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Data warehouse ⇄ Database

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

Keep BigQuery and MySQL 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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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect BigQuery and MySQL

Connect MySQL 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 sync MySQL with BigQuery to run analytics on application data without loading the production database. MySQL Tables and Views replicate into partitioned BigQuery Tables keyed on Primary and Unique Keys, and JSON Columns are carried over so semi-structured application fields remain queryable.

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

Common use cases

  • 01 Run heavy dashboard queries on BigQuery clustered Tables instead of the MySQL primary.
  • 02 Merge several MySQL application Databases (Schemas) into one BigQuery Project for company-wide reporting.
  • 03 Track schema drift by syncing MySQL Columns and keys into a governed BigQuery Dataset.
  • 04 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources

Common sync patterns

Production offload

MySQL Tables replicate continuously into partitioned BigQuery Tables for reporting.

JSON field analytics

MySQL JSON Columns sync to BigQuery where they can be unnested and queried at scale.

Multi-schema consolidation

MySQL Databases (Schemas) map to BigQuery Datasets for cross-application joins.

What you can sync between BigQuery and MySQL

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 MySQL 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.
Projects Connection scope: the service account grants access per project. JSON Columns Validated semi-structured payloads for nested SaaS data. Projects is specific to BigQuery and JSON Columns to MySQL — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Stored Procedures Server-side logic that can post-process synced rows. Partitioned tables is specific to BigQuery and Stored Procedures to MySQL — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Triggers An alternative change-capture mechanism when binlog access is unavailable. Clustered tables is specific to BigQuery and Triggers to MySQL — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Databases (Schemas) Top-level namespaces that scope a sync's reads and writes. Datasets is specific to BigQuery and Databases (Schemas) to MySQL — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and MySQL

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

MySQL BigQuery Sub-second propagation

DetectionChanges in MySQL are captured at the source via change data capture — no polling loop against its API. Database triggers — Stacksync creates deterministic triggers for internal logging and syncing (requires log_bin_trust_function_creators=ON when.

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.
  • MySQL: No API rate limits; throughput is bounded by connection limits and server resources.
What ships with BigQuery ⇄ MySQL

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or MySQL 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 MySQL record.

Observability

Monitoring

Track your BigQuery ⇄ MySQL 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 MySQL.

How the BigQuery and MySQL 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

MySQL

Integration surface
SQL wire protocol (MySQL client/server protocol)
Authentication
Database credentials entered as a connection string or parameters, with optional SSL root certificate upload and optional SSH tunnel (SSH user + SSH host)
Change detection
Database triggers — Stacksync creates deterministic triggers for internal logging and syncing (requires log_bin_trust_function_creators=ON when binary logging is enabled)
Capabilities
read · write · CDC
Rate limits
No API rate limits; throughput is bounded by connection limits and server resources
MySQL setup guide
How it works

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

    Choose tables

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

BigQuery and MySQL 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 582 integrations available for BigQuery and MySQL.

Popular · 8 of 582
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