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Dataflows Gen2 vs Data Factory in Microsoft Fabric: What’s the Difference? I have been asked this question several times in recent training sessions on Microsoft Fabric, so I jotted some notes down here.

Microsoft Fabric brings together the best of Microsoft’s data engineering, data integration, analytics, and AI capabilities into a single unified platform. For many teams adopting Fabric, one of the first questions that arises is:

“What’s the difference between Dataflows Gen2 and Data Factory Pipelines?”

Both can move, transform, and prepare data. Both live inside the Fabric experience. And both can be scheduled, monitored, and orchestrated. However, they serve different purposes, offer different strengths, and work best in different parts of the modern data lifecycle.

This post explains the key differences and provides practical examples to help you choose the right tool for your scenario.

What Are Dataflows Gen2?

Dataflows Gen2 are Fabric’s low-code data preparation and transformation solution. They are built on Power Query, the same engine used in Power BI and Excel, giving analysts and citizen developers a familiar, friendly interface.

Key Characteristics

• Low-code / no-code: Drag-and-drop transformation steps rather than writing SQL or Python.
• Power Query based: Ideal for data wrangling, cleansing, merging, shaping, and enrichment.
• Works well for mid-volume data: Excellent for business data preparation and M-code transformations.
• Outputs straight into Fabric: Can load data into Lakehouses, Warehouses, and KQL databases.
• Accessible to analysts: You don’t need a data engineering background to use it effectively.

When to Use Dataflows Gen2

Dataflows Gen2 shine in scenarios such as:

• Self-service data preparation for analysts building semantic models.
• Ingesting business application data (Excel files, SharePoint lists, Dataverse, SQL).
• Quick transformations such as splitting columns, merging tables, cleaning text, or deduplication.
• Prototyping datasets before handing them over to engineering teams.

If you know Power Query, you’ll feel at home immediately.

What Is Data Factory (in Fabric)?

Fabric’s version of Data Factory combines two things:

1. Pipelines – orchestration and data movement.
2. Dataflows (Power Query) and Notebooks (Spark) – heavy-duty transformation for engineers.

It is Microsoft’s full data integration and ETL/ELT platform, now tightly integrated into Fabric.

Key Characteristics

• Enterprise-grade orchestration with pipelines, triggers, and dependency management.
• Powerful connectors for large-scale ingestion, especially from cloud and on-premises systems.
• Supports Spark notebooks and data engineering workloads.
• Handles high-volume, complex pipelines.
• CI/CD friendly and suited for production data engineering.

When to Use Data Factory

Data Factory is designed for more complex engineering tasks, such as:

• High-volume ingestion from operational systems, APIs, or files landing in cloud storage.
• ETL/ELT using Spark notebooks, SQL scripts, and pipeline activities.
• Orchestrating multi-step workflows, including branching, loops, and conditional logic.
• Copying terabyte-scale datasets from Azure SQL Database, Synapse, ADLS, AWS S3, Oracle, and more.
• Building production-ready pipelines with monitoring, retries, and error handling.

If you are familiar with Azure Data Factory, this will feel like its next evolution within Fabric.

Dataflows Gen2 vs Data Factory: How to Choose?

Here is a simple way to think about it:

Choose Dataflows Gen2 when:

• You want low-code data shaping.
• Business analysts are preparing their own datasets.
• You need simple ingestion or transformation.
• The data volumes are small to medium.
• The source systems are Excel, SharePoint, Dataverse, or SQL.

Choose Data Factory when:

• You are building enterprise pipelines.
• You need orchestration, scheduling, and dependencies.
• You are working with large or complex datasets.
• You require Spark, notebooks, Data Engineering, or SQL pipeline logic.
• Data movement needs to be integrated into CI/CD or operated at production scale.

A Combined Approach

In many organisations the best approach is both, working together:

• Data Factory pipelines handle ingestion from source systems into the Bronze layer.
• Dataflows Gen2 then apply transformations to shape and enrich the data for the Silver layer or the semantic model.

This layered approach provides scalability, governance, and flexibility while still enabling self-service analytics.

Need help applying this in practice?

If your organisation is using Power BI or Microsoft Fabric and needs clarity around architecture, governance, or next steps,
The Data Platform Accelerator is designed to help.

It’s a focused engagement that assesses your current setup and delivers a practical roadmap you can execute.

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Learn more about The Data Platform Accelerator

Summary

While Dataflows Gen2 and Data Factory sit side-by-side in Microsoft Fabric, they target very different users and workloads:

• Dataflows Gen2 → Best for analysts, low-code transformations, quick data preparation.
• Data Factory → Best for engineers, enterprise data pipelines, complex ingestion, and orchestration.

Understanding these differences ensures your team uses the right tool for the right job, helping you build efficient, scalable, and well-governed data solutions in Microsoft Fabric.

If you’re teaching or adopting Fabric, this distinction is one of the most important concepts to get right early.