We performed a comparison between Azure Data Factory and SAP Data Services based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Users prefer Azure Data Factory, as it is mature, robust, and consistent. The built-in connectors of more than 100 sources and onboarding data from many different sources to the cloud environment make it easier for users to understand the data flow better. An experienced data engineer is recommended to ensure proper speed and functionality when using SAP Data Services; it is not recommended for the novice user.
"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"The solution is okay."
"From my experience so far, the best feature is the ability to copy data to any environment. We have 100 connects and we can connect them to the system and copy the data from its respective system to any environment. That is the best feature."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."
"The user interface is very good. It makes me feel very comfortable when I am using the tool."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"The product's most valuable features are data validation and rules."
"The BA reporting tools, such as Data Services, and ETL tool in SAP Data Services are the most valuable. When we had in-memory requirements, we used HANA. HANA is most preferably for most the customers for in-memory. SAP is the first company that created the in-memory concept."
"It is a powerful product with a broad range of features."
"The initial setup is not complex."
"Data Services' table comparison mechanism is very powerful. It's pretty hard to find a similar feature in other solutions."
"The solution is easy to use since it's a graphical tool. It also requires only low-level coding."
"The reporting on the data, even from third-party software, is very good."
"The most valuable feature is the logging capability."
"Data Factory's performance during heavy data processing isn't great."
"Some of the optimization techniques are not scalable."
"We have experienced some issues with the integration. This is an area that needs improvement."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"The Microsoft documentation is too complicated."
"The initial setup is not very straightforward."
"There's space for improvement in the development process of the data pipelines."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"They could make it easier to work with web services."
"There needs to be multi-language support, however, my understanding is they are working on multi-language now."
"There should be some kind of enhancement that can be done on the admin side of certain sites where we can assign the roles and responsibilities. We should be able to control who is using the tool and how."
"Source code control is another headache. When your source code base gets too large, managing the source code becomes cumbersome."
"The interface is not quite user-friendly and is in need of improvement."
"It will work fine only in an SAP environment. It could be said that the integration with other vendors could be better."
"The solution shows a lack of cloud support data services."
"Some of the jobs that are built within Data Services require local files, and during initial deployment, those local files cannot be transported between machines simply because of security issues."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while SAP Data Services is ranked 10th in Data Integration with 45 reviews. Azure Data Factory is rated 8.0, while SAP Data Services is rated 8.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of SAP Data Services writes "Responsive support, scalable, and beneficial integration". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and AWS Lake Formation, whereas SAP Data Services is most compared with Syniti Data Quality, Informatica PowerCenter, SAP Process Orchestration, Palantir Foundry and SSIS. See our Azure Data Factory vs. SAP Data Services report.
See our list of best Data Integration vendors.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.