Real-time
Two-way sync
Changes in Google Pubsub or Snowflake instantly reflect in both systems. No stale data, no manual imports.
Developer tools ⇄ Data warehouse
Google Pubsub to Snowflake integration — real-time, two-way sync
Keep Google Pubsub and Snowflake 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 Google Pubsub and Snowflake
Close the gap between analytics and operations: Snowflake 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.
Snowflake is the central store where teams keep Streams, Stages, Tasks, VARIANT Columns 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 Snowflake, and the curated rows in Snowflake 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 Streams, Stages, Tasks, VARIANT Columns in Snowflake 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 Keep a customer 360 table aligned with its source systems in both directions instead of one-way reverse ETL
- 02 Push product usage aggregates from Snowflake into sales and success tools for account prioritization
- 03 Bridge order or billing events off a topic into an ERP or ticketing system as they arrive, mapping message attributes to target fields.
- 04 Attach an Avro or Protocol Buffer schema to a topic so every message Stacksync publishes is validated against an agreed contract.
Common sync patterns
Keep user and access records aligned
Where Google Pubsub manages users, directory, or access data, those records stay current in Snowflake — and can be provisioned back from it — so ownership and permissions match across both.
Operational data lands in Snowflake for analytics
Records created in Google Pubsub — issues, events, messages, metrics, or user changes — replicate into Snowflake 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 Snowflake 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 Google Pubsub and Snowflake
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.
| Google Pubsub objects | Snowflake objects | How this pairing syncs | |
|---|---|---|---|
| Schemas Avro or Protocol Buffer definitions bound to a topic; validate that every published message matches the agreed structure. | Schemas Namespaces within a database used to organize synced tables. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Dead-letter topics Destination for messages that exceed a subscription's max delivery attempts; isolates poison messages for later handling. | Stages File staging areas used for bulk loads into synced tables. | Dead-letter topics is specific to Google Pubsub and Stages to Snowflake — each maps to any object or custom field on the other side. | |
| Ordering keys Tag messages so those sharing a key deliver in publish order when message ordering is enabled; throughput capped at 1 MBps per key. | Tasks Scheduled SQL used to transform synced data after it lands. | Ordering keys is specific to Google Pubsub and Tasks to Snowflake — each maps to any object or custom field on the other side. | |
| Topics Named resource publishers send to; Stacksync publishes each record change as a message to a topic for downstream subscribers to consume. | VARIANT Columns Semi-structured JSON payloads stored alongside relational columns. | Topics is specific to Google Pubsub and VARIANT Columns to Snowflake — each maps to any object or custom field on the other side. | |
| Subscriptions A stream of messages from one topic; Stacksync consumes here via StreamingPull or a push endpoint, acknowledging each message after a successful write. | Virtual Warehouses The compute a sync's queries run on, sized independently of storage. | Subscriptions is specific to Google Pubsub and Virtual Warehouses to Snowflake — each maps to any object or custom field on the other side. | |
| Messages The synced unit: base64-encoded data plus attributes, ordering key, messageId and publishTime; capped at 10 MB each. | Databases Top-level containers that scope which data a sync can touch. | Messages is specific to Google Pubsub and Databases to Snowflake — each maps to any object or custom field on the other side. |
How changes propagate between Google Pubsub and Snowflake
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.
Google Pubsub Snowflake 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 Snowflake as a row-level write, with types converted between the two schemas.
Snowflake Google Pubsub Sub-second propagation
DetectionChanges in Snowflake are captured at the source via change data capture — no polling loop against its API. The setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism.
DeliveryEach detected change is written to Google Pubsub through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- 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.
- Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Google Pubsub ⇄ Snowflake
Connect Google Pubsub and Snowflake for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Google Pubsub–Snowflake connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Google Pubsub or Snowflake 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 Google Pubsub or Snowflake record.
Observability
Monitoring
Track your Google Pubsub ⇄ Snowflake sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Google Pubsub and Snowflake.
How the Google Pubsub and Snowflake connectors work
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
Snowflake
- Integration surface
- SQL via JDBC/ODBC and native drivers, plus the Snowflake SQL REST API
- Authentication
- Dedicated Snowflake service user + role with RSA key-pair authentication (Stacksync-provided public key), created via a setup script requiring SECURITY_ADMIN and ACCOUNTADMIN roles
- Change detection
- Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism
- Capabilities
- read · write · CDC
- Rate limits
- No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time
How it works
How to connect Google Pubsub to Snowflake — 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.
- 01
Connect your apps
Authenticate Google Pubsub and Snowflake with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Google Pubsub
connected
Snowflake
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Google Pubsub and Snowflake 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.
objects · Google Pubsub ⇄ SnowflakeCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Google Pubsub SnowflakeCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Google Pubsub and Snowflake integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Google Pubsub and Snowflake: authenticate both systems, choose the objects to sync (such as Google Pubsub's Schemas and Dead-letter topics), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Yes — Stacksync ships production-grade connectors for both Google Pubsub and Snowflake. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Google Pubsub: 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. On Snowflake: Not explicitly stated; the setup script grants "create stream" on synced schemas (Snowflake streams), but the docs do not name the change-capture mechanism. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Snowflake side: Streams, Stages, Tasks, VARIANT Columns, plus custom fields where Snowflake exposes them. On the Google Pubsub side: Dead-letter topics, Ordering keys, Topics, Subscriptions. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for Google Pubsub and Snowflake: Keep user and access records aligned; Operational data lands in Snowflake for analytics; Warehouse signals reach Google Pubsub. Where Google Pubsub manages users, directory, or access data, those records stay current in Snowflake — and can be provisioned back from it — so ownership and permissions match across both.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 359 integrations available for Google Pubsub and Snowflake.
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