what is the desired behavior (keep first, last, or merge), and what are the latency and cost constraints. Then propose a multi-layered approach that combines a deterministic deduplication key, a global unique constraint or index, and a batch/streaming pipeline that handles late-arriving data. Finally, discuss trade-offs between different strategies (e.g., upserts vs. delete-insert, probabilistic vs. exact) and how to monitor and backfill.
Pro tip: Emphasize that deduplication is not just a one-time cleanup but an ongoing process; propose a unique constraint or idempotent writes to prevent duplicates at ingestion, and mention how you'd handle schema evolution and backfills without downtime.
Ask questions to understand what constitutes a duplicate (e.g., same primary key, same composite key, fuzzy matching) and the desired outcome (keep latest, earliest, or merge). Also clarify data volume, latency needs, and whether deduplication should be real-time or batch.
Select a deterministic key (e.g., hash of business keys) and decide on an exact vs. probabilistic approach. For exact dedup, use a unique index or constraint; for large-scale, consider partitioning and window functions.
Implement idempotent writes (e.g., INSERT ... ON CONFLICT DO NOTHING/UPDATE) or a two-phase approach: stage data, then merge with deduplication using SQL window functions (ROW_NUMBER) or a MapReduce job. Ensure late-arriving data is handled.
Discuss partitioning, indexing, and batch sizes. For very large tables, consider using a distributed system like Spark or a streaming solution with state stores. Mention the cost of global operations and how to mitigate (e.g., incremental dedup).
Set up metrics to detect duplicates and monitor pipeline health. Plan for backfilling historical data and handling schema changes without breaking deduplication logic.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.