signalpilot-labs Product Analytics
Driving Table
When aggregating usage or event metrics, drive FROM the fact/event table (events, sessions, pageviews), not the dimension table (users, features, pages). Users with zero activity MUST NOT appear in activity reports - they have no data to aggregate.
Calendar Spine Cross-Joins
Daily metrics models that cross-join a calendar spine with entities (users, features, pages) MUST cap the join at current_date - generating rows beyond today produces phantom future rows with zero metrics that inflate row counts.
If the YML description says "from creation date to current date," add AND spine.date_day >= entity.created_at::date AND spine.date_day <= current_date to the join condition - both bounds are required.
NEVER cross-join without a date cap - unbounded spines grow indefinitely and break row count expectations.
When an entity has a created_at or equivalent timestamp, the spine MUST start from that date, not from the earliest date in the spine - starting earlier produces rows before the entity existed.
Event Type Pivoting
When an events table has a type or event_name column, pivot distinct values into separate metric columns - each event type becomes a count_<type> column in the output.
Do NOT filter the events table to a single type before pivoting - all types must appear in the output as separate columns, using conditional aggregation (COUNT(CASE WHEN type = 'x' THEN 1 END)).
Read the YML or a sibling model to confirm the exact set of event types expected - inventing column names from partial data causes schema mismatches.
First-Run Behavior
On first build with no prior state, rolling window columns (7-day avg, 30-day sum) MUST be NULL for the first N-1 periods - this is correct behavior, not a bug.
Do NOT substitute 0 for NULL in rolling window columns - NULL means "insufficient history," 0 means "activity was zero."
Period-over-period columns follow the same rule: NULL on first build. See dbt-workflow "Incremental Models and Period-Over-Period Columns" for the full rule.