Data Warehouse Partitioning and Clustering

Explain partitioning and clustering optimization strategies in data warehouses.

Partitioning

Divides a large table into logical segments by a column value (typically a date). If the WHERE clause includes the partition column, only relevant partitions are scanned (Partition Pruning), dramatically reducing data scanned.

Common columns: event_date, created_at

In BigQuery: query cost = only data in scanned partitions.

Clustering

Within a partition, data is physically sorted by one or more columns (similar to an index). Queries with WHERE or GROUP BY on clustered columns can skip large data blocks.

Common columns: user_id, country, category

Best Practices

• Partition column: A high-cardinality time column; ensure partitions are neither too small nor too large.
• Cluster columns: Columns frequently used in filter/group by; choose moderate cardinality.
• Use both together: Partition by date, then cluster by user_id within each partition.
• Avoid over-partitioning (too many tiny partitions hurts performance).