TopN AnalyticsTechnology
BigQuery
Google's serverless, columnar data warehouse, our default choice for teams that want fast analytics without managing infrastructure.
Our Experience
How we typically use it
We use BigQuery as the analytical warehouse in most GCP-based stacks, paired with partitioning and clustering strategies that keep query costs predictable as data volume grows.
Implementation Patterns
Where it fits
- Central analytics warehouse fed by streaming or batch pipelines
- Partitioned and clustered tables for cost-efficient querying at scale
- Scheduled queries for recurring transformations
FAQs
Common questions
- Is BigQuery a good fit for a small team?
- Yes; its pay-per-query pricing and zero infrastructure management make it a reasonable default even before you have a large data volume.
- Do we need to already be on GCP to use BigQuery?
- No. BigQuery can ingest data from almost any source regardless of where the rest of your stack runs.