We performed a comparison between Azure Data Factory and IBM InfoSphere Information Server based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"The best part of this product is the extraction, transformation, and load."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"The solution has a good interface and the integration with GitHub is very useful."
"The function of the solution is great."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"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."
"Azure Data Factory became more user-friendly when data-flows were introduced."
"This solution is extremely flexible and scalable."
"IBM InfoSphere Information Server is stable."
"The integration with different technologies is the most valuable feature."
"Stability-wise, I rate the solution a ten out of ten."
"There's space for improvement in the development process of the data pipelines."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"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."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"If the user interface was more user friendly and there was better error feedback, it would be helpful."
"IBM InfoSphere Information Server should be more scalable. It should have the option to change the configuration to run on a single, non-multiple node, or multi-threading processing."
"Their technical support needs improvement."
"This solution would benefit from the engine being made more lightweight."
"There are certain shortcomings in the cloud side of the solution, where improvements are required."
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Azure Data Factory is ranked 1st in Data Integration with 81 reviews while IBM InfoSphere Information Server is ranked 36th in Data Integration with 7 reviews. Azure Data Factory is rated 8.0, while IBM InfoSphere Information Server is rated 8.4. 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 IBM InfoSphere Information Server writes "Prompt support, reliable, but lacking scalability". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas IBM InfoSphere Information Server is most compared with IBM InfoSphere DataStage, Qlik Replicate, IBM Watson Knowledge Catalog, IBM Cloud Pak for Data and Oracle GoldenGate. See our Azure Data Factory vs. IBM InfoSphere Information Server report.
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