Azure Data Factory vs Infogix Data360 Analyze [EOL] comparison

"The solution handles large volumes of data very well. One of its best features is its ability to integrate data end-to-end, from pulling data from the source to accessing Databricks. This makes it quite useful for our needs."

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

"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."

"The scalability of the product is impressive."

"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."

"I find the most valuable feature in Azure Data Factory to be its ability to handle large datasets."

"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."

"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."

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

"The speed and performance need to be improved."

"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."

"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."

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

"Lacks in-built streaming data processing."

"There's space for improvement in the development process of the data pipelines."

"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."

"The support and the documentation can be improved."

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

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

"It's not particularly expensive."

"Data Factory is affordable."

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

"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."

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

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

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