BigQuery Data Warehouse
End-to-end BigQuery implementations for marketing and product teams: warehouse design, scheduled imports, event and campaign models, cost-aware partitioning, and Data Studio reports so leadership sees one source of truth.
BigQuery architecture, ETL/ELT pipelines & Google Data Studio dashboards
A data warehouse is where you bring marketing, product, and business data together in one place—clean, structured, and ready to query. Google BigQuery is our cloud warehouse of choice: serverless, fast on large volumes, and native to the Google stack you already use for analytics and ads.
We ingest events from GA4, GTM, ad platforms, CRM, and app backends into BigQuery, transform them with SQL (and dbt where it helps), then connect Google Data Studio (now Looker Studio) so stakeholders get beautiful, governed dashboards—not spreadsheet exports.
From raw events to dashboards people trust
Collect data in BigQuery, process it with SQL, and present it in Google Data Studio—one pipeline from tags to executive reporting.BigQuery is Google Cloud’s serverless data warehouse. You load structured and semi-structured data at scale, run SQL in seconds, and pay for storage and queries—not idle servers. For analytics teams, it is the central place where GA4 events, ad spend, CRM records, and product logs become comparable.
A data warehouse is not just storage—it is the governed layer between collection and reporting. Raw events land in staging tables; SQL transforms them into business-ready models (sessions, campaigns, revenue); BI tools like Google Data Studio read those models so everyone sees the same definitions.
- Unify GA4, Google Ads, Meta, CRM, and backend data in one queryable store
- Separate raw ingestion from curated marts—stable metrics for dashboards
- Scale from startup volumes to billions of rows without managing clusters
- Pair with Google Data Studio for filters, charts, and board-ready visualizations
- Keep costs predictable with partitioning, clustering, and query patterns