Real-time
Two-way sync
Changes in Databricks or Google Pubsub instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Developer tools
Databricks to Google Pubsub integration — real-time, two-way sync
Keep Databricks 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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Adopted by fast-scaling companies moving mission-critical data in real time
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Why teams connect Databricks and Google Pubsub
Close the gap between analytics and operations: Databricks 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.
Databricks is the central store where teams keep Volumes, SQL Warehouses, Change Data Feed, Catalogs 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 Snapshots, Message attributes, Dead-letter topics, Ordering keys produced in Google Pubsub are exactly what analysts want to measure in Databricks, and the curated rows in Databricks 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 Volumes, SQL Warehouses, Change Data Feed, Catalogs in Databricks with Snapshots, Message attributes, Dead-letter topics, Ordering keys 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 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- 03 Consume messages from a subscription via StreamingPull and write them into a Postgres or warehouse table for durable, SQL-queryable storage.
- 04 Fan out CRM record changes to a topic and let billing, analytics, and notification subscriptions each react without coupling to the source.
Common sync patterns
Operational data lands in Databricks for analytics
Records created in Google Pubsub — issues, events, messages, metrics, or user changes — replicate into Databricks 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 Databricks creates or updates the matching record in Google Pubsub, so the operational tool acts on the same data the analysts already see.
Backfill history, then stay live
Load the existing set of Snapshots, Message attributes, Dead-letter topics, Ordering keys into Databricks once, then keep it current with every change — you get full history plus real-time updates without a separate pipeline.
What you can sync between Databricks 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.
| Databricks objects | Google Pubsub objects | How this pairing syncs | |
|---|---|---|---|
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Schemas Avro or Protocol Buffer definitions bound to a topic; validate that every published message matches the agreed structure. | Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Message attributes Up to 100 key-value pairs per message; carry routing metadata and drive subscription filter expressions. | Views is specific to Databricks and Message attributes to Google Pubsub — each maps to any object or custom field on the other side. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Dead-letter topics Destination for messages that exceed a subscription's max delivery attempts; isolates poison messages for later handling. | Materialized Views is specific to Databricks and Dead-letter topics to Google Pubsub — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | 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. | Volumes is specific to Databricks and Ordering keys to Google Pubsub — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Topics Named resource publishers send to; Stacksync publishes each record change as a message to a topic for downstream subscribers to consume. | SQL Warehouses is specific to Databricks and Topics to Google Pubsub — each maps to any object or custom field on the other side. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Subscriptions A stream of messages from one topic; Stacksync consumes here via StreamingPull or a push endpoint, acknowledging each message after a successful write. | Change Data Feed is specific to Databricks and Subscriptions to Google Pubsub — each maps to any object or custom field on the other side. |
How changes propagate between Databricks 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.
Databricks Google Pubsub Sub-second propagation
DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.
DeliveryEach detected change is written to Google Pubsub through its API, with automatic retries and rate-limit backoff.
Google Pubsub Databricks 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 Databricks as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
- 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 Databricks ⇄ Google Pubsub
Connect Databricks and Google Pubsub for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Google Pubsub connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks 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 Databricks or Google Pubsub record.
Observability
Monitoring
Track your Databricks ⇄ 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 Databricks and Google Pubsub.
How the Databricks and Google Pubsub connectors work
Databricks
- Integration surface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Personal access tokens or OAuth machine-to-machine credentials for service principals
- Change detection
- Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
- Capabilities
- read · write · CDC
- Rate limits
- Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
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 Databricks 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.
- 01
Connect your apps
Authenticate Databricks 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.
Databricks
connected
Google Pubsub
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks 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.
objects · Databricks ⇄ Google PubsubCustomers 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.
Databricks Google PubsubCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Google Pubsub integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Google Pubsub: authenticate both systems, choose the objects to sync (such as Databricks's Schemas and Views), 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 Databricks and Google Pubsub. 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 Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. 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. 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 Databricks side: Volumes, SQL Warehouses, Change Data Feed, Catalogs, plus custom fields where Databricks exposes them. On the Google Pubsub side: Snapshots, Message attributes, Dead-letter topics, Ordering keys. 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 Databricks and Google Pubsub: Operational data lands in Databricks for analytics; Warehouse signals reach Google Pubsub; Backfill history, then stay live. Records created in Google Pubsub — issues, events, messages, metrics, or user changes — replicate into Databricks tables as they happen, so reporting runs on current data instead of last night's export.
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 362 integrations available for Databricks and Google Pubsub.
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