What is BigQuery?
BigQuery is Google Cloud’s fully managed, serverless data warehouse for analytical SQL at scale. It’s built to crunch petabytes quickly without you provisioning clusters, patching, or worrying about storage engines. For web analytics teams, BigQuery becomes the “single source of truth” where raw event streams (site/app events, ad platform exports, CRM, product data) land and can be queried together to answer real business questions about Sessions, Pageview, and downstream Conversion performance.
How BigQuery works
Under the hood, BigQuery separates compute from storage and uses a columnar format that’s optimized for scans and aggregations. You write ANSI SQL; BigQuery handles parallelization. Typical patterns:
- Partition and cluster large tables for predictable performance.
- Stream near–real-time events (e.g., server-side tracking) and join with historical data for up-to-the-minute Real-Time Data views.
- Schedule transformations to build durable marts for reporting (think daily attribution tables using your chosen Attribution Model).
Costs are driven by data processed and/or reserved capacity; good table design and filtered queries keep bills sane.
Why analysts use it
- Join marketing UTMs with product and revenue to compute channel ROAS, CAC, and retention. See UTM and Source.
- Build reliable cohorts, funnels, and LTV models that exceed the limits of UI tools. See Cohort Analysis.
- Enforce governance: model event schemas, track Client ID logic, and map cookies in privacy-safe ways aligned with your GDPR posture.
Visualization and tooling
BigQuery plugs into BI tools so stakeholders don’t need SQL. Common front ends include Google Data Studio and Power BI. Pairing BigQuery with Tag Management ensures consistent event names/params from collection to warehouse.
When to pick BigQuery
Choose BigQuery when your analytics questions outgrow platform dashboards: multi-touch attribution across channels, user-level retention, marketing mix modeling, or blending ad spend with backend revenue. It’s equally useful whether you collect events from GA-style exporters or from alternative privacy-first trackers—what matters is landing clean events and modeling them well.