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."Palantir Foundry is a robust platform that has really strong plugin connectors and provides features for real-time integration."
"It's scalable."
"The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."
"The data lineage is great."
"The interface is really user-friendly."
"The solution offers very good end-to-end capabilities."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
"Encapsulates all the components without the requirement to integrate or check compatibility."
"The amount of data that it loads and processes is good."
"Provides a good open source option."
"The graphical nature of the development interface is most useful because we've got people with quite mixed skills in the team. We've got some very junior, apprentice-level people, and we've got support analysts who don't have an IT background. It allows us to have quite complicated data flows and embed logic in them. Rather than having to troll through lines and lines of code and try and work out what it's doing, you get a visual representation, which makes it quite easy for people with mixed skills to support and maintain the product. That's one side of it."
"I absolutely love Hitachi. I'm one of the forefront supporters of Hitachi for my firm. It's so easy to integrate within our environments. In terms of being able to quickly build ETL jobs, transform, and then automate them, it's really easy to integrate throughout for data analytics."
"The fact that it enables us to leverage metadata to automate data pipeline templates and reuse them is definitely one of the features that we like the best. The metadata injection is helpful because it reduces the need to create and maintain additional ETLs. If we didn't have that feature, we would have lots of duplicated ETLs that we would have to create and maintain. The data pipeline templates have definitely been helpful when looking at productivity and costs."
"It makes it pretty simple to do some fairly complicated things. Both I and some of our other BI developers have made stabs at using, for example, SQL Server Integration Services, and we found them a little bit frustrating compared to Data Integration. So, its ease of use is right up there."
"We can schedule job execution in the BA Server, which is the front-end product we're using right now. That scheduling interface is nice."
"It has improved our data integration capabilities."
"Some error messages can be very cryptic."
"The solution could use more online documentation for new users."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"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."
"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."
"Cost of this solution is quite high."
"There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers."
"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 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."
"I would like to see improvement when it comes to integrating structured data with text data or anything that is unstructured. Sometimes we get all kinds of different files that we need to integrate into the warehouse."
"Parallel execution could be better in Pentaho. It's very simple but I don't think it works well."
"In the Community edition, it would be nice to have more modules that allow you to code directly within the application. It could have R or Python completely integrated into it, but this could also be because I'm using an older version."
"There is not a data quality or MDM solution in the Pentaho DI suite."
"One thing that I don't like, just a little, is the backward compatibility."
"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."
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Palantir Foundry is ranked 11th in Data Integration with 13 reviews while Pentaho Data Integration and Analytics is ranked 16th 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 Azure Data Factory, SSIS, 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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