We performed a comparison between AWS Glue and Palantir Foundry based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Our entire use case was very easily handled or solved using this solution."
"AWS Glue is fast and managed by AWS. Hence, you don't have to worry about capacity and the performance of Glue jobs. It has integrations with other data stores of AWS. The product offers metadata management, logging, and ETL processing capabilities. It comes with a powerful feature, Glue Studio, which helps to do queries interactively within the community. It is a managed service and very secure. Another popular and mature service is S3."
"AWS Glue's best features are scalability and cloud-based features."
"The solution's technical support is good. Whenever we raise a use case where we face an issue in our company, we get a response from the solution's technical team."
"The solution is highly user-friendly, and its features are easy to use. The new addition of AWS Glue Data Catalog is also very beneficial, making the tool even more helpful for its users."
"The most valuable feature of AWS Glue is that it provides a GUI format with a drag-and-drop feature."
"I appreciate AWS Glue for its cost-effectiveness."
"It is a stable and scalable solution."
"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."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"The virtualization tool is useful."
"Great features available in one tool."
"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 AI engine that comes with Palantir Foundry is quite interesting."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
"I have encountered challenges with multi-region support."
"On occasion, the solution's dashboard reports that a project failed due to runtime but it actually succeeded."
"The solution's visual ETL tool is of no use for actual implementation."
"Glue could perform better. It sometimes takes too long to test a Glue job. Google Cloud Platform offers more Python scripts than AWS."
"The setup and installation is a bit complex without advanced knowledge or training."
"I would like to see a more robust interface on the no-code side. This would be nice to be able to split cells."
"It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do. For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do. It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options."
"It fails to handle massive databases acquired from various sources."
"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 workflow could be improved."
"The solution could use more online documentation for new users."
"Difficult to receive data from external sources."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"Some error messages can be very cryptic."
"It would be helpful to build applications based on Azure functions or web apps in Palantir Foundry."
"The solution's visualization and analysis could be improved."
AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while Palantir Foundry is ranked 12th in Cloud Data Integration with 14 reviews. AWS Glue is rated 7.8, while Palantir Foundry is rated 7.6. The top reviewer of AWS Glue writes "Provides serverless mechanism, easy data transformation and automated infrastructure management". On the other hand, the top reviewer of Palantir Foundry writes "The data visualization is fantastic and the security is excellent". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, Informatica Cloud Data Integration, SSIS and Denodo, whereas Palantir Foundry is most compared with Azure Data Factory, Palantir Gotham, SAP Data Services, Denodo and Mule Anypoint Platform. See our AWS Glue vs. Palantir Foundry report.
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