Integrate Infor CloudSuite M3 data with Azure, AWS and Microsoft Fabric
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Elvenite Infor Connector is a cloud-native integration solution that offers an alternative to selected VM-based ETL client patterns for Infor CloudSuite and Infor M3. It is built on Azure Data Factory, Spark Notebooks, and pipelines as code (Bicep), and is designed to be serverless, cost-efficient, and scalable.
With Infor CloudSuite M3 as the operational data foundation, relevant ERP data can be connected to a modern data platform for analytics, automation and AI.
Direct answer: To use Infor CloudSuite M3 data in Microsoft Fabric, start with the business use case and the M3 data it requires. Extract that data through a controlled integration, land it in a governed Fabric storage layer, transform it into reusable business data, and validate changes, permissions and operations before it is used in reporting, automation or AI.
A practical architecture for Infor M3 data in Microsoft Fabric
Connecting Infor M3 to Microsoft Fabric is not only a data-movement task. The design must preserve business meaning, handle changes reliably and give teams a controlled way to use M3 data outside the operational ERP environment.
Microsoft Fabric provides an integrated analytics environment for data ingestion, engineering, warehousing, real-time processing and Power BI. Its workloads use OneLake as a shared logical data lake. The role of an M3 integration is to make the relevant operational data available to that environment in a controlled and maintainable way.
| Architecture layer | What needs to be decided | Practical output |
|---|---|---|
| Business use case | Which decision, report, workflow or AI use case should improve? | A limited first scope with a business owner and success measure. |
| Infor source | Which M3 objects, fields and surrounding data are required? | A source map with business definitions and a trusted owner. |
| Extraction | Which approved Infor extraction method fits the data format, volume and latency? | A reviewed pattern using the relevant Data Fabric API, JDBC, data flow or connector capability. |
| Change handling | How will inserts, updates, deletions and late-arriving records be handled? | An incremental-load and reconciliation design that can be tested. |
| Fabric destination | Should the workload use a lakehouse, warehouse or another governed Fabric pattern? | A target design based on the workload, not on a generic platform preference. |
| Transformation | Where should technical cleaning and business logic be applied? | Reusable, documented data that can support more than one report or use case. |
| Governance and operations | Who owns access, monitoring, errors, lineage and future changes? | An operating model with named owners, permissions and support routines. |
Infor's current Data Fabric documentation describes APIs, the Compass JDBC driver and ION Data Flows as supported ways to retrieve or move data from Data Lake. The exact choice should follow the customer's data, latency, security and operating requirements. It should not be selected from a generic architecture diagram alone.
These decisions should also follow the company's wider data strategy, so the integration supports a defined business priority instead of becoming an isolated technical project.
Why traditional ETL solutions for Infor CloudSuite M3 fall short
Many companies are currently stuck with integration solutions that are both expensive and complicated to maintain. Classic ETL tools for Infor M3 often involve:
Costly licenses
Separate licenses such as ION-S-DATALAKE-ETL drive up expenses.
Operations and maintenance on VMs
The solutions require dedicated Windows servers that need installation, patching, and ongoing maintenance.
Lack of flexibility
They are limited in scalability and make it difficult to meet growing data demands.
Learn more about ETL & ELT - differences and when should you use what?
How Elvenite Infor Connector helps you unlock the power of your Infor data
Elvenite Infor Connector is built for the cloud from the ground up. It is serverless, requires no separate license, and eliminates the need for VM operations.
- Azure Data Factory & Spark Notebooks – for secure, token-based extraction in Azure
- AWS services (e.g., Glue, S3 and Step Functions) – equivalent secure and serverless data extraction in AWS
- Pipelines as code (Bicep) – makes it easy to automate, version, and scale integrations
- Support for full and incremental data processing – inserts, updates, and deletes are handled smoothly
Benefits of a cloud-based integration between Infor CloudSuite M3, Azure, and AWS
- Cost-effective – no extra licenses or unnecessary infrastructure
- Easy to scale – built for cloud environments (Azure and AWS) and growing data needs
- Secure – token-based access via ION API Gateway
- Future-proof – optimized for Azure Data Lake, Microsoft Fabric, and BI/AI initiatives
- Fast implementation – ready-made pipelines let you get started quickly
Use cases - how you can leverage Infor CloudSuite M3 data in Azure and AWS
At its core, the target audience for Elvenite Infor Connector is any company that wants to unlock the power of its Infor data and use it outside of CloudSuite and M3.
