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Data warehouse ⇄ Database

BigQuery to SQL Server integration — real-time, two-way sync

Keep BigQuery and SQL Server 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 SQL Server

Connect SQL Server 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 SQL Server and BigQuery to keep transactional data and analytical workloads in step: SQL Server Tables and Views hold the operational records, while BigQuery Datasets and Partitioned tables serve reporting and large-scale queries. A two-way sync means analysts query current data in BigQuery without ETL lag, and results written back land in the operational database.

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

Common use cases

  • 01 Offload heavy analytical queries from SQL Server to BigQuery while keeping both stores current.
  • 02 Consolidate multiple SQL Server Databases into one BigQuery Project for cross-database analysis.
  • 03 Retire nightly ETL jobs by replacing them with continuous table-level sync.
  • 04 Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Common sync patterns

Operational reporting pipeline

SQL Server Tables and Views replicate continuously into BigQuery Partitioned tables for analytics without batch ETL windows.

Schema-aware replication

SQL Server Schemas, Columns, and Primary and Unique Keys map onto BigQuery Datasets so table structures stay consistent as they evolve.

Write-back of computed results

aggregates computed in BigQuery Clustered tables sync back into designated SQL Server Tables for application use.

What you can sync between BigQuery and SQL Server

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 SQL Server objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Tables The primary sync target; rows map to records in connected systems. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Clustered tables Supported; clustering is transparent to the sync. Views Read-side projections used as outbound sync sources. Clustered tables is specific to BigQuery and Views to SQL Server — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Columns Field-level mapping targets with T-SQL types. Datasets is specific to BigQuery and Columns to SQL Server — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Primary and Unique Keys Match keys for idempotent upserts and conflict handling. Projects is specific to BigQuery and Primary and Unique Keys to SQL Server — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. CDC Change Tables System-populated tables holding captured inserts, updates, and deletes for consumers. Partitioned tables is specific to BigQuery and CDC Change Tables to SQL Server — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and SQL Server

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

SQL Server BigQuery Sub-second propagation

DetectionChanges in SQL Server are captured at the source via change data capture — no polling loop against its API. SQL Server Native Change Data Capture (CDC).

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.
  • SQL Server: No API rate limits; throughput depends on instance resources, licensing tier, and connection limits.
What ships with BigQuery ⇄ SQL Server

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or SQL Server 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 SQL Server record.

Observability

Monitoring

Track your BigQuery ⇄ SQL Server 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 SQL Server.

How the BigQuery and SQL Server 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

SQL Server

Integration surface
SQL over the TDS wire protocol (Tabular Data Stream), via ODBC/JDBC/ADO.NET drivers
Authentication
Database credentials entered as a connection string or as parameters (host/user/password) in the Create New Sync page
Change detection
SQL Server Native Change Data Capture (CDC); a DBA runs a one-time setup script with sysadmin privileges to enable CDC and create Stacksync wrapper procedures
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput depends on instance resources, licensing tier, and connection limits
SQL Server setup guide
How it works

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

    Choose tables

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

BigQuery and SQL Server 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 575 integrations available for BigQuery and SQL Server.

Popular · 8 of 575
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