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."We use the solution to move data from on-premises to the cloud."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"It is easy to integrate."
"We have been using drivers to connect to various data sets and consume data."
"Data Factory's most valuable feature is Copy Activity."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The AI engine that comes with Palantir Foundry is quite interesting."
"The interface is really user-friendly."
"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."
"The solution offers very good end-to-end capabilities."
"The data lineage is great."
"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 virtualization tool is useful."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
"Some known bugs and issues with Azure Data Factory could be rectified."
"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."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"The tool’s workflow is not user-friendly. It should also improve its orchestration monitoring."
"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."
"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."
"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 no built-in pipeline exit activity when encountering an error."
"The frontend capabilities of Palantir Foundry could be improved."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"The workflow could be improved."
"The data lineage was challenging. It's hard to track data from the sources as it moves through stages. Informatica EDC can easily capture and report it because it talks to the metadata. This is generated across those various staging points."
"The solution's visualization and analysis could be improved."
"Difficult to receive data from external sources."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while Palantir Foundry is ranked 11th in Data Integration with 14 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 Microsoft Azure Synapse Analytics, whereas Palantir Foundry is most compared with Palantir Gotham, SAP Data Services, AWS Glue, Denodo and Mule Anypoint Platform. See our Azure Data Factory vs. Palantir Foundry report.
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