BigQuery
BigQuery is where your data actually lives — we build the schemas, optimize the queries, and control the costs so it stays that way.
What It Is
BigQuery is Google's serverless, fully managed data warehouse. It can query terabytes in seconds without any infrastructure to manage. Its columnar storage format and distributed query engine make it fast for analytics workloads by default. Recent releases add first-class Gemini integration — SQL-native AI functions, Data Canvas for natural-language exploration, and conversational analytics — but you still need to model your data well to get the most out of it.
Why We Chose It
Every major cloud has a data warehouse. BigQuery is the one where cost at scale is predictable, partitioning and clustering work without ceremony, and the ecosystem of tools (Dataform, Looker, dbt) is purpose-built around it. For European companies, BigQuery also supports EU multi-region storage natively.
How We Use It
Design partitioned and clustered table schemas that keep query costs predictable
Write and optimize complex SQL — window functions, nested structs, JSON parsing, incremental logic
Implement column-level security and row-level access policies for multi-tenant datasets
Set up BigQuery reservations and slot commitments for teams moving off on-demand pricing
Audit slow or expensive queries and rewrite them — typically achieving 60–90% cost reductions
Use Gemini in BigQuery where it fits — AI.GENERATE / AI.FORECAST SQL functions for in-warehouse AI, Data Canvas for exploration
When BigQuery is the right warehouse — and when it isn't
Choose BigQuery when:
- Analytics is your primary workload (SQL queries, not OLTP transactions)
- You want serverless pricing that scales from zero — no idle compute to pay for
- You're already on GCP or planning to be
- You need EU data residency with clean multi-region storage
- You want in-warehouse AI features without bolting on a separate ML platform
Choose Snowflake when:
- You need fine-grained warehouse sizing or per-workload compute isolation
- You're multi-cloud and need a warehouse that runs on all three clouds
- Specific Snowflake features matter (Snowpark, zero-copy cloning, data sharing)
Choose Databricks when:
- ML and data science workloads are the primary use case, not analytics reporting
- You run Spark-based pipelines or need a notebook-first environment
- A lakehouse architecture with Delta Lake is an explicit requirement
Choose Redshift when:
- You're deeply on AWS and data movement costs outweigh warehouse quality
- Your team is already fluent in Redshift and migration ROI is weak