Azure Data Factory vs Palantir Gotham comparison

"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."

"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 function of the solution is great."

"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."

"We haven't had any issues connecting it to other products."

"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 data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."

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

"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."

"Lacks in-built streaming data processing."

"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."

"There aren't many third-party extensions or plugins available in the solution."

"The main challenge with implementing Azure Data Factory is that it processes data in batches, not near real-time. To achieve near real-time processing, we need to schedule updates more frequently, which can be an issue. Its interface needs to be lighter."

"There is a problem with the integration with third-party solutions, particularly with SAP."

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

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

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

"The pricing model is based on usage and is not cheap."

"Understanding the pricing model for Data Factory is quite complex."

"The cost is based on the amount of data sets that we are ingesting."

"Data Factory is expensive."

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

"The price you pay is determined by how much you use it."

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

"I would not say that this product is overly expensive."