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Continously ingest and deliver both streaming and batch change data from 100s of sources using Estuary's custom no-code connectors.
- <100ms Data pipelines
- 100+ Connectors
- 2-5x less than batch ELT
BigQuery connector details
The BigQuery Batch connector captures data from your BigQuery datasets into Flow collections by periodically running SQL queries and translating the results into JSON documents. It’s ideal for analytical workloads or datasets that don’t require continuous change tracking.
- Periodically executes queries from BigQuery tables or views and loads results into Flow collections
- Supports cursor-based incremental updates for efficient polling and reduced reprocessing
- Customizable polling intervals (default: every 24 hours) for flexible scheduling
- Uses a Google Cloud Service Account with BigQuery User and Data Viewer roles for secure access
- Works seamlessly within Estuary’s Private and BYOC environments for compliance and governance
💡 Tip: Specify a timestamp or monotonically increasing ID column as your cursor to reduce data volume and capture only new or updated records efficiently.
HIGH THROUGHPUT
Distributed event-driven architecture enable boundless scaling with exactly-once semantics.
DURABLE REPLICATION
Cloud storage backed CDC w/ heart beats ensures reliability, even if your destination is down.
REAL-TIME INGESTION
Capture and relay every insert, update, and delete in milliseconds.
Real-timehigh throughput
Point a connector and replicate changes from BigQuery in <100ms. Leverage high-availability, high-throughput Change Data Capture.Or choose from 100s of batch and real-time connectors to move and transform data using ELT and ETL.
- Ensure your BigQuery insights always reflect the latest data by connecting your databases to BigQuery with change data capture.
- Or connect critical SaaS apps to BigQuery with real-time data pipelines.
Don't see a connector?Request and our team will get back to you in 24 hours
Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
| Estuary | Batch ELT/ETL | DIY Python | KAFKA | |
|---|---|---|---|---|
| Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
| Speed | <100ms | 5min+ | Varies | <100ms |
| Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
| Scale |
Deliver real-time and batch data from DBs, SaaS, APIs, and more
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