Within Data Factory in Microsoft Fabric, you'll encounter two primary tools for data movement and transformation tasks: Data Pipelines and Dataflow Gen2. While both achieve similar goals, they cater to different use cases and user preferences.
Understanding when to use which is key to building efficient and scalable data solutions in Fabric.
What is Data Factory in Fabric?
Data Factory in Microsoft Fabric provides a modern, cloud-based data integration service that allows you to create, schedule, and orchestrate your data movement and transformation workflows. It is essentially the engine that helps you bring data into OneLake and prepare it for analytics.
You'll find the Data Factory experience directly integrated into your Fabric workspace, allowing seamless interaction with other items like Lakehouses and Data Warehouses.
When you click on New Item in Your Fabric Workspace, you will find both Pipelines and Dataflow Gen2 as shown below:
Data Pipelines: The Orchestration Maestro
Data Pipelines in Fabric are the evolution of Azure Data Factory and Synapse Pipelines. They are designed for robust orchestration, control flow, and high-scale data movement. If you need to copy data from various sources to OneLake, execute notebooks, trigger stored procedures, or chain together a complex sequence of activities, Pipelines are your primary tool.
Key Characteristics of Data Pipelines:
- Orchestration: Excellent for defining a sequence of activities, handling dependencies, and scheduling complex workflows.
- Data Movement: Highly optimized for copying data between a vast array of data sources (databases, SaaS applications, file systems, cloud storage) to OneLake.
- Control Flow: Provides activities for conditional logic, looping, error handling, and parallel execution.
- Code-First & Low-Code Activities: While they primarily involve dragging and dropping activities, many activities (like calling a stored procedure or running a notebook) involve writing or pointing to code.
- Monitoring: Comprehensive monitoring tools to track pipeline runs, identify failures, and troubleshoot.
When to Use Data Pipelines:
- Ingesting large volumes of data from various sources into your Lakehouse or Warehouse.
- Orchestrating end-to-end data workflows that involve multiple steps (e.g., ingest raw data, run a Spark notebook to transform it, then load it into a data warehouse).
- Triggering other Fabric items, such as Spark notebooks, KQL queries, or dataflow refreshes.
- Implementing robust error handling and retry mechanisms.
- Scheduling batch data loads (e.g., daily, hourly).
A typical Data Pipeline showing various activities like Copy Data, Notebook, and Dataflow execution.
Dataflow Gen2: The Low-Code Transformation Powerhouse
Dataflow Gen2 in Fabric is the next generation of Power Query Online, familiar to anyone who has used Power BI or Power Apps. It's a low-code, visual tool primarily focused on data transformation and cleansing, designed for data engineers, analysts, and even business users who prefer a graphical interface.
Dataflow Gen2 excels at shaping, cleaning, and preparing data from a multitude of sources before loading it into your Fabric Lakehouse or Warehouse.
Key Characteristics of Dataflow Gen2:
- Low-Code/No-Code: The primary interaction is through a visual Power Query editor, allowing users to apply transformations without writing a single line of code.
- Intuitive Interface: Easy to learn for users familiar with Excel or Power BI's Power Query.
- Data Cleansing & Shaping: Strong capabilities for common data preparation tasks like merging, splitting, pivoting, unpivoting, type conversion, and error handling.
- Schema On Write: It writes directly to your Lakehouse or Warehouse in Delta Parquet format, creating or updating tables.
- Scalability: Leverages Spark compute under the hood for scalable transformations.
When to Use Dataflow Gen2:
- Quickly ingest and transform smaller to medium-sized datasets into a Lakehouse or Warehouse.
- When your team prefers a visual, low-code experience for data preparation.
- Performing common data cleansing and shaping tasks (e.g., standardizing formats, removing duplicates, simple joins).
- When you need to get data ready for Power BI semantic models with minimal coding effort.
- For "citizen data integrators" who are comfortable with Power Query.
Pipelines vs. Dataflow Gen2: A Quick Comparison
| Feature | Data Pipelines | Dataflow Gen2 |
| Primary Focus | Orchestration, Control Flow, Large-Scale Movement | Visual Transformation, Data Cleansing, Shaping |
| User Experience | Activity-based canvas, JSON definition | Power Query Editor (visual) |
| Best For | Complex ETL/ELT workflows, orchestration, varied activity types, high-volume ingestion | Agile data prep, smaller-to-medium datasets, business user transformations, quick data landing |
| Code Level | Low-code (configuring activities), code-first for some activities (Notebook, SP) | No-code/Low-code (M language generated behind the scenes) |
| Output Target | Can write to various destinations via Copy activity | Primarily writes to Fabric Lakehouse/Warehouse (Delta) |
| Dependencies | Can orchestrate other Fabric items | Can be orchestrated by Pipelines |
How They Work Together: A Powerful Synergy
The real power of Data Factory in Fabric comes when you use Pipelines and Dataflow Gen2 together.
You might use a Pipeline to:
- Copy raw CSV files from an external Blob Storage account into the "Files" area of your Lakehouse.
- Then, trigger a Dataflow Gen2 to read those raw CSVs, apply transformations (e.g., parse dates, clean text, merge with a lookup table), and write the cleaned data as a Delta table into the "Tables" area of your Lakehouse.
- Finally, use the same Pipeline to trigger a Spark notebook for more advanced transformations or machine learning tasks.
This combination allows you to leverage the strengths of both tools: Pipelines for robust orchestration and large-scale movement, and Dataflow Gen2 for efficient, visual data preparation.
