Azure Data Factory vs Informatica Enterprise Data Lake comparison

"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."

"We use the solution to move data from on-premises to the cloud."

"The interface of Azure Data Factory is very usable with a more interactive visual experience, making it easier for people who are not as experienced in coding to work with."

"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."

"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."

"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."

"Azure Data Factory is a low code, no code platform, which is helpful."

"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."

"The process of using the tool's scalability option is well documented."

"Data Factory's monitorability could be better."

"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."

"Currently, smaller businesses face a disadvantage in terms of pricing, and reducing costs could address this issue."

"In the next release, it's important that some sort of scheduler for running tasks is added."

"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."

"The pricing model should be more transparent and available online."

"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."

"Informatica Enterprise Data Lake's setup process was complex since it doesn't support a lot of real-time systems."

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

"In terms of licensing costs, we pay somewhere around S14,000 USD per month. There are some additional costs. For example, we would have to subscribe to some additional computing and for elasticity, but they are minimal."

"ADF is cheaper compared to AWS."

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"The solution's pricing is competitive."

"The price is fair."

"Data Factory is affordable."

"Data Factory is expensive."

"The licenses attached to the solution are highly priced."