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Data warehouse

BigQuery to Starburst Enterprise integration — real-time, two-way sync

Keep BigQuery and Starburst Enterprise 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 Starburst Enterprise

Keep tables consistent across BigQuery and Starburst Enterprise, for a migration, a multi-warehouse stack, or a dataset two platforms both need.

Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.

Stacksync syncs tables between BigQuery and Starburst Enterprise continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.

Common use cases

  • 01 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 02 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 03 Read federated views that join warehouse, lake, and database tables, then sync the result into operational tools like a CRM
  • 04 Expose Iceberg or Hive lake tables to sync jobs through one SQL endpoint without moving the data first

Common sync patterns

Serve tools that only connect to one platform

Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

What you can sync between BigQuery and Starburst Enterprise

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 Starburst Enterprise objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Tables Queryable relations; writes pass through to sources whose connectors support them. 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. Columns Typed per the Trino type system, mapped from each source's native types. Datasets is specific to BigQuery and Columns to Starburst Enterprise — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Catalogs Each catalog maps to a connector (Iceberg, Hive, PostgreSQL, and others) exposing an external source. Projects is specific to BigQuery and Catalogs to Starburst Enterprise — 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 within a catalog, mirroring the underlying source's databases or schemas. Partitioned tables is specific to BigQuery and Schemas to Starburst Enterprise — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Views Engine-level SQL views used to shape federated data before syncing it out. Clustered tables is specific to BigQuery and Views to Starburst Enterprise — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Starburst Enterprise

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

Starburst Enterprise BigQuery Interval-based propagation

DetectionStacksync polls Starburst Enterprise for changes on an incremental schedule, reading only records changed since the previous pass. Query-based polling.

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.
  • Starburst Enterprise: Throughput is governed by cluster sizing and resource groups rather than API quotas.
What ships with BigQuery ⇄ Starburst Enterprise

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Starburst Enterprise 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 Starburst Enterprise record.

Observability

Monitoring

Track your BigQuery ⇄ Starburst Enterprise 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 Starburst Enterprise.

How the BigQuery and Starburst Enterprise 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

Starburst Enterprise

Integration surface
ANSI SQL over JDBC/ODBC drivers and the Trino client REST protocol
Authentication
Deployment-dependent: username/password, LDAP, OAuth 2.0, or Kerberos
Change detection
Query-based polling; Starburst is a query engine and exposes no change log of its own
Capabilities
read · write
Rate limits
Throughput is governed by cluster sizing and resource groups rather than API quotas
How it works

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

    Choose tables

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

BigQuery and Starburst Enterprise 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 472 integrations available for BigQuery and Starburst Enterprise.

Popular · 5 of 472
All BigQuery integrations All Starburst Enterprise integrations Browse all integrations
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