Azure Data Factory vs Informatica Enterprise Data Lake comparison

"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."

"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 most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."

"It is easy to integrate."

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

"We have been using drivers to connect to various data sets and consume data."

"The most valuable aspect is the copy capability."

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

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

"Some prebuilt data source or data connection aspects are generic."

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

"Customer service is not satisfactory. Third-party personnel handle support and rely on a knowledge repository."

"Azure Data Factory uses many resources and has issues with parallel workflows."

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

"The solution needs to integrate more with other providers and should have a closer integration with Oracle BI."

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

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

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

"The pricing is a bit on the higher end."

"I don't see a cost; it appears to be included in general support."

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

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

"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."

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

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