Real-time
Two-way sync
Changes in Databricks or Gmail instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Communications
Databricks to Gmail integration — real-time, two-way sync
Keep Databricks and Gmail 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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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Databricks and Gmail
Land the messages, calls, and events from Gmail in Databricks as live tables, and write results back, without building or maintaining a pipeline.
Gmail produces a constant stream of activity — messages sent and received, calls placed and answered, meetings held, and the delivery and engagement events attached to them. That record is what the rest of the company wants to analyze, and it usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay to get it out.
Stacksync syncs Labels, Drafts, Attachments, History from Gmail into tables in Databricks in real time, handling schema, rate limits, and retries. Because the connection works in both directions, results computed in Databricks — segments, contact updates, suppression flags — can be written back into fields in Gmail wherever it exposes them, so analysis lands where outreach actually happens.
Common use cases
- 01 Read Attachments and message metadata into a warehouse or document store for retention, discovery, and compliance reporting.
- 02 Mirror Labels with pipeline or ticket states so triage done in an external tool is reflected inside the inbox.
- 03 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
Common sync patterns
Queryable history that outlives retention
A continuously synced copy in Databricks preserves messages, call logs, and events for reporting and audit even as they age out of Gmail or get purged inside it.
Communications analytics without ETL
Messages, calls, and events from Gmail arrive in Databricks as queryable tables, current within seconds instead of a day behind.
Engagement and delivery on live data
Sends, opens, clicks, bounces, and call outcomes from Gmail land in Databricks as they happen, so deliverability and response monitoring stop lagging the reality they describe.
What you can sync between Databricks and Gmail
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 | Gmail objects | How this pairing syncs | |
|---|---|---|---|
| Views Curated read-only projections used as sync sources for downstream tools. | Threads Conversation groupings that keep replies together; synced so a CRM timeline or support desk shows the full exchange, not isolated messages. | Views is specific to Databricks and Threads to Gmail — 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. | Labels User and system labels (INBOX, SENT, custom); applied and removed via sync to mirror pipeline stages or triage states from an external system. | Materialized Views is specific to Databricks and Labels to Gmail — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Drafts Unsent messages; created and updated from templates or sequence tools so reps review before sending from their own mailbox. | Volumes is specific to Databricks and Drafts to Gmail — each maps to any object or custom field on the other side. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Attachments File payloads referenced by message part; read out to storage or a document system for archival and compliance workflows. | SQL Warehouses is specific to Databricks and Attachments to Gmail — 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. | History The incremental change log keyed by historyId; used for efficient delta sync so only mailbox changes since the last cursor are fetched. | Change Data Feed is specific to Databricks and History to Gmail — each maps to any object or custom field on the other side. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Messages Individual emails with headers, body, and label state; read out to a CRM or warehouse for activity logging, and sent or modified two-way from sales tools. | Catalogs is specific to Databricks and Messages to Gmail — each maps to any object or custom field on the other side. |
How changes propagate between Databricks and Gmail
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 Gmail 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 Gmail through its API, with automatic retries and rate-limit backoff.
Gmail Databricks Sub-second propagation
DetectionGmail notifies Stacksync of record changes through webhook events. Incremental sync via users.history.list from a stored historyId cursor, plus real-time push notifications through Google Cloud Pub/Sub watch on the.
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.
- Gmail: Quota is measured in units per user per second (e.g. messages.list = 5 units, messages.send = 100 units) against a 250 quota-unit/user/second limit and a daily project cap; exceeding limits returns 429 with exponential backoff expected.
What ships with Databricks ⇄ Gmail
Connect Databricks and Gmail for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Gmail connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Gmail 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 Gmail record.
Observability
Monitoring
Track your Databricks ⇄ Gmail 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 Gmail.
How the Databricks and Gmail 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
Gmail
- Integration surface
- Gmail API (REST, v1)
- Authentication
- OAuth 2.0 via Google — user-authorized consent with granular scopes (gmail.readonly, gmail.modify, gmail.send, gmail.labels); restricted scopes require Google's app verification for production use
- Change detection
- Incremental sync via users.history.list from a stored historyId cursor, plus real-time push notifications through Google Cloud Pub/Sub watch on the mailbox (watch must be renewed at least every 7 days)
- Capabilities
- read · write · webhooks
- Rate limits
- Quota is measured in units per user per second (e.g. messages.list = 5 units, messages.send = 100 units) against a 250 quota-unit/user/second limit and a daily project cap; exceeding limits returns 429 with exponential backoff expected.
How it works
How to connect Databricks to Gmail — 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 Gmail 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
Gmail
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Gmail 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 ⇄ GmailCustomers 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 GmailCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Gmail integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Gmail: authenticate both systems, choose the objects to sync (such as Databricks's Views and Materialized Views), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Gmail: A mailbox watch expires after 7 days and must be re-issued; missing the renewal silently stops real-time updates until the next poll. Databricks: Delta Lake's Change Data Feed records row-level inserts, updates, and deletes, enabling incremental sync without full scans. Stacksync's field mapping accounts for these differences between Databricks and Gmail without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Databricks and Gmail records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Databricks and Gmail connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Databricks–Gmail integration in-house.
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Yes — Stacksync ships production-grade connectors for both Databricks and Gmail. 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 Gmail: Incremental sync via users.history.list from a stored historyId cursor, plus real-time push notifications through Google Cloud Pub/Sub watch on the mailbox (watch must be renewed at least every 7 days). Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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
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