"The data copy template is a valuable feature."
"In terms of my personal experience, it works fine."
"The initial setup is very quick and easy."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"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 solution can scale very easily."
"An excellent tool for pipeline orchestration."
"On the tool itself, we've never experienced any bugs or glitches. There haven't been crashes. Stability has been good."
"As long as you pick the solution that best fits with your requirements, you won't find that performance is a problem. It's good."
"I have found it to be a very good, stable, and strong product."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"It can improve from the perspective of active logging. It can provide active logging information."
"Data Factory could be improved by eliminating the need for a physical data area. We have to extract data using Data Factory, then create a staging database for it with Azure SQL, which is very, very expensive. Another improvement would be lowering the licensing cost."
"The solution needs to be more connectable to its own services."
"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 would be better if it had machine learning capabilities."
"The speed and performance need to be improved."
"I believe that visual data flow management and the transformation function should be improved."
"The solution has room for improvement in the ETL. They have an ETL, but when it comes to the monitoring portion, Qlik Compose doesn't provide a feature for monitoring."
Azure Data Factory is ranked 2nd in Data Integration Tools with 21 reviews while Qlik Compose is ranked 37th in Data Integration Tools with 2 reviews. Azure Data Factory is rated 7.6, while Qlik Compose is rated 6.0. The top reviewer of Azure Data Factory writes "Easy to bring in outside capabilities, flexible, and works well". On the other hand, the top reviewer of Qlik Compose writes "Excellent data integration but needs better scheduling and monitoring features". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Talend Open Studio, Palantir Foundry and Alteryx Designer, whereas Qlik Compose is most compared with Qlik Replicate, Talend Open Studio, Palantir Foundry, WhereScape RED and Matillion ETL. See our Azure Data Factory vs. Qlik Compose report.
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