Deduplicate Before Join — Solution & ExplanationProblems / Deduplicate Before Join / EditorialApproachThe events table has 380 rows, but only 300 are unique (80 duplicates). Without deduplication:

Deduplicate first — SELECT DISTINCT on business columns reduces 380 → 300 rows.

Join the deduplicated events with users.

Aggregate per user.

Why This MattersDuplicate rows in fact tables are common due to:

ETL retries that insert the same data twice

Event collection systems that replay messages

Upstream bugs that duplicate records

If you join duplicated events with users, every duplicate gets matched, inflating:

COUNT by the number of duplicates

SUM by the duplicated values

Deduplicating first ensures:

Correct results — no inflated metrics

Better performance — fewer rows in the join

Complexity

Deduplication: O(n) with a hash set

Join: O(k) where k = unique events, not total events

Hints

Use SELECT DISTINCT on the business columns (not event_id) to deduplicate.

Deduplication should happen in a CTE before the JOIN.

In PySpark, use .dropDuplicates() before .join().

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