Can you integrate BigQuery with Supabase?

Explore a BigQuery and Supabase integration with a Stacksync engineer. Stacksync support for Supabase is not established by the connector documentation reviewed for this page. Start with one record and the update your business needs to identify an implementation path.

Can BigQuery and Supabase sync in both directions?

Confirm two-way support with Stacksync for the records and fields you need in both systems. Access to a vendor API does not confirm that its Stacksync connector supports write-back.

What should be ready before the BigQuery and Supabase initial load?

Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project. Identify the Supabase account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.

How fast does the BigQuery to Supabase sync run?

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.

Will existing duplicates merge automatically?

No. Stacksync documents that pre-existing duplicates are not merged automatically when two-way sync begins. Review the initial dataset and matching plan before enabling it; an empty destination can simplify the first load.

How do I map BigQuery fields to Supabase?

Start with one business entity and a stable record ID. Map a small set of editable fields with compatible types, test required values and relationships, then expand after the pilot passes.

What should I do if a record fails to sync?

Check the destination error, field constraints, permissions, and current source value. The Stacksync issues dashboard supports retry and revert; retry reads current source values, so verify the intended record state before acting.

What does a BigQuery and Supabase integration cost?

Use the current Stacksync pricing page and confirm the supported implementation with the team. Scope the required objects, record volume, update frequency, initial load, and support needs when comparing a managed connector with native or custom development.

Data warehouse / Database

BigQuery and Supabase integration

Plan how BigQuery and Supabase should share data across your business. Work with Stacksync engineers on record mapping, system access, and the requirements for running the integration.

Scope your workflow with an integration engineer
Review the systems, records, and updates you need

Integration planning

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BigQuery

Supabase

Review the integration scope

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Proposed workflow

Plan table mappings between BigQuery and Supabase

This is an evaluation scenario; connector and operation support require confirmation.

Starting eventA selected source table needs an operational replica, a reporting projection, or a migration copy.

Choose actual tables in BigQuery and Supabase and compare their schemas and record types.
Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.
Choose a one-way copy or two-way sync according to connector support and your update rules. Keep source views, aggregate outputs, and writable base tables distinct.

What to verifyCounts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.

Review records and field ownership

Proposed record relationships

Records to connect

Use these examples to define record matching and field ownership for your technical review.

Download the mapping worksheet

CSV · No email required

Choose the tables you need in BigQuery and Supabase, then map their keys, field types, and filters. Your schemas determine how records relate and which system should own each field. Use the worksheet to document those choices before testing the first load.

Record coverage to review

Use documented coverage where available. Catalog record types are starting points for review and do not confirm Stacksync support.

BigQuery

Read and write support varies by record

Record types covered in the setup guide

Record types	Coverage and requirements
Tables	See connector requirements. Confirm field permissions and sync direction.

Read the BigQuery connector guide

Supabase

Connection and object support require review

Record types to review with Stacksync

Record types	Coverage and requirements
Tables Views Schemas auth.users Row Level Security Policies JSONB Columns	Confirm support for this record type and the direction you need.

Discuss Supabase requirements

Connection essentials

Confirm Stacksync support and account requirements for undocumented connections. Interface information alone does not establish connector availability.

View setup requirements and limits

Connection requirement	BigQuery	Supabase
Integration interface	GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs	Direct PostgreSQL wire protocol connection, plus an auto-generated REST API (PostgREST)
Authentication	Dedicated Google Cloud service account and JSON key	Confirm the credentials, API plan, and permissions required for Supabase.
Change detection	The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.	Confirm how Stacksync detects changes for this connector and the objects you need.
Read access	Available for supported records	Confirm with Stacksync
Write access	Available for supported records	Confirm with Stacksync

Enterprise controls

Security and control for your integrations

Explore security controls

Compliance and data transfers

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.

SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH

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:

Record-level recovery

Inspect sync errors and use retry and revert controls to resolve failed updates.

Read the recovery guide

Implementation

Technical reference

Review setup, record relationships, testing, and recovery for your implementation.

Authentication, permissions and API limits

Connection requirements and limits

BigQuery

Integration interface: GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication: Dedicated Google Cloud service account and JSON key; enable the APIs and grant the roles in the authorization guide.
Change detection: The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.
Read access: Available for supported records
Write access: Available for supported records

Setup requirements

Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project.
Create a dedicated service account with the roles in the authorization guide and supply its credentials through Stacksync.

Limitations to check

Only tables are supported in the saved guide; ordinary and materialized views are excluded.
Google Cloud quotas and the selected write method affect capacity; validate the current project limits before a large backfill.

Technical documentation

Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.

BigQuery setup guide

Supabase

Integration interface: Direct PostgreSQL wire protocol connection, plus an auto-generated REST API (PostgREST)
Authentication: Confirm the credentials, API plan, and permissions required for Supabase.
Change detection: Confirm how Stacksync detects changes for this connector and the objects you need.
Read access: Confirm with Stacksync
Write access: Confirm with Stacksync

Setup requirements

Identify the Supabase account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Supabase, including read/write support, authentication, and initial-load limits.

