Azure Data Factory vs Infogix Data360 Analyze [EOL] comparison

"We have used other ETL solutions in the past, and Azure Data Factory is the best one."

"So far, I'm quite happy with the solution overall."

"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."

"The reason that we implemented this product is for the full integration with the whole Azure environment."

"The data is more scalable."

"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."

"The function of the solution is great."

"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."

"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."

"The performance could be better. It would be better if Azure Data Factory could handle a higher load. I have heard that it can get overloaded, and it can't handle it."

"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."

"The pricing scheme is very complex and difficult to understand."

"The inability to connect local VMs and local servers into the data flow is a limitation that prevents giving Azure Data Factory a perfect score."

"Azure Data Factory can improve by having support in the drivers for change data capture."

"On the UI side, they could make it a little more intuitive in terms of how to add the radius components. Somebody who has been working with tools like Informatica or DataStage gets very used to how the UI looks and feels."

"When we initiated the cluster, it took some time to start the process."

"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."

"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."

"The solution's fees are based on a pay-per-minute use plus the amount of data required to process."

"The licensing is a pay-as-you-go model, where you pay for what you consume."

"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."

"It's not particularly expensive."

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

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

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

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