We performed a comparison between Azure Data Factory and Palantir Gotham based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration."The user interface is very good. It makes me feel very comfortable when I am using the tool."
"It makes it easy to collect data from different sources."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"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."
"The data copy template is a valuable feature."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"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 factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"This solution is seamless. From one platform, we can do just about anything."
"The pricing scheme is very complex and difficult to understand."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful."
"The number of standard adaptors could be extended further."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"The Microsoft documentation is too complicated."
"I think there should be less coding involved. Currently, using it involves a tremendous amount of coding."
Earn 20 points
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Palantir Gotham is ranked 33rd in Data Integration with 1 review. Azure Data Factory is rated 8.0, while Palantir Gotham is rated 8.0. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of Palantir Gotham writes "A seamless all-in-one solution ". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Microsoft Azure Synapse Analytics, whereas Palantir Gotham is most compared with Palantir Foundry, Stone Bond Enterprise Enabler and SAS Data Management.
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