| Time-unit partitioning (DAY/HOUR/MONTH/YEAR) | Queries always filter on a date/timestamp column | Most common; use DAY unless data volume demands HOUR or is low enough for MONTH/YEAR |
| Integer-range partitioning | Queries filter on an integer key (e.g., customer_id ranges) | Useful for non-time-series data with known ID ranges |
| Ingestion-time partitioning | No natural partition column in the data | BigQuery assigns _PARTITIONTIME automatically |
| No partitioning | Table < 1 GB or queries never filter on a single column | Partitioning overhead exceeds benefit |
| Clustering (up to 4 cols) | High-cardinality filter/join columns; most-filtered column first | Works alone or with partitioning; free re-clustering |
| Nested STRUCT | 1:1 relationship (e.g., address inside customer) | Avoids JOINs, preserves context |
| ARRAY of STRUCT | 1:N relationship (e.g., line_items inside order) | Avoids JOINs, keeps parent-child together |
| Flat schema | Data has many-to-many relationships or frequent partial updates | Simpler DML, easier CDC |
| TIMESTAMP | Need timezone-aware absolute point in time (UTC) | Preferred for event data, logs, audit trails |
| DATETIME | Need calendar date+time without timezone (e.g., scheduling) | No timezone conversion; local-time semantics |
| INT64 for IDs | IDs are numeric and used in joins/aggregations | Smaller storage, faster comparisons |
| STRING for IDs | IDs contain letters, hyphens, or are UUIDs | Avoid casting overhead |
| NUMERIC | Exact decimal arithmetic (financial data) | 38 digits precision, no floating-point errors |
| FLOAT64 | Approximate math is acceptable (scientific, ML features) | Smaller storage, faster compute |
