Azure Data Factory vs Palantir Gotham comparison

"Allows more data between on-premises and cloud solutions"

"Powerful but easy-to-use and intuitive."

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

"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."

"Feature-wise, one of the most valuable ones is the data flows introduced recently in the solution."

"The trigger scheduling options are decently robust."

"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."

"I find that the solution integrates well with cloud technologies, which we are using for different clouds like Snowflake and AWS"

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

"I would like to be informed about the changes ahead of time, so we are aware of what's coming."

"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 is room for improvement primarily in its streaming capabilities. For structured streaming and machine learning model implementation within an ETL process, it lags behind tools like Informatica."

"While it has a range of connectors for various systems, such as ERP systems, the support for these connectors can be lacking."

"Data Factory's monitorability could be better."

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

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

"We have experienced some issues with the integration. This is an area that needs improvement."

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

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

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

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

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

"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."

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

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

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