Skip to content
ERP ⇄ Database

Infor M3 to Jdbc integration — real-time, two-way sync

Keep Infor M3 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.

  • 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 Infor M3 and Jdbc

Give your engineers Infor M3's data in Jdbc: read it with normal queries, write back through the sync, and skip the ERP API entirely.

ERP data sits behind interfaces built for the ERP's own modules, not for your internal systems. Teams that need those records, for reporting services, internal tools, or automations, end up writing integration code against a strict API and maintaining it through every upgrade.

Stacksync mirrors Warehouses, Price Lists, Items, Customers from Infor M3 into Jdbc and keeps both sides consistent in real time. Whatever Infor M3 is the system of record for, whether financials, operations, people, or procurement, those records become rows your code can query, and changes written in Jdbc sync back into Infor M3 with its validations respected.

Common use cases

  • 01 Write records from a CRM, ERP, or another app back into database tables via SQL INSERT and UPDATE so the database stays current.
  • 02 Connect a niche or legacy RDBMS that has no dedicated Stacksync connector but ships a JDBC driver, using its JDBC URL to sync it two-way.
  • 03 Sync M3 item master and inventory balances to commerce or CPQ systems so quotes price against live stock
  • 04 Push orders captured in a CRM into M3 as customer orders without manual re-entry

Common sync patterns

Controlled write-back

Choose exactly which tables and fields may flow from Jdbc back into Infor M3, keeping the ERP authoritative.

Read ERP records with a query

Records from Infor M3 live in Jdbc as ordinary tables or collections, joinable with the rest of your data.

Internal tools and automations without API code

Scripts and services read and write the synced tables; Stacksync handles the Infor M3 interface, limits, and retries.

What you can sync between Infor M3 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.

Infor M3 objects Jdbc objects How this pairing syncs
Price Lists Pricing data keeps quoting tools consistent with the prices M3 will actually invoice. Columns Per-table fields with data types, nullability, and defaults; enumerated via DatabaseMetaData.getColumns to auto-generate and type-check field mappings. Price Lists is specific to Infor M3 and Columns to Jdbc — each maps to any object or custom field on the other side.
Items Item master records provide the SKU, unit, and attribute data other systems price and sell against. 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. Items is specific to Infor M3 and Primary keys & indexes to Jdbc — each maps to any object or custom field on the other side.
Customers Customer master records sync with CRM account records to keep one shared customer file. 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. Customers is specific to Infor M3 and Schemas & catalogs to Jdbc — each maps to any object or custom field on the other side.
Suppliers Supplier records align procurement tools with the vendors M3 purchases from. Stored procedures & functions Server-side routines callable via JDBC CallableStatement; invoked for custom read or write logic when a table-level mapping is not enough. Suppliers is specific to Infor M3 and Stored procedures & functions to Jdbc — each maps to any object or custom field on the other side.
Customer Orders Orders created in commerce or CRM systems land in M3 for fulfillment and invoicing. Sequences Server-generated identity values; relevant when writing rows into tables whose keys are assigned by the database rather than the source system. Customer Orders is specific to Infor M3 and Sequences to Jdbc — each maps to any object or custom field on the other side.
Purchase Orders PO headers and lines sync outward so buyers and receiving teams see the same demand. 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. Purchase Orders is specific to Infor M3 and Tables to Jdbc — each maps to any object or custom field on the other side.

How changes propagate between Infor M3 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.

Infor M3 Jdbc Sub-second propagation

DetectionInfor M3 notifies Stacksync of record changes through webhook events. Event publishing through Infor ION (Business Object Documents), configured in ION, or scheduled polling of API endpoints.

DeliveryEach detected change is applied to Jdbc as a row-level write, with types converted between the two schemas.

Jdbc Infor M3 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 Infor M3 through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Infor M3: Subject to Infor ION API gateway throttling policies.
  • 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 Infor M3 ⇄ Jdbc

Connect Infor M3 and Jdbc for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Infor M3–Jdbc connection.

Real-time

Two-way sync

Changes in Infor M3 or Jdbc instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Infor M3 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 Infor M3 or Jdbc record.

Observability

Monitoring

Track your Infor M3 ⇄ Jdbc sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Infor M3 and Jdbc.

How the Infor M3 and Jdbc connectors work

Infor M3

Integration surface
REST API (M3 API programs exposed through the Infor ION API gateway)
Authentication
OAuth 2.0 via Infor OS / ION API authorization
Change detection
Event publishing through Infor ION (Business Object Documents), configured in ION, or scheduled polling of API endpoints
Capabilities
read · write · webhooks
Rate limits
Subject to Infor ION API gateway throttling policies

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 Infor M3 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 Infor M3 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
    Infor M3 connected
    Jdbc connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Infor M3 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 · Infor M3 ⇄ 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
    Infor M3 Jdbc
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

Infor M3 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
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
CSA STAR
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 430 integrations available for Infor M3 and Jdbc.

Popular · 5 of 430
All Infor M3 integrations All Jdbc 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