Stream data from Google Firestore to Amazon Aurora for Postgres
Move data from Google Firestore to Amazon Aurora for Postgres in minutes using Estuary. Stream, batch, or continuously sync data with control over latency from sub-second to batch.
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- 200+Of connectors
- 5500+Active users
- <100msEnd-to-end latency
- 7+GB/secSingle dataflow
How to integrate Google Firestore with Amazon Aurora for Postgres in 3 simple steps
Connect Google Firestore as your data source
Set up a source connector for Google Firestore 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.
Configure Amazon Aurora for Postgres 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 Amazon Aurora for Postgres.
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 Firestore connector details
The Google Firestore connector captures data from your Firestore collections into Estuary, keeping your NoSQL data in sync for analytics, pipelines, or downstream systems.
- Schema-flexible ingestion: Automatically maps Firestore’s hierarchical collections and subcollections into Estuary collections.
- Real-time change capture: Streams document inserts and updates directly from Firestore with high fidelity.
- Smart backfill options: Choose from none, async, or sync modes to control how existing data is captured.
- Simple setup: Authenticate with a Google Cloud service account and minimal configuration.
- Safe & scalable: Built to handle Firestore’s nested structure while preventing data duplication or overload.
💡 Data freshness: Real-time streaming — continuously captures updates from Firestore to Estuary as they happen.

Amazon Aurora for Postgres connector details
- Merge-based materializations to sync only what's changed
- Low-latency delivery from streaming and batch sources
- Automatic schema alignment so your destination matches your pipeline's evolving data
- Flexible deployment models, including BYOC and hybrid for enterprise governance
- Unified streaming + batch outputs in a single tool
- End-to-end security and compliance for sensitive data workloads
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 Firestore to Amazon Aurora for Postgres pricing estimate
Estimated monthly cost to move 800 GB from Google Firestore to Amazon Aurora for Postgres is approximately $1,000.
Data moved
Choose how much data you want to move from Google Firestore to Amazon Aurora for Postgres each month.
GB
Choose number of sources and destinations.
Why pay more?
Move the same data for a fraction of the cost.



Estuary in action
See how to build end-to-end pipelines using no-code connectors in minutes. Estuary does the rest.
What customers are saying
Why Estuary is the best choice for data integration
Estuary combines streaming and batch data movement capabilities into a unified modern data pipeline. This approach simplifies building and operating pipelines like Google Firestore to Amazon Aurora for Postgres without custom code or orchestration.

Increase productivity 4x
With Estuary companies increase productivity 4x and deliver new projects in days, not months. Spend much less time on troubleshooting, and much more on building new features faster. Estuary decouples sources and destinations so you can add and change systems without impacting others, and share data across analytics, apps, and AI.
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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 Firestore to Amazon Aurora for Postgres 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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DataOps made simple
Add advanced capabilities like schema inference and evolution with a few clicks. Or automate your data pipeline and integrate into your existing DataOps using Estuary's rich CLI.






































