Azure Data Factory vs Palantir Gotham comparison

"Its integrability with the rest of the activities on Azure is most valuable."

"This solution will allow the organisation to improve its existing data offerings over time by adding predictive analytics, data sharing via APIs and other enhancements readily."

"From what we have seen so far, the solution seems very stable."

"Azure Data Factory became more user-friendly when data-flows were introduced."

"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."

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

"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."

"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 is seamless. From one platform, we can do just about anything."

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

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

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

"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."

"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."

"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."

"Data Factory's performance during heavy data processing isn't great."

"It can improve from the perspective of active logging. It can provide active logging information."

"I think there should be less coding involved. Currently, using it involves a tremendous amount of coding."

"The licensing cost is included in the Synapse."

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

"I am aware of the pricing of Azure Data Factory, but I prefer not to disclose specific details."

"Data Factory is affordable."

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

"ADF is cheaper compared to AWS."

"Data Factory is expensive."

"The price is fair."