Azure Data Factory vs Synapse Pipelines: Which One Should You Actually Use?

Last updated: November 2025

Azure Data Factory (ADF) and Synapse Analytics Pipelines look almost identical in the Azure portal. Same drag-and-drop interface. Same activity types. Same data flow expressions. If you've built a pipeline in one, you can build it in the other without relearning anything. So why do both exist?

The short version: they share the same underlying engine, but they're packaged differently with different pricing models, different deployment boundaries, and different integration points. Picking the wrong one won't break anything, but it'll cost you money or add complexity you don't need. Let's get specific.

The Shared Engine

Both ADF and Synapse Pipelines use the same pipeline execution engine. Activities, datasets, linked services, integration runtimes, triggers, data flows - all identical under the hood. A Copy activity in ADF moves data the same way a Copy activity in Synapse does. Mapping data flows use the same Spark-based engine. Even the JSON definitions are interchangeable (with minor caveats).

This means the technical question isn't "which one is more capable?" - it's "which deployment model makes more sense for my architecture?"

When to Use Azure Data Factory

ADF is a standalone service. You provision it independently, it has its own resource group, and it doesn't require any other Azure analytics services. That independence is its biggest advantage.

Use ADF when:

• Your data warehouse is Snowflake, not Synapse. This is the most common scenario we see. If you're loading data into Snowflake, adding a Synapse workspace just for pipelines adds cost and complexity with zero benefit. ADF connects to Snowflake directly via the Snowflake linked service or JDBC driver. A self-hosted integration runtime handles on-premises sources, and the Azure IR handles cloud-to-cloud movement.
• You need standalone ETL/ELT without an analytics workspace. If your architecture is "extract from sources, load into a warehouse, transform there," ADF does everything you need. You don't need Synapse SQL pools, Spark pools, or integrated notebooks.
• You have multiple data targets across clouds. ADF supports linked services for AWS S3, Google BigQuery, Snowflake, Databricks, and dozens of other non-Azure targets. Synapse Pipelines support many of the same connectors, but ADF has historically gotten new connector support first.
• You want Git integration and CI/CD without workspace overhead. ADF's Git integration (Azure DevOps or GitHub) is well-documented and battle-tested. You can deploy pipelines across environments (dev/staging/prod) using ARM templates or the ADF SDK. Synapse's CI/CD story works, but it deploys the entire workspace artifact, not just pipelines.
• You want global parameters. ADF supports global parameters that can be referenced across all pipelines. Synapse Pipelines don't support global parameters. If you rely on environment-level configuration (like connection strings that change between dev and prod), this is a real gap in Synapse.

When to Use Synapse Pipelines

Synapse Pipelines make sense when you're committed to the Synapse Analytics ecosystem and want a unified workspace for data engineering, data science, and analytics.

• You're using Synapse dedicated SQL pools as your warehouse. If your data warehouse is a Synapse dedicated SQL pool (formerly SQL Data Warehouse), using Synapse Pipelines gives you tighter integration: native PolyBase loading, direct SQL pool linked services, and unified monitoring.
• You need Spark pool integration for complex transformations. Synapse lets you call Spark notebooks directly from pipelines. If your transformation logic involves PySpark, Scala, or ML model scoring, having Spark pools in the same workspace as your pipelines simplifies the architecture.
• You want integrated monitoring across SQL, Spark, and pipelines. Synapse Studio provides a single pane of glass for monitoring SQL queries, Spark jobs, and pipeline runs. In ADF, you'd need to jump between the ADF monitor, Azure Monitor, and the Synapse workspace separately.
• You're building a data lakehouse on ADLS Gen2. If your architecture centers on Azure Data Lake Storage Gen2 with Synapse serverless SQL for exploration and Spark for processing, Synapse Pipelines are the natural fit because everything shares the same storage layer and workspace.

Pricing: Where It Gets Interesting

The per-activity-run charges are the same between ADF and Synapse Pipelines. Where they diverge is in the overhead costs.

The "workspace overhead" line is the key difference. A Synapse workspace comes with baseline costs even when no pipelines are running: the managed VNET (if enabled), a serverless SQL endpoint that's always provisioned, and workspace storage. For teams that only need pipelines and not the full Synapse experience, these costs add up without adding value.

Synapse Pipelines Limitations

Because Synapse Pipelines live inside a workspace, they inherit some constraints that ADF doesn't have:

• No global parameters. This is the most commonly cited gap. In ADF, global parameters let you define environment-level variables (like database connection strings) that all pipelines can reference. In Synapse, you need to use pipeline parameters or workarounds like Key Vault references for each pipeline individually.
• Fewer linked service types. Synapse supports most of the same connectors as ADF, but there are gaps. Some niche linked service types available in ADF aren't supported in Synapse Pipelines. Always check the Synapse connector list against your specific sources before committing.
• Workspace-level deployment. When you deploy Synapse via CI/CD, you publish the entire workspace artifact - pipelines, SQL scripts, Spark definitions, linked services, everything. In ADF, you can deploy pipeline changes independently. For large teams with separate pipeline and analytics workstreams, the ADF model is more flexible.
• SHIR sharing is different. In ADF, self-hosted integration runtimes can be shared across multiple ADF instances. In Synapse, SHIR sharing is supported but the configuration is more involved and has some version-compatibility constraints.

Real Scenario: Loading Data into Snowflake

Here's a scenario that comes up frequently: you have on-premises SQL Server databases, some cloud APIs (REST), and a few Azure Blob Storage containers. The target is Snowflake. Which one do you use?

Use ADF. Here's why:

1. Snowflake has its own compute. You don't need Synapse SQL pools or Spark pools for transformation - you'll do that in Snowflake with dbt or stored procedures.
2. ADF's self-hosted IR connects to your on-premises SQL Servers. The Copy activity moves data from SQL Server to Azure Blob Storage (staging), then a second Copy activity pushes it to Snowflake using the Snowflake linked service. Clean, simple, no extra workspace needed.
3. Global parameters in ADF let you switch between dev/staging/prod Snowflake accounts with a single parameter change per deployment. Without global parameters in Synapse, you'd need to update each pipeline's parameters separately.
4. You avoid paying for a Synapse workspace that you're only using for pipelines. That's money better spent on Snowflake credits.

Pipeline Portability

Since both tools share the same pipeline JSON format, migrating from ADF to Synapse (or vice versa) is possible. The process involves exporting the pipeline JSON from the source, adjusting linked service references to match the target environment, and importing into the destination. Microsoft provides migration documentation, and tools like the Synapse migration utility can help automate parts of the process.

That said, "possible" doesn't mean "painless." Linked services that reference ADF-specific features (like global parameters or certain shared SHIR configurations) will need manual adjustment. Test every pipeline after migration, and plan for a day or two of debugging for medium-sized deployments (20-50 pipelines).

Key Takeaways

• ADF and Synapse Pipelines share the same engine. The difference is deployment model, pricing overhead, and integration scope.
• Use ADF for standalone ETL/ELT, multi-cloud targets (especially Snowflake), and when you want the simplest possible architecture.
• Use Synapse Pipelines when you're already invested in the Synapse workspace for SQL pools, Spark pools, or lakehouse architecture.
• Synapse Pipelines lack global parameters and have fewer linked service types. Check these gaps against your requirements before choosing.
• For Snowflake as the target warehouse, ADF is almost always the better choice because Synapse adds workspace costs without adding value.
• Pipelines are portable between the two, but plan for linked service adjustments and testing during migration.