Azure Data Factory vs Infogix Data360 Analyze [EOL] comparison

"The platform excels in data transformation with its user-friendly interface and robust monitoring capabilities, making ETL processes seamless."

"The data flows were beneficial, allowing us to perform multiple transformations."

"The data copy template is a valuable feature."

"The best part of this product is the extraction, transformation, and load."

"Instead of individual people reviewing these files, we were able to automate the ingestion process, which saved a bunch of time and hours of repeated manual work."

"Data Factory itself is great, it's pretty straightforward, you can easily add sources, join and lookup information, etc., and the ease of use is pretty good."

"The solution has a good interface and the integration with GitHub is very useful."

"It has built-in connectors for more than 100 sources and onboarding data from many different sources to the cloud environment."

"This solution allows you to include your own functions that are coded in Python."

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

"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."

"If the user interface was more user friendly and there was better error feedback, it would be helpful."

"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."

"Some known bugs and issues with Azure Data Factory could be rectified."

"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."

"I have encountered a problem with the integration with third-party solutions, particularly with SAP."

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

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

"I would not say that this product is overly expensive."

"I rate the product price as six on a scale of one to ten, where one is low price and ten is high price."

"The cost is based on the amount of data sets that we are ingesting."

"It's not particularly expensive."

"ADF is cheaper compared to AWS."

"There's no licensing for Azure Data Factory, they have a consumption payment model. How often you are running the service and how long that service takes to run. The price can be approximately $500 to $1,000 per month but depends on the scaling."

"The price you pay is determined by how much you use it."

"I would rate Data Factory's pricing nine out of ten."

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