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Storage ⇄ Data warehouse

Amazon S3 to Google Cloud Platform integration — real-time, two-way sync

Keep Amazon S3 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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Amazon S3 and Google Cloud Platform

Bridge query-ready tables and stored files: Google Cloud Platform and Amazon S3 keep the same records in step, in real time, in both directions.

Google Cloud Platform keeps the tables and query results a business reports on; Amazon S3 keeps the raw files, documents, and objects that the same business produces and shares. The two overlap wherever a dataset lives as both — a file dropped in Amazon S3 that has to become rows in Google Cloud Platform, or a result in Google Cloud Platform that people downstream need back as a file in Amazon S3. When that overlap is bridged by manual export and import or an overnight job, one side spends the day working from a stale copy.

Stacksync syncs BigQuery datasets, BigQuery tables, Cloud SQL databases, Cloud Storage objects in Google Cloud Platform with Objects, Object metadata, Object tags, Object versions in Amazon S3 field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync keeps every copy consistent and resolves conflicts by rules you set.

Common use cases

  • 01 Publish change events to Pub/Sub so downstream services react to record updates as they happen.
  • 02 Reconcile Object versions and delete markers into an audit table so teams track when files under a compliance prefix were added, replaced, or removed.
  • 03 Index every new Object's key, size, LastModified, and user metadata into a Postgres catalog table so applications query S3 contents in SQL instead of paging ListObjectsV2.

Common sync patterns

Where Google Cloud Platform computes the labels: push them onto the files

Classifications, scores, or status derived in Google Cloud Platform are written back onto the matching Objects, Object metadata, Object tags, Object versions in Amazon S3 as metadata or tags, so the file store reflects what analytics decided.

Where Amazon S3 receives the raw files: land them as query-ready rows

Files and exports that arrive in Amazon S3 are parsed into BigQuery datasets, BigQuery tables, Cloud SQL databases, Cloud Storage objects in Google Cloud Platform as they land, so analysts query current data instead of waiting on the next scheduled load.

Where Amazon S3 is the shared drive: publish results back as files

Curated tables and query results from Google Cloud Platform are written to Amazon S3 as files the rest of the business can open, keeping the shared copy current without a hand-run export.

What you can sync between Amazon S3 and Google Cloud Platform

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 S3 objects Google Cloud Platform objects How this pairing syncs
Object tags Up to 10 key-value tags per object, mutable in place via the tagging API independent of content, so classification and retention labels sync two-way without rewriting files. Firestore documents Document data read and written through the Firestore API for app-facing syncs. Object tags is specific to Amazon S3 and Firestore documents to Google Cloud Platform — each maps to any object or custom field on the other side.
Object versions When bucket versioning is enabled every write creates a new version ID; prior versions and delete markers are readable for history and audit syncs. Spanner tables Strongly consistent relational tables accessed via SQL for transactional workloads. Object versions is specific to Amazon S3 and Spanner tables to Google Cloud Platform — each maps to any object or custom field on the other side.
Prefixes (folders) Logical path segments in object keys used to scope a sync and to parallelize throughput, since S3 rate limits partition by prefix. BigQuery datasets Namespaces that group tables; syncs target tables within a dataset. Prefixes (folders) is specific to Amazon S3 and BigQuery datasets to Google Cloud Platform — each maps to any object or custom field on the other side.
Multipart uploads In-progress large-object uploads assembled from parts; objects above ~100 MB (required above 5 GB) are written this way, and incomplete uploads persist until completed or aborted. BigQuery tables The primary analytics destination, written through load jobs or the Storage Write API and queried with SQL. Multipart uploads is specific to Amazon S3 and BigQuery tables to Google Cloud Platform — each maps to any object or custom field on the other side.
Buckets Top-level, region-scoped containers that hold objects; enumerated to discover the namespaces and prefixes a sync should cover. Cloud SQL databases Managed Postgres, MySQL, and SQL Server instances synced like ordinary relational databases. Buckets is specific to Amazon S3 and Cloud SQL databases to Google Cloud Platform — each maps to any object or custom field on the other side.
Objects Files stored under a key; content is read with GET and written with PUT, and each object's key/size/ETag/LastModified is the unit indexed into a database. Cloud Storage objects Staging area for file-based bulk loads into BigQuery and other services. Objects is specific to Amazon S3 and Cloud Storage objects to Google Cloud Platform — each maps to any object or custom field on the other side.

How changes propagate between Amazon S3 and Google Cloud Platform

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Amazon S3 Google Cloud Platform Sub-second propagation

DetectionAmazon S3 notifies Stacksync of record changes through webhook events. S3 Event Notifications push object-created, object-removed, and object-tagging events to SNS, SQS, Lambda, or EventBridge.

DeliveryEach detected change is applied to Google Cloud Platform as a row-level write, with types converted between the two schemas.

Google Cloud Platform Amazon S3 Sub-second propagation

DetectionGoogle Cloud Platform pushes changes as they happen — webhook events backed by change data capture. Varies by service: log-based CDC on Cloud SQL (logical replication or binlog, also via Datastream), Pub/Sub for event delivery, polling for BigQuery.

DeliveryEach detected change is written to Amazon S3 through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Amazon S3: S3 sustains at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second per partitioned prefix and scales higher automatically; bursts can return HTTP 503 SlowDown while it repartitions.
  • Google Cloud Platform: Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits.
What ships with Amazon S3 ⇄ Google Cloud Platform

Connect Amazon S3 and Google Cloud Platform for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon S3–Google Cloud Platform connection.

Real-time

Two-way sync

Changes in Amazon S3 or Google Cloud Platform instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon S3 or Google Cloud Platform data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single Amazon S3 or Google Cloud Platform record.

Observability

Monitoring

Track your Amazon S3 ⇄ Google Cloud Platform sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon S3 and Google Cloud Platform.

How the Amazon S3 and Google Cloud Platform connectors work

Amazon S3

Integration surface
S3 REST API (also via AWS SDKs and the S3-compatible endpoint)
Authentication
AWS IAM credentials — an access key ID and secret access key signed with AWS Signature Version 4; supports temporary STS credentials and cross-account IAM roles
Change detection
S3 Event Notifications push object-created, object-removed, and object-tagging events to SNS, SQS, Lambda, or EventBridge; there is no modified-since query, so polling relies on each object's LastModified from ListObjectsV2
Capabilities
read · write · webhooks
Rate limits
S3 sustains at least 3,500 PUT/COPY/POST/DELETE and 5,500 GET/HEAD requests per second per partitioned prefix and scales higher automatically; bursts can return HTTP 503 SlowDown while it repartitions.

Google Cloud Platform

Integration surface
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
Change detection
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
Capabilities
read · write · CDC · webhooks
Rate limits
Quotas are set per service and per project; BigQuery, Pub/Sub, and Cloud SQL each enforce their own limits
How it works

How to connect Amazon S3 to Google Cloud Platform — three steps, no code

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.

  1. 01

    Connect your apps

    Authenticate Amazon S3 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Amazon S3 connected
    Google Cloud Platform connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon S3 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Amazon S3 ⇄ Google Cloud Platform
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Amazon S3 Google Cloud Platform
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon S3 and Google Cloud Platform integration FAQ

SECURITY

Security teams trust Stacksync

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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 530 integrations available for Amazon S3 and Google Cloud Platform.

Popular · 7 of 530
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