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Marketing ⇄ Database

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

Keep Iterable and Jdbc 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 Jdbc

Give your team Iterable's contacts, campaigns, and engagement in Jdbc: query them with SQL, push audiences built from your own data back to Iterable, and skip the marketing API.

Iterable holds the people, audiences, and campaign activity that marketing runs on, but that data sits behind interfaces built for marketers, not for your internal systems. Engineers and analysts who need contacts, list membership, or engagement, for reporting, attribution, or product logic, end up writing integration code against a rate-limited API and maintaining it forever. Meanwhile the customer and usage data marketers want for targeting already lives in Jdbc, hard to get into campaigns without manual exports.

Stacksync mirrors Events, Campaigns, Templates, Lists from Iterable into Stored procedures & functions, Sequences, Tables, Views in Jdbc field by field, in real time, and in both directions. Marketing records become rows your code can query and join with product and customer data; audiences and attributes computed in Jdbc, from usage, orders, or account status, sync back into Iterable to drive campaigns and ads, with Iterable kept authoritative for engagement. You decide which side owns which fields, and Stacksync resolves conflicts by rules you set.

Common use cases

  • 01 Incrementally sync a high-volume table by polling an updated_at or auto-increment column, keeping a downstream store fresh without full reloads.
  • 02 Consolidate several databases from different vendors into one relational store by syncing each through its own JDBC driver.
  • 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

React to marketing changes as row changes

A new lead, form fill, or list change in Iterable arrives as a row change in Jdbc, so scoring, jobs, and notifications run in the tooling your team already uses.

Keep contact attributes consistent

Lifecycle stage, subscription status, or plan maintained on either side stays current on the other, ending exports and dual data entry.

Query Iterable like a database

Contacts, leads, audiences, and campaign metrics from Iterable live in Jdbc as ordinary tables or collections, joinable with the rest of your data and reachable without touching the vendor API.

What you can sync between Iterable and Jdbc

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 Jdbc objects How this pairing syncs
Commerce / Purchases Purchase and cart activity tracked via /api/commerce/trackPurchase and /api/commerce/updateCart, feeding revenue attribution and abandoned-cart journeys. Sequences Server-generated identity values; relevant when writing rows into tables whose keys are assigned by the database rather than the source system. Commerce / Purchases is specific to Iterable and Sequences to Jdbc — 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. Tables The base relational tables in the target database; synced two-way as rows over SQL, with each table's primary key driving upserts and row-level updates. Export data is specific to Iterable and Tables to Jdbc — each maps to any object or custom field on the other side.
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. Views Stored SELECT queries exposed like tables; read-only projections synced outbound when raw base tables should not be exposed downstream. Users is specific to Iterable and Views to Jdbc — each maps to any object or custom field on the other side.
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}. Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. Events is specific to Iterable and Columns to Jdbc — each maps to any object or custom field on the other side.
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. Primary keys & indexes Key and index definitions read via DatabaseMetaData; the primary key is required for reliable upserts, and indexes on the cursor column keep incremental polling fast. Campaigns is specific to Iterable and Primary keys & indexes to Jdbc — 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. Schemas & catalogs Namespaces that group tables and views; the connector targets a schema/catalog and lists its objects from the JDBC metadata to build the sync. Templates is specific to Iterable and Schemas & catalogs to Jdbc — each maps to any object or custom field on the other side.

How changes propagate between Iterable and Jdbc

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

Jdbc Iterable Interval-based propagation

DetectionStacksync polls Jdbc for changes on an incremental schedule, reading only records changed since the previous pass. No native change feed.

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.
  • Jdbc: No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
What ships with Iterable ⇄ Jdbc

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Iterable or Jdbc 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 Jdbc record.

Observability

Monitoring

Track your Iterable ⇄ Jdbc 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 Jdbc.

How the Iterable and Jdbc 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.

Jdbc

Integration surface
JDBC API (java.sql / javax.sql) executing SQL through a JDBC driver, typically a pure-Java Type 4 driver; reaches any relational database with a driver - PostgreSQL, MySQL, SQL Server, Oracle, IBM DB2, and others - via a JDBC URL such as jdbc:postgresql://host:5432/db.
Authentication
A database user's username and password supplied in the JDBC connection (DriverManager or a DataSource), typically over a TLS/SSL-encrypted connection. Some drivers add Kerberos, integrated Windows auth, or cloud IAM-token auth, but the available methods depend on the target database and its driver.
Change detection
No native change feed. Incremental sync polls a cursor column - an updated_at timestamp or an auto-incrementing key - to pull new and changed rows; detecting deletes needs soft-delete flags or database triggers writing to a shadow table. No webhooks.
Capabilities
read · write
Rate limits
No SaaS-style request quota. Throughput is bounded by the target database's max connections and connection-pool size, plus the CPU and I/O it shares with production queries, so heavy syncs can contend with live workloads.
How it works

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

    Choose tables

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

Iterable and Jdbc 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
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ISO 27001
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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 391 integrations available for Iterable and Jdbc.

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