Azure Data Factory an Universal pipe
January 27, 2026

Azure Data Factory an Universal pipe

Raghuram Subramaniam | TrustRadius Reviewer
Score 6 out of 10
Vetted Review
Verified User

Overall Satisfaction with Azure Data Factory

We live in a world where half of the data for analytics come from SAP and half from non SAP sources. We use Azure Data Factory to load non SAP data from different source systems into Azure lake house. The project follows medallion architecture where Azure Data Factory takes data from multiple sources and stores them in the bronze layer of the medallion architecture. Since our SAP Datasphere has limitations connecting to non SAP sources as good and native like Azure Data Factory, we use Azure Data Factory for these scenarios. Further modelling of data in the next layers (silver layer and gold layer) is done using Azure Data Bricks, where the final data product is created. The Azure Data Factory also helps in applying transformations which loading the data from different source systems. Datasphere often relies on ODBC/JDBC/OData connectivity, whereas Azure Data Factory provides maintenance-free connectors for our web applications, like partner portal, cloud applications like one crm, on-prem Oracle systems, and also to NoSQL dbs like MongoDB. To summarize Azure Data Factory is used in our organisation to ingest non SAP data from different sources into our Bronze layer for the Databricks to further clean and curate the data for data product creation. Without Azure data factory connecting the data from different source wouldnt been possible because SAP Datasphere has limitations when it comes to connection with non SAP source systems

Pros

  • Connectivity with other cloud environment like Salesforce
  • Connectivity with non structured data and big data systems
  • Reduces data islands
  • Azure Data Factory handles perfectly the huge volume of data in JSON format from our global apps and services

Cons

  • The error details where there is an error while processing the files is not clear
  • Connectivity with s4 system is not so good compared to Datasphere
  • Since Azure Data Factory just transfers data it lacks the capacity to identify the wrongness in the data. It is just a dumb data transfer tool from point A to B
  • Drag and drop interface is a positive feature which allows end users to create data pipelines
  • The advantage of no or low code is causing spaghetti situation sometimes
  • Cost efficient as this is serverless and use only for pay is a interesting
  • No meta data or governance of data
Azure Data Factory has a positive impact by providing cost effective and good connectivity. However lack of modelling except for the transformation feature in the data pipelines and less governance and meta data possibility is forcing our org. to use external tools to manage data quality and meta data. Hence the rating of 6/10.
Azure Data Factory is more of a universal pipeline. SAP BW is a tool offering good SAP connectivity but very limited third-party connectivity. The same is the case with BW4hana. SAP Datasphere is offering better connectivity with SAP sources, but not so good when compared to adf. Power Center of Informatica is a legacy tool, and Anaplan is a planning tool with limited connectivity options.

Do you think Azure Data Factory delivers good value for the price?

Yes

Are you happy with Azure Data Factory's feature set?

Yes

Did Azure Data Factory live up to sales and marketing promises?

Yes

Did implementation of Azure Data Factory go as expected?

Yes

Would you buy Azure Data Factory again?

Yes

Best scenario is for ETL process. The flexibility and connectivity is outstanding. For our environment, SAP data connectivity with Azure Data Factory offers very limited features compared to SAP Data Sphere. Due to the limited modelling capacity of the tool, we use Databricks for data modelling and cleaning. Usage of multiple tools could have been avoided if adf has modelling capabilities.

Azure Data Factory Feature Ratings

Connect to traditional data sources
9
Connecto to Big Data and NoSQL
9
Simple transformations
9
Complex transformations
7
Data model creation
2
Metadata management
4
Business rules and workflow
4
Collaboration
6
Testing and debugging
5
Integration with data quality tools
1
Integration with MDM tools
7

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