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.
"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 most important feature is that it can help you do the multi-threading concepts."
"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 trigger scheduling options are decently robust."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"We haven't had any issues connecting it to other products."
"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."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"The most valuable feature of the solution is the ability to incorporate very complex business rules in Data Stage."
"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."
"It works with multiple servers and offers high availability."
"It is quite useful and powerful."
"Offers great flexibility."
"The performance optimization is quite good in DataStage. It provides parallelism and pipelining mechanisms"
"We like the flexibility of modeling."
"The solution is stable."
"This solution is currently only useful for basic data movement and file extractions, which we would like to see developed to handle more complex data transformations."
"Data Factory's performance during heavy data processing isn't great."
"We require Azure Data Factory to be able to connect to Google Analytics."
"I have not found any real shortcomings within the product."
"Lacks in-built streaming data processing."
"There's space for improvement in the development process of the data pipelines."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"It takes a lot of time to actually trigger your job and then go into the logs and other stuff. So all of this is really time-consuming."
"What needs improvement in IBM InfoSphere DataStage is its pricing. The pricing for the solution is higher than its competitors, so a lot of the clients my company has worked with prefer other tools over IBM InfoSphere DataStage because of the high price tag. Another area for improvement in the solution stems from a lot of new types of databases, for example, databases in the cloud and big data have become available, and IBM InfoSphere DataStage is working on various connectors for different data sources, but that still isn't up-to-date, meaning that some connectors are missing for modern data sources. The latest version of IBM InfoSphere DataStage also has a complex architecture, so my team faced frequent outages and that should be improved as well."
"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."
"Currently lacking virtualization ability."
"The error messaging needs to be improved."
"The solution can be a bit more user-friendly, similar to Informatica."
"In the future, I would like to see more integration with cloud technologies."
"It would be useful to provide support for Python, AR, and Java."
Azure Data Factory is ranked 1st in Data Integration Tools with 49 reviews while IBM InfoSphere DataStage is ranked 13th in Data Integration Tools with 10 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 good, the bad and the lots of ugly". On the other hand, the top reviewer of IBM InfoSphere DataStage writes "User-friendly with a lot of functionalities, and doesn't require much coding because of its drag-and-drop features". Azure Data Factory is most compared with Informatica PowerCenter, Microsoft Azure Synapse Analytics, Informatica Cloud Data Integration, Alteryx Designer and SSIS, whereas IBM InfoSphere DataStage is most compared with SSIS, Talend Open Studio, AWS Glue, IBM Cloud Pak for Data and Informatica PowerCenter. See our Azure Data Factory vs. IBM InfoSphere DataStage report.
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