Sync Amazon Aurora Data to Google Cloud Spanner in Minutes

About Amazon Aurora

Amazon Aurora is a relational database engine that combines the speed and reliability of high-end commercial databases with the simplicity and cost-effectiveness of open source databases.

About Google Cloud Spanner

The first horizontally scalable, globally consistent, relational database service.

Most Popular Connectors

Get Started on Your Data Integration Today

Connect Amazon Aurora to Google Cloud Spanner and 200+ other platforms in minutes.

Talk to an expert

FAQ

Frequently asked questions

Clear answers to the questions teams ask when evaluating Integrate.io.

Still have questions?

Talk to an expert →
Can Integrate.io sync Amazon Aurora data to Google Cloud Spanner?

Yes. Integrate.io helps teams build managed pipelines that move Amazon Aurora data into Google Cloud Spanner for analytics, operations, and reporting workflows.

What Amazon Aurora data can I move to Google Cloud Spanner?

The available Amazon Aurora data depends on the connector, authentication, API permissions, and objects selected. Integrate.io helps map that data into Google Cloud Spanner fields and tables.

Can I transform Amazon Aurora data before it lands in Google Cloud Spanner?

Yes. Integrate.io supports mapping, filtering, joins, enrichment, scheduling, monitoring, and error handling before Amazon Aurora data reaches Google Cloud Spanner.

How often can Integrate.io refresh Amazon Aurora data in Google Cloud Spanner?

Refresh timing depends on source limits, destination capacity, data volume, and business requirements. Teams can configure schedules that keep Google Cloud Spanner updated from Amazon Aurora.

Do I need custom code for a Amazon Aurora to Google Cloud Spanner pipeline?

Most Amazon Aurora to Google Cloud Spanner pipelines can be configured visually in Integrate.io. Teams can add advanced logic when the integration requires API-specific handling or custom transformations.

How do I validate a Amazon Aurora to Google Cloud Spanner integration?

Start with a scoped Amazon Aurora sync, confirm field mapping and row counts in Google Cloud Spanner, review pipeline logs, then schedule the production workflow once the data matches expectations.