We performed a comparison between Palantir Foundry and Pentaho Data Integration and Analytics 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."Great features available in one tool."
"The interface is really user-friendly."
"The data lineage is great."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"The solution offers very good end-to-end capabilities."
"The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."
"Encapsulates all the components without the requirement to integrate or check compatibility."
"It's scalable."
"It's very simple compared to other products out there."
"We also haven't had to create any custom Java code. Almost everywhere it's SQL, so it's done in the pipeline and the configuration. That means you can offload the work to people who, while they are not less experienced, are less technical when it comes to logic."
"I can use Python, which is open-source, and I can run other scripts, including Linux scripts. It's user-friendly for running any object-based language. That's a very important feature because we live in a world of open-source."
"The solution has a free to use community version."
"Sometimes, it took a whole team about two weeks to get all the data to prepare and present it. After the optimization of the data, it took about one to two hours to do the whole process. Therefore, it has helped a lot when you talk about money, because it doesn't take a whole team to do it, just one person to do one project at a time and run it when you want to run it. So, it has helped a lot on that side."
"Flexible deployment, in any environment, is very important to us. That is the key reason why we ended up with these tools. Because we have a very highly secure environment, we must be able to install it in multiple environments on multiple different servers. The fact that we could use the same tool in all our environments, on-prem and in the cloud, was very important to us."
"The abstraction is quite good."
"This solution allows us to create pipelines using a minimal amount of custom coding."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"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."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"Difficult to receive data from external sources."
"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."
"Some error messages can be very cryptic."
"The frontend capabilities of Palantir Foundry could be improved."
"Although it is a low-code solution with a graphical interface, often the error messages that you get are of the type that a developer would be happy with. You get a big stack of red text and Java errors displayed on the screen, and less technical people can get intimidated by that. It can be a bit intimidating to get a wall of red error messages displayed. Other graphical tools that are focused at the power user level provide a much more user-friendly experience in dealing with your exceptions and guiding the user into where they've made the mistake."
"If you're working with a larger data set, I'm not so sure it would be the best solution. The larger things got the slower it was."
"In terms of the flexibility to deploy in any environment, such as on-premise or in the cloud, we can do the cloud deployment only through virtual machines. We might also be able to work on different environments through Docker or Kubernetes, but we don't have an Azure app or an AWS app for easy deployment to the cloud. We can only do it through virtual machines, which is a problem, but we can manage it. We also work with Databricks because it works with Spark. We can work with clustered servers, and we can easily do the deployment in the cloud. With a right-click, we can deploy Databricks through the app on AWS or Azure cloud."
"I would like to see more improvements with AS400 DB2."
"There is not a data quality or MDM solution in the Pentaho DI suite."
"I work with different databases. I would like to work with more connectors to new databases, e.g., DynamoDB and MariaDB, and new cloud solutions, e.g., AWS, Azure, and GCP. If they had these connectors, that would be great. They could improve by building new connectors. If you have native connections to different databases, then you can make instructions more efficient and in a more natural way. You don't have to write any scripts to use that connector."
"One thing that I don't like, just a little, is the backward compatibility."
"I would like to see support for some additional cloud sources. It doesn't support Azure, for example. I was trying to do a PoC with Azure the other day but it seems they don't support it."
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Palantir Foundry is ranked 11th in Data Integration with 13 reviews while Pentaho Data Integration and Analytics is ranked 15th in Data Integration with 48 reviews. Palantir Foundry is rated 7.6, while Pentaho Data Integration and Analytics is rated 8.0. The top reviewer of Palantir Foundry writes "The data visualization is fantastic and the security is excellent". On the other hand, the top reviewer of Pentaho Data Integration and Analytics writes "It's flexible and can do almost anything I want it to do". Palantir Foundry is most compared with Azure Data Factory, Palantir Gotham, SAP Data Services, AWS Glue and Alteryx Designer, whereas Pentaho Data Integration and Analytics is most compared with SSIS, Azure Data Factory, Talend Open Studio, Oracle Data Integrator (ODI) and AWS Glue. See our Palantir Foundry vs. Pentaho Data Integration and Analytics report.
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