In the previous post, we discussed about Pipelines in Azure Synapse Analytics (Synapse Pipelines, for short). In today’s post, we are going to elaborate some of the major differences between Synapse Pipelines and Azure Data Factory Pipelines.
| S. No. | Feature | Azure Data Factory | Azure Synapse Analytics |
| 1. | Using SSIS and SSIS Integration Runtime | Yes | No |
| 2. | Support for Cross-region Integration Runtime (Data Flows) | Yes | No |
| 3. | Integration Runtime Sharing across different Data Factories | Yes | No |
| 4. | Time to Live | Yes | No |
| 5. | SSIS Package Activity | Yes | No |
As is evident from the table above, the main differences between the two are around SSIS and SSIS integration runtime.
Detailed Explanation:
- Using SSIS and SSIS Integration Runtime: SSIS and SSIS Integration Runtime are not available while using Synapse Pipelines.
- Cross-region Integration Runtime (Data Flows) Support: This feature allows Azure Data Factory Pipelines to be run in an Azure Region located closer to the source and destination, resulting in better performance.
- Integration Runtime Sharing across different Data Factories: This comes in handy as it can save some development time for the data engineers as they don’t have to create a new Integration runtime for every data factory.
- Time-to-Live: This is a feature in Integration Runtime to reduce the time it takes to execute Data Flows by keeping the cluster environment alive for a fixed amount of time after data flow execution.
- SSIS Package Activity: Monitoring of SSIS package activity is not supported in Synapse Pipelines.
The complete feature comparison between the two can be found at docs online: Differences from Azure Data Factory – Azure Synapse Analytics | Microsoft Docs
Azure Data Engineering 
One thought on “Difference Between Synapse Pipelines and Azure Data Factory Pipelines”