Skip to content
Data warehouse ⇄ Marketing

Databricks to Iterable integration — real-time, two-way sync

Keep Databricks and Iterable in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Databricks and Iterable

Put modeled data to work and measure what it drives: Databricks and Iterable 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; Iterable 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 Delta Tables, Views, Materialized Views, Volumes in Databricks with Campaigns, Templates, Lists, Catalogs in Iterable 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 Iterable 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 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 02 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 03 Two-way sync User profiles and their custom data fields between Iterable and a Postgres database or CRM so attributes and subscription state stay aligned in both systems.
  • 04 Push warehouse or CRM audience segments into Iterable as Users via bulkUpdate (up to 1000 per call) and subscribe them to static Lists to drive campaigns.

Common sync patterns

Keep the contact and audience list current

New and updated contacts, leads, or audience members flow between Databricks and Iterable, 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 Iterable, so targeting, routing, and personalization use up-to-date context.

What you can sync between Databricks and Iterable

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 Iterable objects How this pairing syncs
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Catalogs Named catalogs of items (products, content) used for personalization and recommendations; items upserted and read via /api/catalogs/{catalogName}/items. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
SQL Warehouses The compute endpoint a sync connects to for query execution. Export data Historical user and event records pulled through the Export API (/api/export/data.json, data.csv, and userEvents) across data types like emailSend, emailOpen, emailClick, emailBounce, purchase, and customEvent. SQL Warehouses is specific to Databricks and Export data to Iterable — 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. Users User profiles keyed by email or userId with custom data fields; upserted via POST /api/users/update, read via GET /api/users/{email} or getByUserId, bulk-written via /api/users/bulkUpdate (up to 1000 users per call), and deleted or GDPR-forgotten. Change Data Feed is specific to Databricks and Users to Iterable — 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. Events Custom and system events tracked via /api/events/track and /api/events/trackBulk (up to 1000 events per call); a single user's event history is read via GET /api/events/{email}. Schemas is specific to Databricks and Events to Iterable — 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. Campaigns Email, SMS, push, and in-app sends; metadata and metrics read via GET /api/campaigns and /api/campaigns/metrics, created and sent via /api/campaigns/create and /api/campaigns/trigger. Delta Tables is specific to Databricks and Campaigns to Iterable — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Templates Reusable email/SMS/push/in-app message templates with handlebars fields; read via /api/templates and per-channel get endpoints, written via /api/templates/email/upsert and the other channel upserts. Views is specific to Databricks and Templates to Iterable — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Iterable

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 Iterable 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 Iterable through its API, with automatic retries and rate-limit backoff.

Iterable Databricks Sub-second propagation

DetectionIterable notifies Stacksync of record changes through webhook events. System Webhooks push email/SMS/push/in-app and custom events (send, open, click, bounce, complaint, unsubscribe) as JSON POSTs in near real time.

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.
  • Iterable: Limits are per endpoint and per project/key; exceeding one returns HTTP 429, so exponential backoff is advised. Event tracking allows far higher volume than metadata endpoints like campaigns/templates, and the Export API is throttled more tightly. Since Nov 10, 2025, passing the key in the query string or body is rate-limited more strictly than the Api-Key header.
What ships with Databricks ⇄ Iterable

Connect Databricks and Iterable for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Iterable connection.

Real-time

Two-way sync

Changes in Databricks or Iterable instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Iterable 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 Iterable record.

Observability

Monitoring

Track your Databricks ⇄ Iterable 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 Iterable.

How the Databricks and Iterable 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

Iterable

Integration surface
Iterable REST API (JSON over HTTPS): Users, Events, Campaigns, Templates, Lists, Catalogs, and Commerce endpoints, plus a bulk Export API for historical data
Authentication
API key sent in the Api-Key HTTP header (also accepted as Api_Key; the name is case-insensitive). Keys are scoped by type - Server-side, JavaScript (Web SDK), or Mobile - with optional JWT-enabled keys. US projects use api.iterable.com; EU projects use api.eu.iterable.com.
Change detection
System Webhooks push email/SMS/push/in-app and custom events (send, open, click, bounce, complaint, unsubscribe) as JSON POSTs in near real time; historical backfill and incremental catch-up run through the Export API over a date range.
Capabilities
read · write · webhooks
Rate limits
Limits are per endpoint and per project/key; exceeding one returns HTTP 429, so exponential backoff is advised. Event tracking allows far higher volume than metadata endpoints like campaigns/templates, and the Export API is throttled more tightly. Since Nov 10, 2025, passing the key in the query string or body is rate-limited more strictly than the Api-Key header.
How it works

How to connect Databricks to Iterable — 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 Databricks and Iterable 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
    Databricks connected
    Iterable connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Iterable 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 · Databricks ⇄ Iterable
    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
    Databricks Iterable
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Databricks and Iterable 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.

Learn more about security
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:

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 510 integrations available for Databricks and Iterable.

Popular · 8 of 510
All Databricks integrations All Iterable integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own