Azure Data Factory vs Synapse Pipelines: A Practical Comparison for Data Engineers
If you’re starting a new data project on Azure, one of the first decisions you’ll face is: should I use Azure Data Factory or Azure Synapse Pipelines for your data integration?
The confusing part is that they look almost identical. Same activities, same expressions, same UI layout. So why do both exist? And when should you pick one over the other?
I’ve built pipelines in both — metadata-driven patterns in ADF and audit-logged pipelines with Parquet output in Synapse. In this post, I’ll share the practical differences that actually matter.
Table of Contents
- The Short Answer
- What They Share (The Same Engine)
- What’s Different (Where They Diverge)
- UI Differences You’ll Notice Immediately
- Feature Comparison Table
- Pricing Comparison
- When to Use Azure Data Factory
- When to Use Synapse Pipelines
- What About Microsoft Fabric?
- Can You Migrate from ADF to Synapse?
- My Recommendation
- Interview Questions
- Wrapping Up
The Short Answer
Azure Data Factory and Synapse Pipelines use the same underlying pipeline engine. The expression language is identical. The activity types are identical. The Copy activity behaves identically.
The key difference is context: ADF is a standalone data integration service, while Synapse Pipelines is embedded inside the Synapse Analytics workspace alongside SQL pools, Spark pools, and data exploration tools.
AZURE DATA FACTORY (Standalone) SYNAPSE ANALYTICS (Unified Workspace)
┌─────────────────────────────┐ ┌─────────────────────────────────────┐
│ │ │ Synapse Studio │
│ ADF Studio │ │ ┌───────────┐ ┌────────────────┐ │
│ ┌───────────────────────┐ │ │ │ Integrate │ │ Develop │ │
│ │ Pipelines │ │ │ │ (Pipelines)│ │ (SQL + Spark) │ │
│ │ Datasets │ │ │ └───────────┘ └────────────────┘ │
│ │ Linked Services │ │ │ ┌───────────┐ ┌────────────────┐ │
│ │ Data Flows │ │ │ │ Data │ │ Monitor │ │
│ │ Triggers │ │ │ │ (Explore) │ │ (All runs) │ │
│ └───────────────────────┘ │ │ └───────────┘ └────────────────┘ │
│ │ │ │
│ That's it. Pipelines only. │ │ + Dedicated SQL Pool │
│ │ │ + Serverless SQL Pool │
└─────────────────────────────┘ │ + Spark Pools │
│ + Built-in ADLS Gen2 │
└─────────────────────────────────────┘Real-life analogy: ADF is like renting a delivery truck — it does one job (move data) and does it well. Synapse is like renting an entire warehouse facility that comes with delivery trucks, a sorting area, a packaging station, and a loading dock. Same trucks inside both — but the warehouse gives you the full operation in one place.
What They Share (The Same Engine)
These are identical between ADF and Synapse Pipelines:
- Pipeline engine — same execution engine, same behavior
- Expression language —
@item(),@activity(),@dataset(),@pipeline()all work the same - Activity types — Copy, Lookup, ForEach, If Condition, Set Variable, Execute Pipeline, Web, Stored Procedure, Data Flow — all identical
- Linked Service types — same 90+ connectors
- Dataset types — same parameterization, same connection tab
- Trigger types — Schedule, Tumbling Window, Event — all the same
- Integration Runtime — Azure IR, Self-hosted IR, Azure-SSIS IR
- Error handling — same dependency conditions (Success, Failure, Completed, Skipped)
- Copy activity output —
output.rowsRead,output.rowsCopied,output.copyDuration— identical
This is the most important takeaway for interviews and real projects: any pipeline you build in ADF works in Synapse with near-zero changes. The skills are 100% transferable. If you learned metadata-driven pipelines in ADF (as we did in our metadata-driven pipeline post), that same pattern works identically in Synapse.
What’s Different (Where They Diverge)
1. Workspace Model
ADF: Standalone resource. Create a Data Factory, build pipelines, done. Exists independently from other Azure resources.
