BigQuery implementation for marketing data: GA4 export modeling, Google Ads transfers, partitioning and clustering for cost control, and Looker Studio reporting.
The GA4 export nobody can query
Teams enable the GA4 BigQuery export, look at the raw tables once, and never return. The reason is the schema: events are nested and repeated, so a basic session or conversion count requires UNNEST gymnastics that most analysts reasonably decline. Then the first dashboard queries a full unpartitioned table on every refresh, the on-demand bill arrives, and BigQuery gets labelled as expensive when the real problem was table design.
What we implement on BigQuery
- GA4 export modeling that flattens events into clean session, user, and conversion tables
- Google Ads and YouTube ingestion via Data Transfer Service, plus non-Google channels through custom pipelines
- Partitioning and clustering on every large table so queries scan a fraction of the data
- A dbt project with tested staging and reporting layers your analysts can extend
- Looker Studio connected to modeled aggregates rather than raw event tables
- Cost controls: query budgets, slot reservations where they pay off, and per-team attribution
How we work
Turn on the GA4 export immediately if it is not already running, since there is no backfill
Model the raw export into queryable session and conversion tables
Add remaining channels so BigQuery becomes the cross-channel hub, not a GA4 silo
Partition, cluster, and point BI at aggregates, then review actual query spend
Typical stack
Frequently asked questions
Because it is an event-level table with nested and repeated fields, where every event parameter sits in an array of key-value structs. Counting sessions correctly means unnesting and re-aggregating, and small mistakes produce plausible but wrong numbers. Modeling it once into flat session and conversion tables is what makes it usable for everyone else.
On-demand pricing bills by bytes scanned, so cost is a design outcome. Partition by date, cluster on high-cardinality filters, and never point a dashboard at a raw event table. Those three together typically cut scan volume by an order of magnitude. Above roughly consistent heavy usage, flat-rate slot reservations become cheaper and we model that break-even for you.
Yes, today. The export only captures data from the moment it is enabled and there is no retroactive backfill. Every day it stays off is history permanently unavailable, and it costs almost nothing to accumulate while you decide.
For a large share of marketing teams, yes. It handles cross-channel reporting well, integrates natively with Google's ecosystem, and needs no infrastructure management. Reasons to look elsewhere are usually strict multi-tenant access requirements or a cloud strategy that rules out GCP.
Go deeper
Turn BigQuery into a real warehouse
Tell us whether the GA4 export is running and which channels you need alongside it, and we will scope the modeling.
Start a project