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Database ⇄ Data warehouse

Amazon RDS to BigQuery integration — real-time, two-way sync

Keep Amazon RDS and BigQuery 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 RDS and BigQuery

Connect Amazon RDS and BigQuery with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.

Data teams sync Amazon RDS into BigQuery to analyze operational data at warehouse scale. RDS Tables and their Columns replicate into BigQuery Datasets, where Partitioned tables and Clustered tables keep large query workloads efficient without touching the production database.

Stacksync covers both directions with one connection. Tables or collections in Amazon RDS sync into BigQuery in real time, and result tables in BigQuery sync back into Amazon RDS, with schema and type mapping between the two systems handled for you.

Common use cases

  • 01 Run heavy analytical queries on Clustered tables in BigQuery instead of the RDS primary.
  • 02 Consolidate multiple RDS Schemas into one BigQuery Project for cross-database reporting.
  • 03 Preserve Primary and Unique Keys during replication so warehouse joins remain reliable.
  • 04 Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Common sync patterns

Operational analytics pipeline

RDS Tables replicate into BigQuery Datasets for BI and SQL analysis.

Partitioned history

high-volume RDS data lands in BigQuery Partitioned tables organized by load date.

Reverse ETL

modeled results in BigQuery write back into RDS Tables that applications read.

What you can sync between Amazon RDS and BigQuery

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 RDS objects BigQuery objects How this pairing syncs
Tables The core sync target; rows map to records in connected SaaS systems. Tables The syncable unit: only tables can be synced per the Stacksync docs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Views Read-side projections exposed to outbound syncs. Partitioned tables Synced like regular tables; partition columns map to target fields. Views is specific to Amazon RDS and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.
Columns Field-level mapping targets, typed per the underlying engine. Clustered tables Supported; clustering is transparent to the sync. Columns is specific to Amazon RDS and Clustered tables to BigQuery — each maps to any object or custom field on the other side.
Primary and Unique Keys Match keys for idempotent upserts. Datasets Organizational container — you pick which dataset’s tables to sync. Primary and Unique Keys is specific to Amazon RDS and Datasets to BigQuery — each maps to any object or custom field on the other side.
Read Replicas Low-impact read endpoints often used as the source side of a sync. Projects Connection scope: the service account grants access per project. Read Replicas is specific to Amazon RDS and Projects to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between Amazon RDS and BigQuery

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 RDS BigQuery Sub-second propagation

DetectionChanges in Amazon RDS are captured at the source via change data capture — no polling loop against its API. Engine-native log-based CDC: MySQL/MariaDB binlog, PostgreSQL logical replication, SQL Server CDC.

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

BigQuery Amazon RDS Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

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

Rate-limit considerations

  • Amazon RDS: No API rate limits; throughput depends on instance class, storage IOPS, and connection limits.
  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
What ships with Amazon RDS ⇄ BigQuery

Connect Amazon RDS and BigQuery for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Amazon RDS–BigQuery connection.

Real-time

Two-way sync

Changes in Amazon RDS or BigQuery instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Amazon RDS or BigQuery 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 RDS or BigQuery record.

Observability

Monitoring

Track your Amazon RDS ⇄ BigQuery sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Amazon RDS and BigQuery.

How the Amazon RDS and BigQuery connectors work

Amazon RDS

Integration surface
SQL wire protocol of the chosen engine (PostgreSQL, MySQL, MariaDB, SQL Server, Oracle)
Authentication
Database credentials over SSL/TLS, or IAM database authentication on supported engines
Change detection
Engine-native log-based CDC: MySQL/MariaDB binlog, PostgreSQL logical replication, SQL Server CDC; enabled through RDS parameter groups, with polling as a fallback
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput depends on instance class, storage IOPS, and connection limits

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide
How it works

How to connect Amazon RDS to BigQuery — 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 RDS and BigQuery 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 RDS connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Amazon RDS and BigQuery 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 RDS ⇄ BigQuery
    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 RDS BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Amazon RDS and BigQuery 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
CSA STAR
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 490 integrations available for Amazon RDS and BigQuery.

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