Synapse: Part of the Synapse workspace. You get pipelines (Integrate tab), SQL pools, Spark pools, data exploration, and managed ADLS Gen2 — all in one place.
2. Default Storage Linked Service
ADF: You must manually create a linked service for every storage account, including ADLS Gen2.
Synapse: Automatically creates a linked service to your primary ADLS Gen2 storage when the workspace is provisioned (e.g., naveen-synapse-ws-WorkspaceDefaultStorage). One fewer thing to set up.
3. Spark Integration
ADF: No built-in Spark. To run Spark jobs, you need a separate Azure Databricks workspace.
Synapse: Built-in Spark pools. Create Spark notebooks directly in the same workspace and call them from pipelines using the Synapse Notebook activity.
4. SQL Pool Integration
ADF: Can connect to SQL databases via linked services, but there’s no native integration.
Synapse: Native integration with Dedicated SQL pools (provisioned warehouse) and Serverless SQL pools (query files in ADLS on-demand).
5. Data Exploration
ADF: No data exploration capabilities. Purely a pipeline tool.
Synapse: Built-in data exploration via Synapse Studio. Browse your data lake, preview files, run ad-hoc SQL queries — all without leaving the workspace.
UI Differences You’ll Notice Immediately
| Element | ADF Studio | Synapse Studio |
|---|---|---|
| Pipeline authoring | Author tab | Integrate tab |
| Dataset creation | Author > Datasets | Data > Integration datasets |
| Parameterized dataset checkbox | “Edit” checkbox | “Enter manually” checkbox |
| URL | adf.azure.com |
web.azuresynapse.net |
| Monitor runs | Monitor tab | Monitor > Pipeline runs |
The “Edit” vs “Enter manually” difference catches people when switching between platforms. Same feature, different label.
Feature Comparison Table
| Feature | Azure Data Factory | Synapse Pipelines |
|---|---|---|
| Pipeline engine | Same | Same |
| Expression language | Same | Same |
| 90+ connectors | Yes | Yes |
| Data Flows (Spark) | Yes | Yes |
| Built-in Spark pools | No (needs Databricks) | Yes |
| Built-in SQL pools | No | Yes (Dedicated + Serverless) |
| Default ADLS linked service | No (create manually) | Yes (auto-created) |
| Data exploration | No | Yes |
| Standalone deployment | Yes | No (part of workspace) |
| CI/CD | Azure DevOps / GitHub | Azure DevOps / GitHub |
| Managed VNet | Yes | Yes (more comprehensive) |
| SSIS lift-and-shift | Yes | Yes |
Pricing Comparison
Pipeline pricing is essentially the same:
| Component | ADF Price | Synapse Price |
|---|---|---|
| Pipeline orchestration | $1.00 / 1,000 runs | $1.00 / 1,000 runs |
| Data movement (DIU-hour) | ~$0.25 | ~$0.25 |
| Data Flow (vCore-hour) | ~$0.27 | ~$0.27 |
Hidden cost: Synapse workspace provisions additional resources with baseline costs even when not running pipelines. ADF has zero baseline cost.
When to Use Azure Data Factory
- You only need data integration — no Spark, no SQL pools
- You want zero baseline costs
- You’re building pipelines that connect to non-Azure sources
- Your transformation logic is in Databricks — ADF just orchestrates
- You want simplicity — standalone service, no workspace overhead
When to Use Synapse Pipelines
- You’re already using the Synapse workspace for SQL or Spark
- You want everything in one place — ingestion, transformation, analytics
- You need to query Parquet files with Serverless SQL
- You’re building a Lakehouse architecture
- You need Synapse Notebooks (PySpark/Scala) as part of your pipeline
What About Microsoft Fabric?
