BigQuery Cost for Firebase and App Analytics

Firebase, GA4, and app event exports can make BigQuery feel free at first because the product team sees dashboards before it sees warehouse cost. Start with events per session, sessions per user, monthly active users, average event size, and how many environments export data.

Keep noisy debug events, client retries, background heartbeat events, and duplicate server events out of the default estimate. They increase stored data and can make every later query scan more bytes than the product actually needs.

BigQuery cost usually has two different questions: how much data is stored, and how much data each query processes. A small table can still be expensive if repeated dashboards, Looker Studio reports, scheduled queries, or ad hoc SQL scan the same wide date range many times.

Partition event tables by date, cluster high-volume dimensions when it helps pruning, and teach analysts to select only needed columns. For recurring reports, create smaller summary tables so every dashboard refresh does not rescan raw event history.

Analytics data rarely needs the same retention everywhere. Keep raw event detail for the period needed for debugging and attribution, then aggregate older data into product metrics, funnels, cohorts, or revenue summaries.

Before launch, pair BigQuery with Cloud Billing budgets, dataset expiration policies, query cost controls, and owner review for scheduled jobs. Then add the expected analytics workload to the calculator's broader Firebase, Cloud Run, Firestore, Storage, and Cloud SQL scenario instead of treating analytics as a separate afterthought.