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Database ⇄ Data warehouse

AWS Aurora PostgreSQL to BigQuery integration — real-time, two-way sync

Keep AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL and BigQuery

Connect AWS Aurora PostgreSQL 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.

Teams connect AWS Aurora PostgreSQL to BigQuery to run analytics on operational data without loading the production database. Aurora PostgreSQL Tables and Rows replicate into BigQuery Datasets, where Partitioned tables and Clustered tables keep large-scale queries efficient.

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

Common use cases

  • 01 Query months of Aurora PostgreSQL Row history in BigQuery while the operational database stays lean.
  • 02 Preserve Primary keys and constraints from Aurora PostgreSQL as merge keys for deduplicated BigQuery Tables.
  • 03 Separate analytics by team using BigQuery Datasets and Projects fed from one Aurora PostgreSQL source.
  • 04 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources

Common sync patterns

Warehouse replication

Aurora PostgreSQL Tables and Rows sync into BigQuery Tables organized by Dataset and Project.

Partitioned analytics loading

high-volume Aurora PostgreSQL tables land as BigQuery Partitioned tables and Clustered tables for cost-controlled querying.

Reporting layer sync

Aurora PostgreSQL Views and materialized views are materialized as BigQuery Tables for BI tools.

What you can sync between AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL objects BigQuery objects How this pairing syncs
Tables The core sync unit; rows are matched across systems by primary key. 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 and materialized views Usable as read-only sources for filtered or precomputed sync datasets. Clustered tables Supported; clustering is transparent to the sync. Views and materialized views is specific to AWS Aurora PostgreSQL and Clustered tables to BigQuery — each maps to any object or custom field on the other side.
Foreign keys Relationship metadata that syncs can translate into object references elsewhere. Datasets Organizational container — you pick which dataset’s tables to sync. Foreign keys is specific to AWS Aurora PostgreSQL and Datasets to BigQuery — each maps to any object or custom field on the other side.
Replication slots and publications The logical replication objects that power log-based CDC. Projects Connection scope: the service account grants access per project. Replication slots and publications is specific to AWS Aurora PostgreSQL and Projects to BigQuery — each maps to any object or custom field on the other side.
Databases and schemas PostgreSQL's two-level namespace scopes which tables a sync connection targets. Partitioned tables Synced like regular tables; partition columns map to target fields. Databases and schemas is specific to AWS Aurora PostgreSQL and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between AWS Aurora PostgreSQL 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.

AWS Aurora PostgreSQL BigQuery Sub-second propagation

DetectionChanges in AWS Aurora PostgreSQL are captured at the source via change data capture — no polling loop against its API. Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback.

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

BigQuery AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • 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 AWS Aurora PostgreSQL ⇄ BigQuery

Connect AWS Aurora PostgreSQL and BigQuery for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every AWS Aurora PostgreSQL–BigQuery connection.

Real-time

Two-way sync

Changes in AWS Aurora PostgreSQL or BigQuery instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL or BigQuery record.

Observability

Monitoring

Track your AWS Aurora PostgreSQL ⇄ BigQuery sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between AWS Aurora PostgreSQL and BigQuery.

How the AWS Aurora PostgreSQL and BigQuery connectors work

AWS Aurora PostgreSQL

Integration surface
SQL wire protocol (PostgreSQL-compatible), standard Postgres drivers and JDBC
Authentication
Database credentials, optionally AWS IAM database authentication, over TLS
Change detection
Log-based CDC via PostgreSQL logical replication (WAL decoding through replication slots), with timestamp polling as a fallback
Capabilities
read · write · CDC

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 AWS Aurora PostgreSQL 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 AWS Aurora PostgreSQL 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
    AWS Aurora PostgreSQL connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the AWS Aurora PostgreSQL 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 · AWS Aurora PostgreSQL ⇄ 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
    AWS Aurora PostgreSQL BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

AWS Aurora PostgreSQL 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
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 488 integrations available for AWS Aurora PostgreSQL and BigQuery.

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