We performed a comparison between Azure Data Factory and IBM Infosphere DataStage based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Azure Data Factory 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 better understand the data flow. Users are happier with its pricing as well. Once IBM Infosphere DataStage moves toward a focus on cloud technologies, it will become a more desirable solution in today’s cloud-focused marketplace.
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"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 solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"The initial setup is very quick and easy."
"From what we have seen so far, the solution seems very stable."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"The data lineage report can be filtered for reporting. The reports are user-friendly and take less time to find what you need."
"The solution is stable."
"The product is a stable and powerful data management solution that can run in parallel mode for enhanced speed."
"We are mostly using transmission rules. It has a lot of functions and logic related to transmission. It is a user-friendly tool with in-built functions."
"The best feature of IBM InfoSphere DataStage for me was that it was very much user-friendly. The solution didn't require that much raw coding because most of its features were drag and drop, plus it had a large number of functionalities."
"Once you have Infosphere up and running properly, it is stable."
"The product is easy to deploy."
"The solution is very easy to use."
"The product could provide more ways to import and export data."
"The support and the documentation can be improved."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"It can improve from the perspective of active logging. It can provide active logging information."
"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."
"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"The interface needs work to be more user-friendly."
"The template mapping could be easier."
"The initial setup can be complex."
"Improvements for DataStage could include better integration with modern data sources like cloud solutions and documents, along with enhancing its capability to handle non-structured data."
"There are three things that could improve - the cloud, monitoring and cloud integration. It's a solid product but not a modern one and of course it depends what you're looking for."
"Their web interface is good but the on-prem sites are outdated. The solution could also be improved if they could integrate the data pipeline scheduling part of their interface."
"Its documentation is not up to the mark. While building APIs, we had a lot of problems trying to get around it because it is not very user-friendly. We tried to get hold of API documentation, but the documentation is not very well thought out. It should be more structured and elaborate. In terms of additional features, I would like to see good reporting on performance and performance-tuning recommendations that can be based on AI. I would also like to see better data profiling information being reported on InfoSphere."
"I really like this tool, but the administration should be on the same client application because a lot of administration features are not on the client-side, and they usually need to have administrative access. It's quite complicated to force IT teams to have separate administrative access from the developers."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while IBM InfoSphere DataStage is ranked 7th in Data Integration with 37 reviews. Azure Data Factory is rated 8.0, while IBM InfoSphere DataStage is rated 7.8. 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 DataStage writes "User-friendly with a lot of functions for transmission rules, but has slow performance and not suitable for a huge volume of data". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Palantir Foundry, whereas IBM InfoSphere DataStage is most compared with SSIS, IBM Cloud Pak for Data, Talend Open Studio, Informatica PowerCenter and IBM InfoSphere Information Server. See our Azure Data Factory vs. IBM InfoSphere DataStage report.
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