Two-way sync
Changes in Amazon Aurora or Google Cloud Platform instantly reflect in both systems. No stale data, no manual imports.
Keep Amazon Aurora 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 Amazon Aurora'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 Amazon Aurora where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Amazon Aurora sync into Google Cloud Platform in real time, and result tables in Google Cloud Platform sync back into Amazon Aurora, with schema and type mapping between the two systems handled for you.
Rows from Amazon Aurora 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 Amazon Aurora, 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.
| Amazon Aurora objects | Google Cloud Platform objects | |
|---|---|---|
| Materialized Views Precomputed result sets (PostgreSQL-compatible clusters) readable as sources. | Firestore documents Document data read and written through the Firestore API for app-facing syncs. | |
| Columns and Data Types Standard MySQL or PostgreSQL types mapped during field mapping. | Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads. | |
| Primary and Foreign Keys Constraints used to identify records and preserve relational integrity in syncs. | BigQuery datasets Namespaces that group tables; syncs target tables within a dataset. | |
| Read Replicas Reader endpoints that syncs can target to keep load off the writer. | BigQuery tables The primary analytics destination, written through load jobs or the Storage Write API and queried with SQL. | |
| Databases Logical databases within a cluster that scope a sync connection. | Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases. | |
| Schemas Namespaces (PostgreSQL) or database-level grouping (MySQL) used in table selection. | Cloud Storage objects Staging area for file-based bulk loads into BigQuery and other services. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon Aurora–Google Cloud Platform connection.
Changes in Amazon Aurora or Google Cloud Platform instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever Amazon Aurora 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 Amazon Aurora or Google Cloud Platform record.
Track your Amazon Aurora ⇄ Google Cloud Platform sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between Amazon Aurora 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 Amazon Aurora 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 Amazon Aurora 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 Amazon Aurora and Google Cloud Platform: authenticate both systems, choose the objects to sync (such as Amazon Aurora's Materialized Views and Columns and Data Types), map fields visually, and changes propagate both ways in milliseconds — no code required.
Common patterns for Amazon Aurora and Google Cloud Platform: Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed; Fresh analytics without loading windows. Rows from Amazon Aurora land in Google Cloud Platform as they change, replacing hand-built CDC and batch extract jobs.
Amazon Aurora: MySQL or PostgreSQL wire protocol (SQL); optional RDS Data API over HTTPS. Authentication: Database credentials or IAM database authentication. Google Cloud Platform: Per-service REST and gRPC APIs; BigQuery speaks SQL and Cloud SQL exposes standard database wire protocols. Authentication: IAM service accounts with OAuth 2.0 tokens. Stacksync manages authentication, retries, and rate limits on both sides.
Google Cloud Platform: Cloud SQL Postgres and MySQL expose log-based CDC (logical replication and binlog), which Datastream and external sync tools consume for real-time replication. Amazon Aurora: A cluster exposes distinct writer and reader endpoints, and supports multiple read replicas, so sync reads can be isolated from transactional writes. Stacksync's field mapping accounts for these differences between Amazon Aurora and Google Cloud Platform without custom code.
Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Amazon Aurora and Google Cloud Platform records are not retained after a sync operation.
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Amazon Aurora and Google Cloud Platform connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Amazon Aurora–Google Cloud Platform integration in-house.
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 Amazon Aurora and Google Cloud Platform.