Examples of use cases:
- Move data to Azure Data Lake or Amazon S3 and combine it with other business data
- Load data into Microsoft Fabric for advanced analytics, AI, and reporting
- Supply BI tools such as Power BI, Qlik, or AWS QuickSight with structured M3 data
- Build decision support where M3 data is integrated with sales, finance, logistics, or customer data from various systems
- Support AI and machine learning initiatives with forecasts, optimizations, and automated decisions
In short: anyone who wants to leverage their Infor data for more than the daily business system functions is the target audience.
Data Intelligence for M3 data, analytics and AI turns that shared data foundation into decision support, automation and practical operational value.
What to confirm before connecting Infor M3 to Microsoft Fabric
Use this checklist before moving from a technical proof of concept to a production data flow.
- Business outcome: Define the decision, process or analytical workload the data should support.
- Data scope: Identify the M3 objects, fields, history and surrounding sources that are actually needed.
- Business meaning: Agree on definitions for customers, suppliers, items, orders, inventory, finance and other critical entities.
- Source of truth and data ownership: Document which system or rule wins when records conflict and who is responsible for the decision.
- Change handling: Test inserts, updates, deletions, archive states and late-arriving records.
- Latency: Choose batch, scheduled, near-real-time or streaming patterns based on the use case and source capability.
- Access: Limit source and target permissions by role, purpose and environment.
- Validation: Reconcile record counts, critical values and data-quality exceptions between source and target.
- Operations: Assign monitoring, alerting, retry, incident and change-management responsibilities.
- Reuse: Design the resulting data so it can support future analytics, automation and AI work without rebuilding the same integration.
The objective is not to move every M3 table into Fabric. It is to make the operational data that matters available, understandable and maintainable for the workloads that create business value.
Future-proof integration between Infor CloudSuite M3 and your cloud platforms
With Elvenite Infor Connector, you take control of your data flows. You avoid license costs, heavy infrastructure, and lock-ins to complex ETL tools. Instead, you get a cloud-based, secure, and flexible solution that grows with your business needs - whether your cloud is Azure, AWS, or you are using Microsoft Fabric.
Frequently asked questions about Infor M3 and Microsoft Fabric
Can Infor CloudSuite M3 data be used in Microsoft Fabric?
Yes. Relevant Infor CloudSuite M3 data can be extracted through an approved integration pattern and loaded into a governed Microsoft Fabric environment for analytics, Power BI, automation and selected AI workloads. The implementation still needs clear data scope, business definitions, change handling, access control, validation and ongoing ownership.
Which Infor extraction method should be used?
Infor documents several ways to retrieve or move Data Lake data, including Data Fabric APIs, the Compass JDBC driver and ION Data Flows. The right method depends on data format, volume, latency, transformation needs, security and the customer's operating model. Elvenite's connector pattern must be confirmed by its technical owner for the specific implementation.
How should incremental M3 data loads be handled?
Incremental loads should use a reviewed change-tracking pattern and account for inserted, updated, deleted and late-arriving records. The pipeline should also reconcile source and target data, retain operational logs and define how failed or incomplete loads are recovered. The exact method depends on the selected Infor interface and source object.
Does Microsoft Fabric remove the need for integration design?
No. Microsoft Fabric provides integrated ingestion, engineering, storage, analytics and governance capabilities, but it does not decide which M3 data is trustworthy, how business definitions should work or who owns quality and access. Those decisions remain part of the integration and operating model.
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