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Data warehouse ⇄ Developer tools

BigQuery to Google Pubsub integration — real-time, two-way sync

Keep BigQuery and Google Pubsub 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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Why teams connect BigQuery and Google Pubsub

Close the gap between analytics and operations: BigQuery holds the record while Google Pubsub runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

BigQuery is the central store where teams keep Clustered tables, Datasets, Projects, Tables for reporting and analysis; Google Pubsub runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Dead-letter topics, Ordering keys, Topics, Subscriptions produced in Google Pubsub are exactly what analysts want to measure in BigQuery, and the curated rows in BigQuery are what should drive the next action in Google Pubsub. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.

Stacksync syncs Clustered tables, Datasets, Projects, Tables in BigQuery with Dead-letter topics, Ordering keys, Topics, Subscriptions in Google Pubsub field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.

Common use cases

  • 01 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 02 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • 03 Attach an Avro or Protocol Buffer schema to a topic so every message Stacksync publishes is validated against an agreed contract.
  • 04 Publish new and changed database rows as messages to a Pub/Sub topic so multiple downstream subscriptions consume the change stream independently.

Common sync patterns

Keep user and access records aligned

Where Google Pubsub manages users, directory, or access data, those records stay current in BigQuery — and can be provisioned back from it — so ownership and permissions match across both.

Operational data lands in BigQuery for analytics

Records created in Google Pubsub — issues, events, messages, metrics, or user changes — replicate into BigQuery tables as they happen, so reporting runs on current data instead of last night's export.

Warehouse signals reach Google Pubsub

A row scored, flagged, or enriched in BigQuery creates or updates the matching record in Google Pubsub, so the operational tool acts on the same data the analysts already see.

What you can sync between BigQuery and Google Pubsub

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 Google Pubsub objects How this pairing syncs
Datasets Organizational container — you pick which dataset’s tables to sync. Messages The synced unit: base64-encoded data plus attributes, ordering key, messageId and publishTime; capped at 10 MB each. Datasets is specific to BigQuery and Messages to Google Pubsub — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Schemas Avro or Protocol Buffer definitions bound to a topic; validate that every published message matches the agreed structure. Projects is specific to BigQuery and Schemas to Google Pubsub — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Snapshots Captured subscription state for seek/replay; lets already-acknowledged messages be redelivered from a point in time. Tables is specific to BigQuery and Snapshots to Google Pubsub — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Message attributes Up to 100 key-value pairs per message; carry routing metadata and drive subscription filter expressions. Partitioned tables is specific to BigQuery and Message attributes to Google Pubsub — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Dead-letter topics Destination for messages that exceed a subscription's max delivery attempts; isolates poison messages for later handling. Clustered tables is specific to BigQuery and Dead-letter topics to Google Pubsub — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Google Pubsub

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 Google Pubsub 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 written to Google Pubsub through its API, with automatic retries and rate-limit backoff.

Google Pubsub BigQuery Sub-second propagation

DetectionGoogle Pubsub notifies Stacksync of record changes through webhook events. Consumes messages as they arrive on a subscription — StreamingPull (long-lived gRPC) or a push subscription delivering each message as an HTTPS POST.

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.
  • Google Pubsub: Per-region throughput quotas (publish up to ~4 GB/s in large regions); 10 MB max per message and per publish request, 1,000 messages per request, and 1 MBps per ordering key.
What ships with BigQuery ⇄ Google Pubsub

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Google Pubsub 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 Google Pubsub record.

Observability

Monitoring

Track your BigQuery ⇄ Google Pubsub 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 Google Pubsub.

How the BigQuery and Google Pubsub 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

Google Pubsub

Integration surface
REST and gRPC (Cloud Pub/Sub API v1)
Authentication
Google Cloud IAM via OAuth 2.0 / service-account credentials (JSON key or workload identity); requires roles such as pubsub.publisher and pubsub.subscriber
Change detection
Consumes messages as they arrive on a subscription — StreamingPull (long-lived gRPC) or a push subscription delivering each message as an HTTPS POST; no modified-date polling
Capabilities
read · write · webhooks
Rate limits
Per-region throughput quotas (publish up to ~4 GB/s in large regions); 10 MB max per message and per publish request, 1,000 messages per request, and 1 MBps per ordering key
How it works

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

    Choose tables

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

BigQuery and Google Pubsub 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.

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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 354 integrations available for BigQuery and Google Pubsub.

Popular · 6 of 354
All BigQuery integrations All Google Pubsub integrations Browse all integrations
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