We performed a comparison between Palantir Foundry and StreamSets 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."The solution provides an end-to-end integrated tech stack that takes care of all utility/infrastructure topics for you."
"Live video sessions enhance the available documentation and allow you to ask questions directly."
"Great features available in one tool."
"It is easy to map out a workflow and run trigger-based scripts without having to deploy to another server."
"The virtualization tool is useful."
"The interface is really user-friendly."
"The data lineage is great."
"The solution offers very good end-to-end capabilities."
"StreamSets Transformer is a good feature because it helps you when you are developing applications and when you don't want to write a lot of code. That is the best feature overall."
"It is a very powerful, modern data analytics solution, in which you can integrate a large volume of data from different sources. It integrates all of the data and you can design, create, and monitor pipelines according to your requirements. It is an all-in-one day data ops solution."
"The scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."
"It's very easy to integrate. It integrates with Snowflake, AWS, Google Cloud, and Azure. It's very helpful for DevOps, DataOps, and data engineering because it provides a comprehensive solution, and it's not complicated."
"I really appreciate the numerous ready connectors available on both the source and target sides, the support for various media file formats, and the ease of configuring and managing pipelines centrally."
"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."
"The most valuable features are the option of integration with a variety of protocols, languages, and origins."
"The Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"
"The solution's visualization and analysis could be improved."
"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."
"It requires a lot of manual work and is very time-consuming to get to a functional point."
"Cost of this solution is quite high."
"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."
"If you want to create new models on specific data sets, computing that is quite costly."
"The documentation is inadequate and has room for improvement because the technical support does not regularly update their documentation or the knowledge base."
"In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."
"Visualization and monitoring need to be improved and refined."
"They need to improve their customer care services. Sometimes it has taken more than 48 hours to resolve an issue. That should be reduced. They are aware of small or generic issues, but not the more technical or deep issues. For those, they require some time, generally 48 to 72 hours to respond. That should be improved."
"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."
"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."
"I would like to see further improvement in the UI. In addition, upgrades are not automatic and they should be automated. Currently, we have to manually upgrade versions."
"The monitoring visualization is not that user-friendly. It should include other features to visualize things, like how many records were streamed from a source to a destination on a particular date."
Palantir Foundry is ranked 11th in Data Integration with 14 reviews while StreamSets is ranked 8th in Data Integration with 24 reviews. Palantir Foundry is rated 7.6, while StreamSets is rated 8.4. 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 StreamSets writes "We no longer need to hire highly skilled data engineers to create and monitor data pipelines". Palantir Foundry is most compared with Azure Data Factory, Palantir Gotham, SAP Data Services, AWS Glue and Denodo, whereas StreamSets is most compared with Fivetran, Informatica PowerCenter, Azure Data Factory, SSIS and IBM InfoSphere DataStage. See our Palantir Foundry vs. StreamSets report.
See our list of best Data Integration vendors and best Cloud Data Integration vendors.
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