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Data warehouse ⇄ Marketing

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

Keep BigQuery 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.

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

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Why teams connect BigQuery and Iterable

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

BigQuery 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 Projects, Tables, Partitioned tables, Clustered tables in BigQuery with Lists, Catalogs, Commerce / Purchases, Export data 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 BigQuery — and Stacksync keeps every copy consistent and resolves conflicts by rules you set.

Common use cases

  • 01 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 02 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 03 Track product and revenue Events (custom events, trackPurchase, updateCart) into Iterable from an app or database so journeys trigger on live behavior.
  • 04 Export Iterable engagement data (emailSend, emailOpen, emailClick, emailBounce, purchase) into a warehouse for deliverability and campaign reporting.

Common sync patterns

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 BigQuery holds the current state for auditing.

Enrich records with warehouse context

Product-usage counts, plan tier, region, or account owner computed in BigQuery appear on the matching record in Iterable, so targeting, routing, and personalization use up-to-date context.

Activate a modeled audience

A segment or score built in BigQuery — 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.

What you can sync between BigQuery 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.

BigQuery objects Iterable objects How this pairing syncs
Partitioned tables Synced like regular tables; partition columns map to target fields. Catalogs Named catalogs of items (products, content) used for personalization and recommendations; items upserted and read via /api/catalogs/{catalogName}/items. Partitioned tables is specific to BigQuery and Catalogs to Iterable — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Commerce / Purchases Purchase and cart activity tracked via /api/commerce/trackPurchase and /api/commerce/updateCart, feeding revenue attribution and abandoned-cart journeys. Clustered tables is specific to BigQuery and Commerce / Purchases to Iterable — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. 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. Datasets is specific to BigQuery and Export data to Iterable — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. 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. Projects is specific to BigQuery and Users to Iterable — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. 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}. Tables is specific to BigQuery and Events to Iterable — each maps to any object or custom field on the other side.

How changes propagate between BigQuery 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.

BigQuery Iterable Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Iterable through its API, with automatic retries and rate-limit backoff.

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

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • 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 BigQuery ⇄ Iterable

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery 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 BigQuery or Iterable record.

Observability

Monitoring

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

Trading partners

EDI

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

How the BigQuery and Iterable connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

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

    Choose tables

    Pick the BigQuery 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 · BigQuery ⇄ 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
    BigQuery Iterable
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery 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
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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 502 integrations available for BigQuery and Iterable.

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