Real-time
Two-way sync
Changes in Databricks or Eloqua instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Marketing
Databricks to Eloqua integration — real-time, two-way sync
Keep Databricks and Eloqua 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 Eloqua
Put modeled data to work and measure what it drives: Databricks and Eloqua keep contacts, audiences, and campaign results in step in real time, in both directions.
Databricks is where your team models customers, product usage, and revenue into trusted tables; Eloqua runs the campaigns, audiences, and messages that reach those people. The two overlap wherever the same person, account, or segment matters to both, and when the bridge between them is a nightly export or a hand-built list, marketing targets stale data while analytics never sees what the campaign returned.
Stacksync syncs Schemas, Delta Tables, Views, Materialized Views in Databricks with Custom Data Objects, Activities, Campaigns, Emails in Eloqua field by field, in real time, and in both directions. You decide which system owns which fields — a computed score or segment can flow out to Eloqua while sends, opens, and conversions flow back to Databricks — and Stacksync keeps every copy consistent and resolves conflicts by rules you set.
Common use cases
- 01 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- 02 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
- 03 Two-way sync Contacts between Eloqua and a Postgres or warehouse table, matched on email, so ops teams query and update marketing profiles in SQL while marketers keep working in Eloqua.
- 04 Export email opens, clicks, form submits, and bounces from the Bulk API Activities export into a warehouse for lead scoring and multi-touch attribution.
Common sync patterns
Keep the contact and audience list current
New and updated contacts, leads, or audience members flow between Databricks and Eloqua, so the marketing audience reflects the people in your warehouse and corrections propagate instead of the two sides drifting apart.
Suppression and consent stay aligned
Unsubscribes, bounces, and consent or opt-out flags held in either system propagate to the other, so no one is messaged after opting out and Databricks holds the current state for auditing.
Enrich records with warehouse context
Product-usage counts, plan tier, region, or account owner computed in Databricks appear on the matching record in Eloqua, so targeting, routing, and personalization use up-to-date context.
What you can sync between Databricks and Eloqua
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 | Eloqua objects | How this pairing syncs | |
|---|---|---|---|
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Campaigns Multi-step campaign-canvas assets managed through the Application REST API; campaign membership and response data read out for attribution reporting. | Catalogs is specific to Databricks and Campaigns to Eloqua — each maps to any object or custom field on the other side. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Emails Email creative and email-group assets managed via the Application REST API; usually read for reporting rather than written by a sync. | Schemas is specific to Databricks and Emails to Eloqua — each maps to any object or custom field on the other side. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Forms and Form Submits Form definitions and their submission data, accessed through the Application REST API; submissions read out into a warehouse for lead capture and analysis. | Delta Tables is specific to Databricks and Forms and Form Submits to Eloqua — each maps to any object or custom field on the other side. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Contact Lists and Segments Shared lists (static) and segments (dynamic audiences); Contacts are added to or removed from shared lists based on lifecycle stage computed downstream. | Views is specific to Databricks and Contact Lists and Segments to Eloqua — 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. | Contacts Core person records, keyed on email address as the unique identifier; imported/exported via the Bulk API and upserted on email, synced two-way with CRM, warehouse, and app databases. | Materialized Views is specific to Databricks and Contacts to Eloqua — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Accounts Company records linked to Contacts via the account-linkage field; moved through the Bulk API and typically mastered in a CRM or ERP and written into Eloqua. | Volumes is specific to Databricks and Accounts to Eloqua — each maps to any object or custom field on the other side. |
How changes propagate between Databricks and Eloqua
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 Eloqua 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 Eloqua through its API, with automatic retries and rate-limit backoff.
Eloqua Databricks Interval-based propagation
DetectionStacksync polls Eloqua for changes on an incremental schedule, reading only records changed since the previous pass. Polling via Bulk API exports filtered on a contact/account modified-date field using EML filter syntax.
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.
- Eloqua: Per-instance soft limits by sync type (Export 2,000/hr, Import 2,000/hr, Sync Action 4,000/hr); 32 MB cap per POST to a staging area and 50,000 records per retrieve batch.
What ships with Databricks ⇄ Eloqua
Connect Databricks and Eloqua for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Eloqua connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Eloqua 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 Eloqua record.
Observability
Monitoring
Track your Databricks ⇄ Eloqua 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 Eloqua.
How the Databricks and Eloqua 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
Eloqua
- Integration surface
- Bulk API 2.0 (contacts, accounts, custom data objects, activities) plus Application REST API (campaigns, emails, forms, lists)
- Authentication
- OAuth 2.0 (Authorization Code or Resource Owner Password Credentials grant; Client Credentials not supported) or HTTP Basic Auth with siteName + username + password; the instance base URL must first be discovered via the login.eloqua.com/id endpoint
- Change detection
- Polling via Bulk API exports filtered on a contact/account modified-date field using EML filter syntax; no general-purpose record-change webhook
- Capabilities
- read · write
- Rate limits
- Per-instance soft limits by sync type (Export 2,000/hr, Import 2,000/hr, Sync Action 4,000/hr); 32 MB cap per POST to a staging area and 50,000 records per retrieve batch
How it works
How to connect Databricks to Eloqua — 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 Eloqua 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
Eloqua
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Eloqua 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 ⇄ EloquaCustomers 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 EloquaCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Eloqua integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Eloqua: authenticate both systems, choose the objects to sync (such as Databricks's Catalogs and Schemas), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Eloqua: Polling via Bulk API exports filtered on a contact/account modified-date field using EML filter syntax; no general-purpose record-change webhook. 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: Schemas, Delta Tables, Views, Materialized Views, plus custom fields where Databricks exposes them. On the Eloqua side: Custom Data Objects, Activities, Campaigns, Emails. 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.
-
Common patterns for Databricks and Eloqua: Keep the contact and audience list current; Suppression and consent stay aligned; Enrich records with warehouse context. New and updated contacts, leads, or audience members flow between Databricks and Eloqua, so the marketing audience reflects the people in your warehouse and corrections propagate instead of the two sides drifting apart.
-
Databricks: 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. Eloqua: Bulk API 2.0 (contacts, accounts, custom data objects, activities) plus Application REST API (campaigns, emails, forms, lists). Authentication: OAuth 2.0 (Authorization Code or Resource Owner Password Credentials grant; Client Credentials not supported) or HTTP Basic Auth with siteName + username + password; the instance base URL must first be discovered via the login.eloqua.com/id endpoint. Stacksync manages authentication, retries, and rate limits on both sides.
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 510 integrations available for Databricks and Eloqua.
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