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Data warehouse ⇄ Database

BigQuery to Postgres Heroku integration — real-time, two-way sync

Keep BigQuery and Postgres Heroku 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 BigQuery and Postgres Heroku

Connect Postgres Heroku 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.

Operational databases and analytical warehouses want the same data at different moments. Analysts want Postgres Heroku's rows in BigQuery, 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 Postgres Heroku where the services that read from it get them at normal query latency.

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

Common use cases

  • 01 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 02 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • 03 Reflect billing and subscription records into the app database so product logic reads local rows
  • 04 Expose CRM objects as Postgres tables the Heroku application can query and join directly

Common sync patterns

Operational data in the warehouse, minus the pipeline

Rows from Postgres Heroku land in BigQuery as they change, replacing hand-built CDC and batch extract jobs.

Serve warehouse results at database speed

Aggregates or model outputs computed in BigQuery sync into Postgres Heroku, where whatever reads from that database gets them without querying the warehouse.

Fresh analytics without loading windows

Because changes stream continuously, analysts query current data instead of waiting for last night's load.

What you can sync between BigQuery and Postgres Heroku

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.

BigQuery objects Postgres Heroku objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Tables Standard Postgres tables; the primary two-way sync target for app data. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Datasets Organizational container — you pick which dataset’s tables to sync. Views Read-side projections exposed to outbound syncs. Datasets is specific to BigQuery and Views to Postgres Heroku — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Materialized Views Precomputed result sets synced outward on refresh. Projects is specific to BigQuery and Materialized Views to Postgres Heroku — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Schemas Namespaces that scope which tables a sync reads and writes. Partitioned tables is specific to BigQuery and Schemas to Postgres Heroku — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Primary and Unique Keys Match keys for idempotent upserts from connected systems. Clustered tables is specific to BigQuery and Primary and Unique Keys to Postgres Heroku — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Postgres Heroku

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.

BigQuery Postgres Heroku 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 Postgres Heroku as a row-level write, with types converted between the two schemas.

Postgres Heroku BigQuery Interval-based propagation

DetectionStacksync polls Postgres Heroku for changes on an incremental schedule, reading only records changed since the previous pass. Trigger-based capture or polling in most configurations.

DeliveryEach detected change is applied to BigQuery 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.
  • Postgres Heroku: No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan.
What ships with BigQuery ⇄ Postgres Heroku

Connect BigQuery and Postgres Heroku for flexible, real-time data sync.

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Postgres Heroku 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 BigQuery or Postgres Heroku record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Postgres Heroku.

How the BigQuery and Postgres Heroku connectors work

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

Postgres Heroku

Integration surface
SQL wire protocol (standard PostgreSQL)
Authentication
Database credentials from the Heroku DATABASE_URL config var; SSL required
Change detection
Trigger-based capture or polling in most configurations; log-based logical replication availability depends on plan and Heroku's managed server settings
Capabilities
read · write
Rate limits
No API rate limits; connection counts and performance are bounded by the Heroku Postgres plan
How it works

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

    Choose tables

    Pick the BigQuery and Postgres Heroku 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 · BigQuery ⇄ Postgres Heroku
    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
    BigQuery Postgres Heroku
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery and Postgres Heroku 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 484 integrations available for BigQuery and Postgres Heroku.

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