Two-way sync
Changes in AWS Aurora MySQL or Google Cloud Platform instantly reflect in both systems. No stale data, no manual imports.
Keep AWS Aurora MySQL and Google Cloud Platform in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
Operational databases and analytical warehouses want the same data at different moments. Analysts want AWS Aurora MySQL's rows in Google Cloud Platform, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in AWS Aurora MySQL where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in AWS Aurora MySQL sync into Google Cloud Platform in real time, and result tables in Google Cloud Platform sync back into AWS Aurora MySQL, with schema and type mapping between the two systems handled for you.
Rows from AWS Aurora MySQL land in Google Cloud Platform as they change, replacing hand-built CDC and batch extract jobs.
Aggregates or model outputs computed in Google Cloud Platform sync into AWS Aurora MySQL, where whatever reads from that database gets them without querying the warehouse.
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.
| AWS Aurora MySQL objects | Google Cloud Platform objects | |
|---|---|---|
| Primary keys and indexes Used to match rows across systems and keep incremental syncs efficient. | Pub/Sub topics Event streams used to move change events between systems in near real time. | |
| Views Can serve as read-only sync sources for derived or filtered datasets. | Firestore documents Document data read and written through the Firestore API for app-facing syncs. | |
| Foreign keys Express relationships that syncs preserve when mapping to related objects elsewhere. | Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads. | |
| Stored procedures and triggers Existing database logic keeps firing on rows written by a sync. | BigQuery datasets Namespaces that group tables; syncs target tables within a dataset. | |
| Databases (schemas) Logical namespaces that scope which tables a sync connection can see. | BigQuery tables The primary analytics destination, written through load jobs or the Storage Write API and queried with SQL. | |
| Tables The primary sync unit; each table maps one-to-one to a table or object in the paired system. | Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora MySQL–Google Cloud Platform connection.
Changes in AWS Aurora MySQL or Google Cloud Platform instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever AWS Aurora MySQL or Google Cloud Platform data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single AWS Aurora MySQL or Google Cloud Platform record.
Track your AWS Aurora MySQL ⇄ Google Cloud Platform sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between AWS Aurora MySQL and Google Cloud Platform.
Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.
Authenticate AWS Aurora MySQL and Google Cloud Platform with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Pick the AWS Aurora MySQL and Google Cloud Platform objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between AWS Aurora MySQL and Google Cloud Platform: authenticate both systems, choose the objects to sync (such as AWS Aurora MySQL's Primary keys and indexes and Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed AWS Aurora MySQL and Google Cloud Platform connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom AWS Aurora MySQL–Google Cloud Platform integration in-house.
Yes — Stacksync ships production-grade connectors for both AWS Aurora MySQL and Google Cloud Platform. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
Change detection on AWS Aurora MySQL: Log-based CDC via the MySQL binary log (binlog), with polling on timestamp columns as a fallback. On Google Cloud Platform: Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery tables. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
On the Google Cloud Platform side: BigQuery datasets, BigQuery tables, Cloud SQL databases, Cloud Storage objects, plus custom fields where Google Cloud Platform exposes them. On the AWS Aurora MySQL side: Tables, Rows, Columns, Primary keys and indexes. Stacksync auto-detects both schemas and converts types between the two systems.
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 386 integrations available for AWS Aurora MySQL and Google Cloud Platform.