# BigQuery

> Serverless data warehouse on Google Cloud

BigQuery is Google Cloud's serverless, fully managed data warehouse with on-demand or capacity-based pricing, streaming ingestion, and built-in ML functions.

Website: https://cloud.google.com/bigquery

## Use it when

- You want a warehouse with effectively no operations: no clusters, no sizing, no maintenance windows.
- The stack is Google Cloud, or the analytics should sit beside GCS, Dataflow, and Vertex AI.
- Workloads are irregular; on-demand pricing by bytes scanned means idle costs nothing, and reservations cap spend when usage stabilizes.
- Streaming ingest and in-warehouse ML (BQML) shorten the path from event to model.

## Think twice when

- Heavy, steady scan volume on demand gets expensive; the move to slot reservations changes the cost conversation entirely, so model both early.
- Multi-cloud portability matters; storage is proprietary Capacitor and the dialect is GoogleSQL, though Omni reaches S3 and Azure Blob and managed Iceberg is available.
- Latency targets are interactive-application grade; BI Engine helps dashboards, but serving engines like ClickHouse hold sub-second under concurrency.

## How it runs

Fully managed and serverless on Google Cloud: queries run on Dremel's shared execution layer over Colossus storage, with compute and storage billed separately. Pick on-demand (bytes scanned) or slot reservations with autoscaling. The Storage Write API handles streaming; federated queries and BigLake/Iceberg tables reach external data.

## Details

### General

| Attribute | Value |
| --- | --- |
| License | Commercial |
| Deployment | Managed, Serverless |
| Operational complexity | Low |
| Pricing | Free, Subscription |
| Documentation | https://cloud.google.com/bigquery/docs |

### Data Warehouses

| Attribute | Value |
| --- | --- |
| Where it runs | Google Cloud; Omni queries S3 and Azure Blob in place from AWS and Azure regions |
| Compute and storage | MPP (Dremel) over Colossus, separated by the Jupiter network |
| Storage format | Proprietary Capacitor; managed Iceberg tables via Lakehouse, formerly BigLake |
| SQL dialect | GoogleSQL (ANSI-based) |
| Streaming ingest | Native: Storage Write API |
| Concurrent users | Slots from a reservation: baseline plus autoscaling, per-project caps; queries queue |
| Acceleration | Partitioning and clustering, materialized views, search indexes, BI Engine; results cached 24 hours |
| Billing unit | Bytes scanned on demand, or slot-hour on a reservation; storage per GB-month |
| Time travel | 2–7 days, 7 by default |
| Access control | IAM plus policy tags, row-level access policies and dynamic masking; Dataplex catalog and lineage |

## Capabilities

- [Data Warehouses](https://matca.io/capabilities/data-warehouse)

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Source: https://matca.io/bigquery
Last updated: 2026-08-31T11:40:47.305Z