Limitations to check

Confirm Stacksync support for Supabase and the record types your workflow needs.
Review write-back, deletion handling, update timing, and account limits with the integration team.

Prepare BigQuery and Supabase access

Set up both accounts before testing the mapping. Use test records where available, and identify the account administrator who can approve access and help resolve setup errors.

BigQuery setup checklist

Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project.
Create a dedicated service account with the roles in the authorization guide and supply its credentials through Stacksync.

Setup guides: Authorize BigQuery

Supabase setup checklist

Identify the Supabase account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Supabase, including read/write support, authentication, and initial-load limits.

Prepare to go live

Record the fields each system can update, the first-load cutoff, both record IDs, the expected update delay, and who handles errors. Complete the tests before production before expanding to more records.

Use the BigQuery and Supabase planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.

Talk to an engineer · Review current pricing

Record identity and field ownership

Use these data-model references to describe the records your connection needs. They are planning examples; connector availability and supported operations must be established before implementation.

Download the mapping worksheet · CSV, no email required

Compare integration approaches

Choose a method around one example record and the update your business needs. Define how the BigQuery source record should appear or trigger work in Supabase. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.

Stacksync managed sync

Best fit: Review compatibility with a Stacksync engineer using an example of the records and updates you need.
Operating responsibility: Fits ongoing record synchronization when the required operations are supported. Add workflow steps for approvals or business actions that go beyond copying fields.
Before you choose: Provide your table or object schemas and one example update; confirm the supported keys, fields, and direction.

Native vendor integration

Best fit: A vendor-built integration may fit if it supports your BigQuery and Supabase record types.
Operating responsibility: Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
Before you choose: First check whether either vendor offers this integration. If available, verify your record types, update direction, account tier, and related-record handling.

Custom API or workflow

Best fit: Consider when BigQuery and Supabase need a transformation, approval, or action outside a direct record sync.
Operating responsibility: Provides control over business steps; the team owns credentials, version changes, error queues, and reconciliation.
Before you choose: Verify endpoint permissions, pagination, quotas, duplicate detection, and failure recovery.

File or scheduled snapshot

Best fit: Consider for a one-time BigQuery / Supabase migration or a reporting need with an explicit freshness window.
Operating responsibility: Can simplify a bounded transfer; later changes and deletion history require another extraction or a separately designed incremental process.
Before you choose: Record the extraction cutoff, source IDs, encoding, date/number formats, and reconciliation totals.

Workflow scenarios and expected results

Plan table mappings between BigQuery and Supabase

This is an evaluation scenario; connector and operation support require confirmation.

Starting event: A selected source table needs an operational replica, a reporting projection, or a migration copy.

Choose actual tables in BigQuery and Supabase and compare their schemas and record types.
Specify the primary/business key, type conversions, filter boundaries, relationship dependencies, and the source of each writable field.
Choose a one-way copy or two-way sync according to connector support and your update rules. Keep source views, aggregate outputs, and writable base tables distinct.

Expected result: Counts reconcile within identical filters; updates preserve keys; precision, nulls, deletes, and schema changes follow the agreed mapping rules.

If it fails: Compare current source state, key mapping, and destination constraints before retrying. Reconcile the backlog after any schema or permission change.

Initial load and acceptance testing

Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.

Direction and permissions

Test case

Bring an example source record and the intended destination operation to the compatibility review. Confirm the supported route before granting write access.

Expected result

Only an approved, supported direction and permitted fields are written.

Schema, keys, and reconciliation

Test case

Use an actual BigQuery and Supabase table. Compare key uniqueness, nulls, decimal precision, timezone conversions, and counts within identical filters.

Expected result

The process meets its agreed freshness target and reconciliation has no unexplained differences.

Failed updates, retries and recovery

Start with the failed record and the destination error, then inspect the source value, field requirements, and access.

A record type or update is unavailable

Investigate

Check the BigQuery and Supabase connector guides, account permissions, and any operations marked On Request.

Next action

Ask the integration team to confirm a supported way to handle that record. Verify whether it needs connector configuration or a separate workflow step.

Source and destination disagree after a retry

Investigate

Compare current source values, destination validation, identity mappings, and any side effects already completed.

Next action

Stacksync issue retry reads the current source state. Decide the intended state before retrying or reverting; reconcile downstream effects separately.

Read the Stacksync issues dashboard guide for retry and revert behavior.

Change detection and update delivery

How updates move between BigQuery and Supabase

See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.

BigQuery Supabase Direction requires confirmation

Detect changesThe setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.

Apply updatesConfirm that Stacksync can create or update the records you need in Supabase.

Supabase BigQuery Direction requires confirmation

Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.

Apply updatesConfirm that Stacksync can create or update the records you need in BigQuery.

Update timing and record limits

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
Google Cloud quotas and the selected write method affect capacity; validate the current project limits before a large backfill.
Review write-back, deletion handling, update timing, and account limits with the integration team.

FAQ