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Data warehouse ⇄ Database

BigQuery to Supabase integration — real-time, two-way sync

Keep BigQuery and Supabase 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 Supabase

Connect Supabase 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.

Product teams pair Supabase, the operational database backend, with BigQuery for analytics at scale: Supabase Tables, JSONB Columns, and auth.users feed BigQuery Datasets where product usage and user growth can be analyzed without loading the production database. Sync respects Supabase structure, including Schemas and Row Level Security Policies context.

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

Common use cases

  • 01 Analyze signups and user activity from auth.users without querying the production database.
  • 02 Keep BigQuery Clustered tables current with application data for ML and BI workloads.
  • 03 Report across Supabase Schemas and other warehouse sources in one BigQuery Project.
  • 04 Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Common sync patterns

Product analytics feed

Supabase Tables and auth.users replicate into BigQuery Partitioned tables for retention and growth analysis.

JSONB flattening for analysis

Supabase JSONB Columns sync into BigQuery Tables where nested data can be queried at scale.

View materialization

Supabase Views land in BigQuery Datasets so curated application data is available for warehouse joins.

What you can sync between BigQuery and Supabase

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 Supabase 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. 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. Schemas Namespaces (public and custom) that scope sync access. Datasets is specific to BigQuery and Schemas to Supabase — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. auth.users Managed authentication users, often mirrored into CRM or support systems. Projects is specific to BigQuery and auth.users to Supabase — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Row Level Security Policies Row-level access rules that govern what the REST layer exposes. Partitioned tables is specific to BigQuery and Row Level Security Policies to Supabase — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. JSONB Columns Semi-structured payloads such as event properties or nested objects. Clustered tables is specific to BigQuery and JSONB Columns to Supabase — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Supabase

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 Supabase 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 Supabase as a row-level write, with types converted between the two schemas.

Supabase BigQuery Sub-second propagation

DetectionSupabase pushes changes as they happen — webhook events backed by change data capture. Log-based CDC via Postgres logical replication, the same WAL feed that powers Supabase Realtime.

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.
  • Supabase: SQL access is bounded by connection limits (pooled connections are provided); the REST layer is subject to the platform's limits.
What ships with BigQuery ⇄ Supabase

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Supabase 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 Supabase record.

Observability

Monitoring

Track your BigQuery ⇄ Supabase 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 Supabase.

How the BigQuery and Supabase 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

Supabase

Integration surface
Direct PostgreSQL wire protocol connection, plus an auto-generated REST API (PostgREST)
Authentication
Database credentials (connection string) for SQL access; API keys (anon / service role) for the REST layer
Change detection
Log-based CDC via Postgres logical replication, the same WAL feed that powers Supabase Realtime; database webhooks can also fire on row changes
Capabilities
read · write · CDC · webhooks
Rate limits
SQL access is bounded by connection limits (pooled connections are provided); the REST layer is subject to the platform's limits
How it works

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

    Choose tables

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

BigQuery and Supabase 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 576 integrations available for BigQuery and Supabase.

Popular · 8 of 576
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