Dataflow Gen1 vs Dataflow Gen2: What’s Changed and Why It Matters
As organizations move toward modern data platforms, Microsoft has evolved its data integration capabilities from Dataflow Gen1 to Dataflow Gen2. While both serve the purpose of data transformation using Power Query, the underlying architecture, scalability, and use cases have significantly changed.
Understanding the difference between these two is essential for anyone working with Power BI or transitioning to Microsoft Fabric.
What is Dataflow Gen1?
Dataflow Gen1 is the original data preparation tool available in Power BI. It allows users to extract, transform, and load (ETL) data using Power Query and store it in Azure Data Lake in Common Data Model (CDM) format.
It was designed primarily for Power BI reporting scenarios, where transformed data feeds directly into datasets and dashboards.
In simple terms:
Dataflow Gen1 = Data preparation layer for Power BI
What is Dataflow Gen2?
Dataflow Gen2 is part of Microsoft Fabric’s Data Factory experience. It builds on the same Power Query interface but introduces a modern, scalable, and reusable data engineering approach.
Instead of being limited to Power BI, Gen2 enables users to load transformed data into multiple destinations like Lakehouse, Data Warehouse, and more.
In simple terms:
Dataflow Gen2 = Data preparation for the entire data platform
🔹 Key Differences
1. Platform & Architecture
- Gen1 operates within Power BI
- Gen2 is built for Microsoft Fabric
This means Gen2 is aligned with a broader ecosystem that includes data engineering, data science, and real-time analytics.
2. Data Storage
- Gen1 stores data in CDM folders in Azure Data Lake
- Gen2 stores data as Delta tables in OneLake
Why this matters:
Delta tables are optimized for performance, versioning, and large-scale analytics, making Gen2 more future-ready.
3. Data Destinations
- Gen1 → Primarily Power BI datasets
- Gen2 → Multiple destinations:
- Lakehouse
- Data Warehouse
- SQL databases
Gen2 allows data reuse across teams and tools, not just reporting.
4. Performance & Scalability
- Gen1 has limited scaling and depends on Power BI capacity
- Gen2 uses Fabric’s compute engine (Spark-based), enabling:
- Faster processing
- Handling large datasets
- Better performance optimization
5. Integration
- Gen1 is mostly isolated within Power BI
- Gen2 integrates with:
- Pipelines
- Notebooks
- Lakehouse architecture
This makes Gen2 suitable for end-to-end data workflows
🔹 Real-World Comparison
Scenario: Retail Company
A retail business collects sales data from stores and online platforms.
Using Gen1:
- Data is cleaned and loaded into Power BI
- Reports are generated
- Data cannot easily be reused elsewhere
Limitation: Each new use case may require rebuilding pipelines.
Using Gen2:
- Data is transformed once
- Stored in a Lakehouse
- Used by:
- Power BI for dashboards
- Data scientists for forecasting
- Operations for inventory optimization
Benefit: Single source of truth across the organization
🔹 Finance Use Case (Practical Insight)
For someone working with financial systems like Dynamics 365:
- Gen1 Approach:
- Prepare Balance Sheet data for Power BI
- Limited reuse outside reporting
- Gen2 Approach:
- Transform financial data once
- Store in Warehouse
- Use for:
- Reporting
- Auditing
- Forecasting
This improves consistency and reduces duplication.
🔹 When Should You Use Each?
- Use Gen1 if:
- You are working only within Power BI
- Your data needs are simple
- You are maintaining legacy solutions
- Use Gen2 if:
- You are building modern data architecture
- You need scalability and flexibility
- You want to integrate across multiple tools
Conclusion
Dataflow Gen1 laid the foundation for self-service data preparation in Power BI. However, Dataflow Gen2 takes a significant leap forward by aligning with modern data engineering practices.
It transforms dataflows from a reporting utility into a central component of enterprise data architecture.
As organizations adopt Microsoft Fabric, Dataflow Gen2 is quickly becoming the preferred choice for building scalable, reusable, and future-ready data pipelines.
