Azure Data Factory vs Infogix Data360 Analyze [EOL] comparison

"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."

"The most valuable features of the solution are its ease of use and the readily available adapters for connecting with various sources."

"In terms of my personal experience, it works fine."

"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."

"The function of the solution is great."

"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."

"The overall performance is quite good."

"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 drag-and-drop functionality makes it easy for business users."

"There is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

"The setup and configuration process could be simplified."

"The product could provide more ways to import and export data."

"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."

"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."

"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."

"The user interface could use improvement. It's not a major issue but it's something that can be improved."

"Occasionally, there are problems within Microsoft itself that impacts the Data Factory and causes it to fail."

"The memory processing needs to be improved because when you deal with a large amount of data, the interface tends to hang a little bit."

"Pricing is comparable, it's somewhere in the middle."

"Product is priced at the market standard."

"My company is on a monthly subscription for Azure Data Factory, but it's more of a pay-as-you-go model where your monthly invoice depends on how many resources you use. On a scale of one to five, pricing for Azure Data Factory is a four. It's just the usage fees my company pays monthly."

"The licensing model for Azure Data Factory is good because you won't have to overpay. Pricing-wise, the solution is a five out of ten. It was not expensive, and it was not cheap."

"The pricing is pay-as-you-go or reserve instance. Of the two options, reserve instance is much cheaper."

"For our use case, it is not expensive. We take into the picture everything: resources, learning curve, and maintenance."

"It's not particularly expensive."

"Data Factory is affordable."

"The open-source version is free to use, although it has a limitation of two-million records."