About
BigQuery is Google Cloud's serverless data warehouse, storing and querying very large datasets without the customer running any infrastructure. Its architecture decouples storage from compute, so queries scale independently of how much data is held, and it reads and writes open Apache Iceberg tables alongside its own storage. Machine learning and generative-AI functions run in SQL against data already in place. In AEC it sits downstream of the delivery tools, as the central store a firm reports across projects from.
How teams use it
By audience
- IT & Systems Administration
- Provisions the Google Cloud project and dataset the firm's project systems load into, and holds the service-account credentials those loaders authenticate with.
- Chooses between on-demand and capacity pricing and sets the refresh cadence, which is what the reporting bill actually turns on.
- Owns IAM roles and dataset-level access, since grant design is what decides who can query which project's data.
- Decides whether project data lands in native storage or in Iceberg tables the firm's other engines can also read.
- Executive Leadership
- Gets cross-project reporting no single project system can answer, because PM, ERP and field extracts land in one place.
- Reads dashboards built on top of the warehouse rather than in each source application.
- Accounting & Finance
- Joins job cost from the ERP to project activity from the PM system, which neither system can do on its own.
- Keeps a query-able cost history that outlives the reporting windows the source systems impose.
Integrations (4)
| Integrates with | Connection | Details |
|---|---|---|
| NativeDatabricks BigQuery data source | ||
| NativeDomo BigQuery Service Connector and BigQuery Writeback Connector | ||
| NativePower Query Google BigQuery connector | ||
| NativeTableau Google BigQuery connector |
Only partners listed on AECi appear here. If one is missing, suggest a correction.
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