Real-time
Two-way sync
Changes in Databricks or Infor M3 instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ ERP
Databricks to Infor M3 integration — real-time, two-way sync
Keep Databricks and Infor M3 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
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Migrated from Mulesoft
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Why teams connect Databricks and Infor M3
Put Infor M3's records in Databricks as live tables for reporting and analysis, without extract jobs, and write results back where Infor M3 can use them.
ERP data is some of the most asked-for data in the warehouse and some of the hardest to get: the record types are many, the APIs are strict, and extract jobs are brittle. Whether Infor M3 carries financials, operations, workforce data, or all three, the analysis belongs in Databricks next to everything else the company measures.
Stacksync syncs Inventory Balances, Invoices, Warehouses, Price Lists from Infor M3 into tables in Databricks continuously, managing API limits and schema drift along the way. The connection is bi-directional, so values computed in Databricks can be written back to fields in Infor M3 where that is useful.
Common use cases
- Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- Push orders captured in a CRM into M3 as customer orders without manual re-entry
- Mirror M3 customers and invoices into a CRM or warehouse so sales and finance share one AR view
Group reporting across systems
Combine Infor M3's records with data synced from other systems in Databricks for consolidated views no single system can produce.
Write-back where Infor M3 exposes writable fields
Classifications or reference values computed in Databricks sync back onto the corresponding records in Infor M3.
Where Infor M3 holds the books: finance reporting from live data
Financial records land in Databricks as they change, so period-end reporting queries current numbers rather than last night's extract.
What you can sync between Databricks and Infor M3
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 | Infor M3 objects | |
|---|---|---|
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Warehouses Warehouse and facility records scope inventory and order data during mapping. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Price Lists Pricing data keeps quoting tools consistent with the prices M3 will actually invoice. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Items Item master records provide the SKU, unit, and attribute data other systems price and sell against. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Customers Customer master records sync with CRM account records to keep one shared customer file. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Suppliers Supplier records align procurement tools with the vendors M3 purchases from. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Customer Orders Orders created in commerce or CRM systems land in M3 for fulfillment and invoicing. |
What ships with Databricks ⇄ Infor M3
Connect Databricks and Infor M3 for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Infor M3 connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Infor M3 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 Infor M3 record.
Observability
Monitoring
Track your Databricks ⇄ Infor M3 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 Infor M3.
How the Databricks and Infor M3 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
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
How it works
How to connect Databricks to Infor M3 — 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.
- 01
Connect your apps
Authenticate Databricks and Infor M3 with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Databricks
connected
Infor M3
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Infor M3 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.
objects · Databricks ⇄ Infor M3Customers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Databricks Infor M3Company company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Infor M3 integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Infor M3: authenticate both systems, choose the objects to sync (such as Databricks's Materialized Views and Volumes), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Databricks: Unity Catalog imposes a three-level namespace (catalog.schema.table) that governs access across workspaces. Infor M3: M3 business logic is exposed through M3 API programs (MI programs), which Infor publishes as REST endpoints through the ION API gateway rather than as a single flat resource model. Stacksync's field mapping accounts for these differences between Databricks and Infor M3 without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Databricks and Infor M3 records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Databricks and Infor M3 connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Databricks–Infor M3 integration in-house.
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Yes — Stacksync ships production-grade connectors for both Databricks and Infor M3. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Infor M3: Event publishing through Infor ION (Business Object Documents), configured in ION, or scheduled polling of API endpoints. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
SECURITY
Security teams love 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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 386 integrations available for Databricks and Infor M3.
Popular · 8 of 386
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Databricks ⇄ Acumatica -
Databricks ⇄ SAP S/4HANA -
Databricks ⇄ Odoo -
Databricks ⇄ Blue Yonder WMS -
Infor M3 ⇄ Vertica -
Infor M3 ⇄ Yellowbrick -
Infor M3 ⇄ Snowflake -
Infor M3 ⇄ BigQuery
No integrations match .
