We performed a comparison between Azure Data Factory and Palantir Foundry based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."UI is easy to navigate and I can retrieve VTL code without knowing in-depth coding languages."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"We have found the bulk load feature very valuable."
"The solution is okay."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"The most valuable feature is the ease in which you can create an ETL pipeline."
"The data factory agent is quite good and programming or defining the value of jobs, processes, and activities is easy."
"The trigger scheduling options are decently robust."
"The virtualization tool is useful."
"The ease of use is my favorite feature. We're able to build different models and projects or combine different projects to build one use case."
"Great features available in one tool."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"The security is also excellent. It's highly granular, so the admins have a high degree of control, and there are many levels of security. That worked well. You won't have an EDC unless you put everything onto the platform because it is its own isolated thing."
"The data lineage is great."
"It's scalable."
"The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."
"Data Factory's cost is too high."
"Azure Data Factory can improve the transformation features. You have to do a lot of transformation activities. This is something that is just not fully covered. Additionally, the integration could improve for other tools, such as Azure Data Catalog."
"The user interface could use improvement. It's not a major issue but it's something that can be improved."
"There aren't many third-party extensions or plugins available in the solution."
"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"Data Factory's monitorability could be better."
"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."
"The solution could use more online documentation for new users."
"The workflow could be improved."
"If you want to create new models on specific data sets, computing that is quite costly."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
"They do not have a data center in Europe, and we have lots of personally identifiable information in our dataset that needs to be hosted by a third-party data center like Amazon or Microsoft Azure."
"Cost of this solution is quite high."
"Difficult to receive data from external sources."
"Some error messages can be very cryptic."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Palantir Foundry is ranked 11th in Data Integration with 13 reviews. Azure Data Factory is rated 8.0, while Palantir Foundry is rated 7.6. 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 Foundry writes "The data visualization is fantastic and the security is excellent". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Oracle Data Integrator (ODI), whereas Palantir Foundry is most compared with Palantir Gotham, SAP Data Services, AWS Glue, Splunk Enterprise Security and Denodo. See our Azure Data Factory vs. Palantir Foundry report.
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We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
