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.

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.

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

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

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.

Wrapping Up

ADF and Synapse Pipelines are more similar than different. The pipeline skills you learn are 100% transferable between both.

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.