Data Factory in Microsoft Fabric

Data Factory in Microsoft Fabric is a core component of modern data integration. It enables organizations to centrally connect data from a wide range of sources, transform it, and make it available in an automated way – without complex programming.

With an intuitive low-code interface, the component combines ease of use with the capabilities of a cloud-based platform. This allows even larger integration and loading processes (ETL/ELT) to be implemented efficiently and adapted to changing requirements.

Whether for a data warehouse, lakehouse, or data science workloads, this integration environment in Microsoft Fabric helps bring data in a structured manner to where it is needed for analytics, reporting, or further processing.

Typical Use Cases

  • Daily loading of data warehouses or lakehouses
  • Automated data provisioning for reporting and analytics
  • Consolidation of on-premises and cloud data sources
  • Migration of existing Azure Data Factory processes to Microsoft Fabric
  • Integration of real-time data for monitoring and forecasting
Diagram with nested if-then-else logic illustrating decision trees and conditional data flows in Microsoft Fabric Data Factory.

Capabilities

Multiple interlocking gears – symbolizing automated data integration, pipeline orchestration, and ETL processes in Microsoft Fabric Data Factory.

Within Microsoft Fabric, Data Factory is the central tool for visual data integration, orchestration, and automation of data flows.

  • Visual pipeline design via drag and drop – without extensive coding
  • More than 200 connectors for databases, APIs, cloud storage, and on-premises systems
  • Integration with OneLake, Power BI, Data Warehouse, and Spark
  • Flexible transformations using data flows, mapping data flows, and data wrangling
  • Scheduled executions and event-triggered workflows
  • Monitoring and alerting via the Fabric workspace – including pipeline status and runtime statistics
  • Parameterization and reusable pipelines to support agile development
  • Secure access via managed identities and role-based permissions
  • Seamless integration with GitHub and Azure DevOps for version control and CI/CD
  • Scalable execution for large data volumes – from batch jobs to near-real-time loads

Services

Comeli at a desk in front of a monitor – visual representation of data engineering, pipeline development, and monitoring in Microsoft Fabric.

We support organizations from initial design through to stable production operations. Our expertise covers all aspects of modern data integration – technical, functional, and organizational.

  • Analysis and design of data flows and integration architectures
  • Design and implementation of Data Factory pipelines (ETL/ELT)
  • Setup of automated loading processes for data warehouse, lakehouse, and reporting
  • Development of data flows for transforming and harmonizing heterogeneous data sources
  • Integration of external systems (e.g., SAP, Oracle, REST APIs, CRM systems)
  • Monitoring, logging, and error handling using Fabric capabilities
  • Version control and deployment via GitHub / Azure DevOps
  • Optimization of existing Data Factory processes with regard to performance and cost
  • Training and coaching for data engineers and administrators

Frequently Asked Questions on Data Factory in Microsoft Fabric

This FAQ addresses the topics most frequently discussed in consulting engagements and training sessions. Each answer is concise and refers to additional material where appropriate. If your question is not listed, please feel free to contact us.

Comeli dragon leans against a “FAQ” sign and answers questions about Data Factory in Microsoft Fabric.

How does Fabric Data Factory differ from Azure Data Factory?

The Fabric version is natively integrated into Microsoft Fabric and works closely with OneLake, Power BI, the Data Warehouse, and Spark. Existing Azure Data Factory processes can, in many cases, be migrated to Fabric and operated in a consolidated manner.

Which scenarios is Microsoft Fabric Data Factory particularly suitable for?

Typical scenarios include daily loading of data warehouses and lakehouses, automated data provisioning for reporting and analytics, integration of hybrid data landscapes, and near-real-time data pipelines for monitoring and forecasting.

Does Fabric Data Factory support real-time or event-based processes?

Yes. In addition to scheduled workflows, event-triggered executions are supported. This enables data-driven processes to be triggered and controlled automatically.

How is security implemented in Microsoft Fabric?

Managed identities, role-based permissions, and centralized governance via the Fabric workspace are supported. This allows access to be managed in a controlled and traceable manner.