Real-time
Two-way sync
Changes in BigQuery or Infor M3 instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ ERP
BigQuery to Infor M3 integration — real-time, two-way sync
Keep BigQuery 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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Why teams connect BigQuery and Infor M3
Put Infor M3's records in BigQuery 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 BigQuery next to everything else the company measures.
Stacksync syncs Purchase Orders, Manufacturing Orders, Inventory Balances, Invoices from Infor M3 into tables in BigQuery continuously, managing API limits and schema drift along the way. The connection is bi-directional, so values computed in BigQuery can be written back to fields in Infor M3 where that is useful.
Common use cases
- Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
- Feed ML feature tables in BigQuery from operational systems on a continuous schedule
- Feed manufacturing order status to a customer portal for order tracking in fashion, food, or equipment supply chains
- Keep supplier and purchase order data aligned between M3 and procurement tools
Where Infor M3 runs operations: order and supply analysis
Operational records become queryable tables in BigQuery, joinable with sales and finance data.
Group reporting across systems
Combine Infor M3's records with data synced from other systems in BigQuery for consolidated views no single system can produce.
Write-back where Infor M3 exposes writable fields
Classifications or reference values computed in BigQuery sync back onto the corresponding records in Infor M3.
What you can sync between BigQuery 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.
| BigQuery objects | Infor M3 objects | |
|---|---|---|
| Partitioned tables Synced like regular tables; partition columns map to target fields. | Manufacturing Orders Production order status feeds portals and CRMs so promised dates reflect the shop floor. | |
| Clustered tables Supported; clustering is transparent to the sync. | Inventory Balances On-hand quantities by warehouse drive available-to-promise in downstream channels. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Invoices Billing documents flow to finance and CRM tools for AR visibility. | |
| Projects Connection scope: the service account grants access per project. | Warehouses Warehouse and facility records scope inventory and order data during mapping. | |
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Price Lists Pricing data keeps quoting tools consistent with the prices M3 will actually invoice. |
What ships with BigQuery ⇄ Infor M3
Connect BigQuery and Infor M3 for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Infor M3 connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever BigQuery 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 BigQuery or Infor M3 record.
Observability
Monitoring
Track your BigQuery ⇄ 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 BigQuery and Infor M3.
How the BigQuery and Infor M3 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
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 BigQuery 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 BigQuery 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.
BigQuery
connected
Infor M3
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the BigQuery 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 · BigQuery ⇄ 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.
BigQuery Infor M3Company company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
BigQuery and Infor M3 integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and Infor M3: authenticate both systems, choose the objects to sync (such as BigQuery's Partitioned tables and Clustered tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the BigQuery side: Clustered tables, Datasets, Projects, Tables, plus custom fields where BigQuery exposes them. On the Infor M3 side: Purchase Orders, Manufacturing Orders, Inventory Balances, Invoices. Stacksync auto-detects both schemas and converts types between the two systems.
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Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
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Common patterns for BigQuery and Infor M3: Where Infor M3 runs operations: order and supply analysis; Group reporting across systems; Write-back where Infor M3 exposes writable fields. Operational records become queryable tables in BigQuery, joinable with sales and finance data.
-
BigQuery: 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. Infor M3: REST API (M3 API programs exposed through the Infor ION API gateway). Authentication: OAuth 2.0 via Infor OS / ION API authorization. Stacksync manages authentication, retries, and rate limits on both sides.
-
BigQuery: Views and materialized views are not supported — only tables. 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 BigQuery and Infor M3 without custom code.
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 BigQuery and Infor M3.
Popular · 8 of 386
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BigQuery ⇄ Visibility ERP -
BigQuery ⇄ NetSuite -
BigQuery ⇄ Dynamics 365 F&O - BigQuery ⇄ SuiteAnalytics
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Infor M3 ⇄ Snowflake -
Infor M3 ⇄ MotherDuck -
Infor M3 ⇄ Databricks -
Infor M3 ⇄ AWS S3
No integrations match .
