Stream data from Google Cloud SQL for PostgreSQL to Google Bigtable
Move data from Google Cloud SQL for PostgreSQL to Google Bigtable in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 7+GB/secSingle dataflow
How to integrate Google Cloud SQL for PostgreSQL with Google Bigtable in 3 simple steps
- 1
Connect Google Cloud SQL for PostgreSQL as your data source
Set up a source connector for Google Cloud SQL for PostgreSQL in minutes. Estuary supports streaming (including CDC where available) and batch data capture through events, incremental syncs, or snapshots — without custom pipelines, agents, or manual configuration.
- 2
Configure Google Bigtable as your destination connector
Estuary supports intelligent schema handling, with schema inference and evolution tools that help align source and destination structures over time. It supports both batch and streaming data movement, reliably delivering data to Google Bigtable.
- 3
Deploy and Monitor Your End-to-End Data Pipeline
Launch your pipeline and monitor it from a single UI. Estuary guarantees exactly-once delivery, handles backfills and replays, and scales with your data — without engineering overhead.

Google Cloud SQL for PostgreSQL connector details
The Google Cloud SQL for PostgreSQL source connector streams changes from a Cloud SQL for PostgreSQL database into Estuary collections using log-based Change Data Capture. After an initial backfill of selected tables, Estuary reads ongoing inserts, updates, and deletes through PostgreSQL logical replication so downstream systems stay current without repeated full reloads.
- Capture real-time inserts, updates, and deletes from Google Cloud SQL for PostgreSQL using PostgreSQL logical replication.
- Support Cloud SQL for PostgreSQL versions 10.0 and later.
- Enable logical replication with wal_level=logical and use a database role with the required REPLICATION and table-read permissions.
- Configure a replication slot, publication, and watermarks table; Estuary can create some of these automatically when permissions allow.
- Backfill selected tables first, then continue streaming incremental changes from PostgreSQL’s write-ahead log.
- Connect securely using Cloud SQL connection details such as host, port, database name, user credentials, and supported networking options like allowlisting or SSH tunneling.

Google Bigtable connector details
The Google Cloud Bigtable materialization connector in Estuary delivers data from Estuary collections into tables in a Google Cloud Bigtable instance. Each materialization binding writes to a Bigtable table, with row keys derived from the source collection’s primary key.
- Materializes Estuary collections directly into Google Cloud Bigtable tables
- One Bigtable table per materialization binding
- Row keys derived from the Estuary collection key, including support for composite keys
- Automatic creation of destination tables and the
**f**column family - Selected fields stored as Bigtable columns, with the full source document also stored as JSON
- Soft deletes by default, represented through
_meta/op - Optional hard deletes to remove rows from Bigtable when records are deleted upstream
- Google Cloud service account authentication using JSON credentials
- Google Cloud IAM / Workload Identity Federation authentication
- Custom Bigtable endpoint support for Bigtable-compatible APIs
Composite collection keys are encoded so their lexicographic ordering is preserved, which makes prefix-based range scans efficient in Bigtable. The connector stores projected fields individually and also stores the full root document in the flow_document column by default.
Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
Spend 2-5x less
Estuary customers not only do 4x more. They also spend 2-5x less on ETL and ELT. Estuary's unique ability to mix and match streaming and batch loading has also helped customers save as much as 40% on data warehouse compute costs.

Google Cloud SQL for PostgreSQL to Google Bigtable pricing estimate
Estimated monthly cost to move 800 GB from Google Cloud SQL for PostgreSQL to Google Bigtable is approximately $1,000.
Data moved
Choose how much data you want to move from Google Cloud SQL for PostgreSQL to Google Bigtable each month.
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Why pay more?
Move the same data for a fraction of the cost.



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Frequently Asked Questions
Is this integration suitable for production workloads?
Yes. Estuary pipelines are designed for production use, with exactly-once delivery semantics, automated backfills, and continuous operation at scale.
Can I control where my data runs and is processed?
Yes. Estuary offers multiple deployment options, including fully managed SaaS, private deployments, and bring-your-own-cloud (BYOC). This allows teams to control where their data plane runs and meet security, compliance, and networking requirements. Learn more about Estuary's security and deployment options.
Can I build this Google Cloud SQL for PostgreSQL to Google Bigtable integration manually?
Yes, it's possible to build a manual pipeline using custom scripts, scheduled jobs, or open-source tools. However, manual approaches typically require ongoing maintenance, custom error handling, schema management, and operational overhead. Estuary simplifies this by providing a managed pipeline with built-in reliability, scaling, and monitoring.
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