BigQuery
Serverless analytics warehousing that powers growth reporting, ML, and AI on one dataset
BigQuery is Google Cloud's serverless, columnar data warehouse built to run SQL over terabyte-to-petabyte datasets in seconds without managing infrastructure. It separates storage from compute, streams events in near real time, and ships native features for machine learning (BigQuery ML), geospatial analysis, and vector search. For marketing and product teams it becomes the single source of truth where ad platforms, CRM, product events, and finance data converge.
We stand BigQuery up as the analytical core of a client's growth stack: raw events and platform exports land in staging datasets, then we model them into clean, tested tables with dbt or scheduled SQL, partitioned and clustered so cost and latency stay predictable at scale. We wire ingestion from Google Ads, Meta, GA4's native export, Shopify, Stripe, and CRM systems, then reconcile identity across sources so spend, sessions, orders, and revenue tie out to a single grain. Streaming inserts and materialized views keep dashboards and downstream activations fresh.
On top of the warehouse we build the intelligence layer. We train propensity, LTV, and attribution models directly in BigQuery ML, run vector search for semantic product and content matching, and expose governed marts to Looker, reverse-ETL tools, and application APIs. Cost controls — reservation slots, byte-scanned budgets, and query linting — are part of every build so performance scales without surprise bills.
We consolidate every paid channel, GA4, CRM, and order data into one BigQuery model so blended CAC, ROAS, and cohort retention are computed on trustworthy, reconciled numbers instead of platform-reported figures.
Using BigQuery ML we train and score customer lifetime-value and churn-propensity models where the data already lives, then feed those scores into bidding, lifecycle segments, and audience sync without moving data out.
We build multi-touch and data-driven attribution tables plus marketing-mix modeling inputs in SQL, giving clients a defensible view of channel contribution that finance and growth teams share.
We partition and cluster tables to the query patterns, replace repeated scans with materialized views, and set byte-scanned budgets plus reservation slots. Every scheduled query is reviewed for the data it touches, so cost tracks actual usage rather than table size.
Yes. BigQuery sits underneath your existing stack — GA4, Looker, ad platforms, and reverse-ETL tools all connect natively. We model a governed layer on top so your BI and activation tools read consistent numbers while the warehouse stays authoritative.
Often not. BigQuery ML lets us train regression, classification, clustering, and forecasting models in SQL against data in place, and integrates with Vertex AI when a workload needs deeper custom modeling or serving.
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