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Marketing ⇄ Data warehouse

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

Keep Iterable 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 Iterable and Snowflake

Put modeled data to work and measure what it drives: Snowflake and Iterable keep contacts, audiences, and campaign results in step in real time, in both directions.

Snowflake 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 Databases, Schemas, Tables, Views in Snowflake with Users, Events, Campaigns, Templates 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 Snowflake — and Stacksync keeps every copy consistent and resolves conflicts by rules you set.

Common use cases

  • 01 Land CRM and ERP records in Snowflake continuously so BI reflects business systems without nightly batch ETL
  • 02 Activate modeled Snowflake tables by syncing scores and attributes back into CRM fields sales can act on
  • 03 Export Iterable engagement data (emailSend, emailOpen, emailClick, emailBounce, purchase) into a warehouse for deliverability and campaign reporting.
  • 04 Stream Iterable System Webhook events into a database in near real time so opens, clicks, bounces, and unsubscribes land alongside CRM contact records.

Common sync patterns

Activate a modeled audience

A segment or score built in Snowflake — high-intent accounts, churn risk, a lifetime-value tier — lands as an audience or contact field in Iterable, so campaigns target the people your data actually points to instead of a static export.

Keep the contact and audience list current

New and updated contacts, leads, or audience members flow between Snowflake 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 Snowflake holds the current state for auditing.

What you can sync between Iterable 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.

Iterable objects Snowflake objects How this pairing syncs
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. Virtual Warehouses The compute a sync's queries run on, sized independently of storage. Campaigns is specific to Iterable and Virtual Warehouses to Snowflake — each maps to any object or custom field on the other side.
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. Databases Top-level containers that scope which data a sync can touch. Templates is specific to Iterable and Databases to Snowflake — each maps to any object or custom field on the other side.
Lists Static subscriber lists; read via GET /api/lists and /api/lists/getUsers, with users added or removed via /api/lists/subscribe and /api/lists/unsubscribe to control who receives a send. Schemas Namespaces within a database used to organize synced tables. Lists is specific to Iterable and Schemas to Snowflake — each maps to any object or custom field on the other side.
Catalogs Named catalogs of items (products, content) used for personalization and recommendations; items upserted and read via /api/catalogs/{catalogName}/items. Tables The main landing and activation target for synced records. Catalogs is specific to Iterable and Tables to Snowflake — each maps to any object or custom field on the other side.
Commerce / Purchases Purchase and cart activity tracked via /api/commerce/trackPurchase and /api/commerce/updateCart, feeding revenue attribution and abandoned-cart journeys. Views Modeled projections used as the source side of outbound syncs. Commerce / Purchases is specific to Iterable and Views to Snowflake — each maps to any object or custom field on the other side.
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. Materialized Views Precomputed results synced outward for low-latency reads. Export data is specific to Iterable and Materialized Views to Snowflake — each maps to any object or custom field on the other side.

How changes propagate between Iterable 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.

Iterable Snowflake 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 Snowflake as a row-level write, with types converted between the two schemas.

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

Rate-limit considerations

  • 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.
  • Snowflake: No conventional API rate limits; cost and throughput are governed by virtual warehouse size and running time.
What ships with Iterable ⇄ Snowflake

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Iterable 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 Iterable or Snowflake record.

Observability

Monitoring

Track your Iterable ⇄ Snowflake sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Iterable and Snowflake.

How the Iterable and Snowflake connectors work

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.

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
Snowflake setup guide
How it works

How to connect Iterable 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.

  1. 01

    Connect your apps

    Authenticate Iterable 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Iterable connected
    Snowflake connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Iterable 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Iterable ⇄ Snowflake
    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
    Iterable Snowflake
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Iterable and Snowflake 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.

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→ 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

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