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Google BigQuery is a serverless cloud data warehouse in Google Cloud, popular with organizations that also use Google Analytics, Google Ads and Looker. Companies integrate BigQuery to combine ERP, CRM, eCommerce and marketing data for unified reporting, to feed scores and segments back to operational tools, and to power AI and ML with BigQuery ML and Vertex AI. AIONDATA builds on the BigQuery Storage Write API, batch loads from Cloud Storage, BigQuery Data Transfer Service, Datastream and Dataflow.
We use BigQuery Data Transfer Service where a connector exists, Datastream for CDC from MySQL, PostgreSQL, SQL Server or Oracle, and custom extractors writing through the Storage Write API or Cloud Storage loads for other APIs. Transformations run as scheduled queries, Dataform or dbt.
Yes. Reverse ETL from BigQuery to CRM, ERP or commerce platforms is common, for example syncing audience segments or lifetime value scores. We build change detection on the BigQuery side and a sync service that writes to the target APIs with retries and audit logs.
Yes. AIONDATA can expose BigQuery to AI agents through a custom MCP server that provides dataset discovery and governed query tools, with IAM and authorized views limiting what each agent can read. Query cost controls such as byte limits are enforced by the server.
Yes. Cost is driven by bytes scanned and storage, so we design partitioned and clustered tables, incremental models, and materialized views for frequent queries. We also set project-level quotas and monitor query patterns so integrations do not create runaway spend.
Tell us the other system and what needs to flow between them. You'll get an honest scope and timeline.