Azure Data Factory vs Infogix Data360 Analyze [EOL] comparison

"I like that it's a monolithic data platform."

"I am one hundred percent happy with the stability."

"When it comes to our business requirements, this solution has worked well for us."

"The overall architecture has been very valuable to us, as it has allowed us to scale up pretty rapidly."

"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface, so that eases the entire process."

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

"One of the most valuable features of Azure Data Factory is the drag-and-drop interface, which helps with workflow management because we can just drag any tables or data sources we need and, because of how easy it is to drag and drop, we can deliver things very quickly."

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

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

"Data Factory could be improved in terms of data transformations by adding more metadata extractions."

"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."

"The performance could be better. It would be better if Azure Data Factory could handle a higher load."

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

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

"Data Factory's monitorability could be better."

"The number of standard adaptors could be extended further."

"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 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 pricing is a bit on the higher end."

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

"Azure Data Factory gives better value for the price than other solutions such as Informatica."

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

"Our licensing fees are approximately 15,000 ($150 USD) per month."

"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."

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

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

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