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Azure Data Factory
An Azure service for ingesting, preparing, and transforming data at scale.
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It's not really "apples to apples" comparison but if you want to compare their semi-equivalent capabilities, then the answer really depends on your use case, scale, and integration needs.
- Purpose and scope
- ADF (Azure Data Factory): A dedicated cloud ETL/ELT service for orchestrating, transforming, and moving data between sources. It's ideal for data pipelines, data ingestion, and transformation tasks at scale.
- Fabric (Data Factory within Microsoft Fabric): Fabric is a unified data platform that integrates data engineering, analytics, and lakehouse features. Its ADF-like integration pipelines exist, but the environment is broader, enabling end-to-end analytics, real-time data flows, and tight integration with OneLake and Power BI.
- Strengths
| Feature | ADF | Fabric |
|---|---|---|
| Maturity & Reliability | Long-established, stable, widely used | Newer platform, rapidly evolving |
| Pipeline Orchestration | Excellent for scheduled batch ETL/ELT pipelines | Integrated orchestration with Fabric workspaces |
| Data Integration | Connects to 100+ sources, supports complex ETL | Connects to major sources, plus seamless OneLake integration |
| Analytics Integration | Works with Synapse Analytics, Databricks | Native integration with Fabric, Power BI, Data Engineering |
| Pricing | Pay-per-activity run; can be cost-efficient for complex ETL | Might include additional cost for Fabric workspace and compute |
- Use case scenarios
- ADF is better if:
- You need a standalone ETL/ELT orchestration tool.
- You are already invested in Azure, using Synapse Analytics or Databricks for transformations.
- You want flexible triggers and mature monitoring.
- Fabric is better if:
- You want an all-in-one analytics platform (lakehouse + ETL + analytics + BI).
- You want tight integration with Power BI and OneLake.
- You are starting fresh and prefer simplified management with integrated tools.
- Key consideration
- ADF is mature, widely adopted, and best for complex enterprise ETL pipelines.
- Fabric is evolving, offers end-to-end workflow, and may reduce operational overhead but could be less flexible for highly customized ETL tasks at this stage.
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hth
Marcin