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 Factory's best features are simplicity and flexibility."
"Its integrability with the rest of the activities on Azure is most valuable."
"The most valuable feature of Azure Data Factory is the core features that help you through the whole Azure pipeline or value chain."
"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."
"The security of the agent that is installed on-premises is very good."
"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
"The overall performance is quite good."
"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"It's easy to understand and deploy. It's easy to create new applications, and depending on the complexity of the application, it is easy to deploy the new requirements."
"The fact that it's built on SQL, it makes it easy to write code. The database and the connection are quite smooth."
"Data Services' best features are its robustness and plug-and-play integration with other SAP applications."
"The solution offers very good integration capabilities."
"The initial setup is not complex."
"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."
"The most valuable feature is the logging capability."
"The reporting on the data, even from third-party software, is very good."
"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"Data Factory's monitorability could be better."
"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."
"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."
"Source code control is another headache. When your source code base gets too large, managing the source code becomes cumbersome."
"We encounter challenges while exporting implemented rules, such as those applied to objects like the material master, into a format like Excel for documentation and further analysis."
"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."
"The pricing of the solution should be improved."
"There needs to be multi-language support, however, my understanding is they are working on multi-language now."
"It would be nice if this solution was a bit easier to move from development to production."
"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."
"Newer feature integration is lagging behind the company acquisitions and the product could do more to service a broader range of devices."
Azure Data Factory is ranked 1st in Data Integration with 79 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, Alteryx Designer, Informatica Cloud Data Integration, Snowflake and AWS Lake Formation, whereas SAP Data Services is most compared with Syniti Data Quality, Informatica PowerCenter, Palantir Foundry, SSIS and SAP Process Orchestration. See our Azure Data Factory vs. SAP Data Services report.
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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.