If you are reading this in 2025 or later, you are probably wondering: where does Microsoft Fabric fit in?
| Aspect | ADF | Synapse Pipelines | Fabric Data Factory |
|---|---|---|---|
| Status | Active, fully supported | Active, but Fabric is the successor | GA — the future direction |
| Datasets | Required | Required | Removed (inline config) |
| Linked Services | Required | Required (+ default ADLS) | Replaced by Connections |
| Data Flows | Spark-based | Spark-based | Replaced by Dataflow Gen2 (Power Query) |
| Notifications | Requires Logic Apps | Requires Logic Apps | Built-in Teams + Outlook activities |
| Storage | External (ADLS, Blob) | Built-in ADLS Gen2 | OneLake (zero config) |
| Billing | Per activity run | Per activity run + workspace costs | Included in Fabric Capacity (CU) |
| Best for | Standalone data movement | Unified Azure analytics | New projects, Fabric-native platform |
The direction: Microsoft is investing heavily in Fabric as the successor to Synapse. ADF remains supported and is unlikely to be retired (too many enterprises depend on it). For new greenfield projects, evaluate Fabric first. For existing ADF/Synapse projects, there is no rush to migrate — your skills transfer directly. For a deep dive, see our Fabric Data Factory post.
Can You Migrate from ADF to Synapse?
Yes. Since they use the same engine:
- Export ADF pipelines as ARM templates (JSON)
- Import into Synapse workspace
- Update linked services references
- Test with Debug runs
The expression language, activity configurations, and dataset parameters carry over without changes.
My Recommendation
For beginners: Start with ADF. Simpler, no baseline costs, focuses purely on pipelines.
For production platforms: Use Synapse if building a complete analytics platform on Azure.
For interviews: Know both. The strongest answer: “They use the same engine. The difference is context — Synapse embeds pipelines in a unified analytics workspace, ADF is standalone. I’ve built metadata-driven pipelines in both.”
Interview Questions
Q: What is the main difference between ADF and Synapse Pipelines? A: Same pipeline engine. ADF is standalone data integration. Synapse Pipelines is embedded in the analytics workspace with SQL pools, Spark pools, and data exploration.
Q: Can you use the same expressions in both? A: Yes. All expressions are identical. A pipeline built in ADF works in Synapse with minimal changes.
Q: How do you migrate pipelines from ADF to Synapse? A: Export as ARM templates, import into Synapse, update linked service references, test. Pipeline logic carries over unchanged.
Q: What does Synapse have that ADF does not? A: Built-in Spark pools (PySpark notebooks), Dedicated SQL pools (provisioned data warehouse), Serverless SQL pools (query Parquet/CSV in ADLS on-demand), data exploration, and an auto-provisioned default ADLS Gen2 linked service. ADF is pipelines only — it needs external services like Databricks for Spark and Azure SQL for warehousing.
Q: Is there a cost difference between ADF and Synapse Pipelines? A: Pipeline execution costs are identical (same engine, same pricing per activity run and DIU-hour). However, a Synapse workspace has baseline infrastructure costs even when no pipelines are running. ADF has zero baseline cost — you pay only when pipelines execute.
Q: Where does Microsoft Fabric fit relative to ADF and Synapse? A: Fabric is Microsoft’s next-generation analytics platform that succeeds Synapse. Fabric Data Factory replaces datasets with inline config, replaces linked services with connections, adds built-in Teams/Outlook notifications, and uses OneLake instead of ADLS. ADF and Synapse remain fully supported, but Fabric is the future direction for new projects.
Wrapping Up
ADF and Synapse Pipelines are more similar than different. The pipeline engine, expression language, activities, and linked services are identical. The difference is context: ADF is a focused, standalone data integration tool. Synapse embeds pipelines inside a unified analytics workspace. And Microsoft Fabric is the next evolution — same concepts, less plumbing, more built-in capabilities. The pipeline skills you build on any of these three platforms transfer directly to the others.
Related posts: – What is Azure Data Factory? – Metadata-Driven Pipeline in ADF – Synapse Pipeline with Audit Logging – Top 15 ADF Interview Questions
Naveen Vuppula is a Senior Data Engineering Consultant and app developer based in Ontario, Canada. He writes about Python, SQL, AWS, Azure, and everything data engineering at DriveDataScience.com.