Two-way sync
Changes in AWS S3 or Google Cloud SQL instantly reflect in both systems. No stale data, no manual imports.
Keep AWS S3 and Google Cloud SQL 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 Google Cloud SQL's rows in AWS S3, 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 Google Cloud SQL where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Google Cloud SQL sync into AWS S3 in real time, and result tables in AWS S3 sync back into Google Cloud SQL, with schema and type mapping between the two systems handled for you.
Rows from Google Cloud SQL land in AWS S3 as they change, replacing hand-built CDC and batch extract jobs.
Aggregates or model outputs computed in AWS S3 sync into Google Cloud SQL, 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 S3 objects | Google Cloud SQL objects | |
|---|---|---|
| Object Versions Prior copies retained when versioning is enabled, relevant for reprocessing. | Transaction logs MySQL binlog or PostgreSQL WAL, the source for log-based change capture. | |
| Event Notifications Notifications on object creation or deletion that trigger incremental processing. | Instances The managed MySQL, PostgreSQL, or SQL Server server a sync connects to. | |
| Access Points Scoped network endpoints used to grant a sync narrow access to a bucket. | Databases Scope the tables included in a sync configuration. | |
| Multipart Uploads The mechanism used to write large export files reliably. | Schemas Namespace tables in PostgreSQL and SQL Server instances. | |
| Buckets Top-level containers a sync targets; region and policy are set at this level. | Tables Mapped directly to sync targets; schema changes can be propagated. | |
| Objects The stored files (CSV, JSON, Parquet); syncs read them as datasets or write exports into them. | Rows Read and written by primary key during each sync cycle. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS S3–Google Cloud SQL connection.
Changes in AWS S3 or Google Cloud SQL instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever AWS S3 or Google Cloud SQL 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 S3 or Google Cloud SQL record.
Track your AWS S3 ⇄ Google Cloud SQL sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between AWS S3 and Google Cloud SQL.
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 S3 and Google Cloud SQL 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 S3 and Google Cloud SQL 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 S3 and Google Cloud SQL: authenticate both systems, choose the objects to sync (such as AWS S3's Object Versions and Event Notifications), map fields visually, and changes propagate both ways in milliseconds — no code required.
On the AWS S3 side: Access Points, Multipart Uploads, Buckets, Objects, plus custom fields where AWS S3 exposes them. On the Google Cloud SQL side: Instances, Databases, Schemas, Tables. 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.
Common patterns for AWS S3 and Google Cloud SQL: Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed; Fresh analytics without loading windows. Rows from Google Cloud SQL land in AWS S3 as they change, replacing hand-built CDC and batch extract jobs.
AWS S3: REST API (the S3 API), accessed directly or through AWS SDKs. Authentication: AWS IAM credentials with SigV4 signing; commonly a role scoped to specific buckets and prefixes. Google Cloud SQL: Native SQL wire protocols (MySQL, PostgreSQL, SQL Server) plus a REST admin API for instance management. Authentication: Database credentials; IAM database authentication is available for MySQL and PostgreSQL. Stacksync manages authentication, retries, and rate limits on both sides.
AWS S3: As object storage, S3 has no row-level semantics; incremental sync operates at file granularity. Google Cloud SQL: Connections use standard wire protocols, so existing drivers and ORMs work without modification. Stacksync's field mapping accounts for these differences between AWS S3 and Google Cloud SQL without custom code.
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 S3 and Google Cloud SQL.