

Denodo and Palantir Foundry are both competing in the data virtualization and data management space, with Denodo having an edge in data virtualization and Palantir leading in data visualization and integration.
Features: Denodo is recognized for its robust data virtualization, strong data catalog, and efficient handling of disparate data sources. Its semantic layer aids in providing a comprehensive platform for data governance and extraction. Palantir Foundry is noted for its impressive data visualization, seamless integration, and comprehensive toolset that caters to complex data management and transformation needs, offering a centralized solution for diverse data visualization tasks.
Room for Improvement: Denodo can enhance its data catalog and performance scalability, with user suggestions highlighting a need for better documentation and integration with tools like Salesforce and MuleSoft. Palantir Foundry could benefit from improvements in application development and making data export methods more intuitive. Its pricing is high, and complexities may arise without detailed documentation, posing challenges for new users.
Ease of Deployment and Customer Service: Denodo offers flexible deployment across on-premises, cloud, and hybrid environments, complemented by strong customer service, though technical support experiences vary. Palantir Foundry supports public cloud deployments, providing direct developer access for technical issues. It excels in customer support but leans more towards public cloud compared to Denodo's versatile options.
Pricing and ROI: Denodo is generally perceived as costly, with expenses linked to CPU usage, but provides significant returns in operational efficiency within six months. Palantir Foundry requires tailored pricing models due to its high cost, yet offers savings in development time, proving valuable for businesses utilizing centralized data solutions.
It provides a positive return on investment for those who can connect multiple data sources and make data-driven decisions easily.
If you don't need to write a whole ETL to make the data virtualization, you need way fewer people to write a query instead of writing an ETL pipeline.
I have seen a return on investment, which showed up in improved customer satisfaction scores.
With traditional development requiring many specialized roles, Palantir Foundry allows us to operate efficiently with fewer personnel.
We saved approximately 20 to 35 percent in man-hours needed and the timing improved our project timelines by approximately 50 to 55 percent.
One clear example was the pipeline optimization I mentioned, where we reduced execution time by thirty to forty percent.
They have a good methodology for learning how to use the tool.
Denodo's customer support team is very competent and responsive.
If we raise a ticket, it can be resolved or addressed within a reasonable time frame, so support is good.
They are knowledgeable, and their boot camps demonstrate solutions in just three days, which typically takes months or years.
When I seek help regarding code in Slate, it can take considerable time for the team to find the right answer or documentation, especially since the responses depend on the level of support provided, and specific queries regarding coding usually require reaching out to more experienced developers.
The support staff are extremely knowledgeable and good at what they are doing.
For huge data requests, it cannot scale automatically; admin action is required.
Denodo's scalability comes into play specifically when there is data transfer.
My client has almost 100 million records, and the performance was impacted in a way that required optimization.
We work with large volumes of healthcare data, and it has been able to handle all the large-scale ingestion, transformation, and distributed processing workflows effectively.
For scalability, I would rate it ten out of ten because you have a lot of flexibility.
Regarding scalability, if you have billions and trillions of records, Palantir Foundry accommodates ETL pipelines with a dedicated compute profile.
I would rate it nine out of ten because it is very reliable, always performing as expected.
If JVM does not function properly, it may cause Denodo to fail to connect to different sources.
Denodo is stable and good.
Live data streaming is very hard and it keeps breaking, so it is not very stable and depends a lot on the satellite network.
I get more technical support from Palantir.
Palantir Foundry has been a stable and reliable enterprise platform.
Ensuring data caching is up to date is critical.
Denodo needs better communication on how the product can be deployed for specific solutions.
The system has dependencies on other environments, like JVM, which can affect its performance.
The platform is extremely capable, but improvements around usability, debugging experience, DevOps flexibility, and ecosystem openness would make it even more effective for enterprise engineering teams.
I want to build conversational BI or conversational agents quickly that can connect to MCPs, and other MCPs that I can communicate with in Palantir Foundry, which are areas to advance forward.
An improvement would be that in case of any changes done by the Palantir team, those changes need to be tested thoroughly so there are no downstream impacts, ensuring that the business is not affected by any modifications in the system.
For small companies, it's not a solution that most small companies can afford.
Denodo is considered pricey, limiting its use to large enterprises or government organizations that can afford its comprehensive setup.
Denodo's pricing is not affordable for small companies and is more suitable for medium to large enterprises.
Its high initial pricing can be intimidating, but it becomes cost-effective as it reduces the need for a development team.
In terms of getting a contractor to work on that, I would probably say it is more expensive because there are fewer people with that skillset compared to, say, Databricks or Azure.
We can consult it in the right way regarding Palantir Foundry use, as it is still a gray area right now concerning costing.
Denodo's ability to connect to multiple data sources and perform extract-transform-load (ETL) operations on the fly is noteworthy.
The most valuable feature of Denodo is its uniformity of self-site data access types, which allows it to connect to almost any storage technology and feed it transparently.
Denodo supports SQL base, so if you want to join two tables or two views, you can use SQL, which is an advantage as most developers or business people know SQL.
The predictive analytics capability within Palantir Foundry impacts financial forecasting strategies through its AIP functionality, which includes numerous pre-built models, LLMs, and data science application libraries.
The main advantage is you can decentralize the analytics, and you will have everything in one place, so that you do not need to rely on multiple departments working on different tools.
The low-code solutions made our lives easier because not everybody is too technical to get started and the barrier to entry is very low.
| Product | Mindshare (%) |
|---|---|
| Palantir Foundry | 2.0% |
| Denodo | 1.4% |
| Other | 96.6% |

| Company Size | Count |
|---|---|
| Small Business | 17 |
| Midsize Enterprise | 6 |
| Large Enterprise | 20 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 7 |
| Large Enterprise | 49 |
Denodo specializes in data virtualization, data cataloging, and user-friendly interfaces. It's recognized for connecting disparate data sources, presenting unified data for analytics, and supporting efficient decision-making with agile analytics and robust data governance.
Denodo effectively aggregates data from multiple sources to offer a comprehensive understanding through its virtualization capabilities. It provides role-based access control, flexible query languages, performance optimization, and integration with databases. Enhancements are needed in its interface and documentation to ensure better user experiences. While the platform supports cloud migration, integration challenges with tools like Salesforce and MuleSoft exist. Improvements in data visualization, automation, and scalability, especially in large data environments, are critical areas for growth.
What are the key features of Denodo?In industries like finance, healthcare, and retail, Denodo plays a crucial role in data virtualization and integration. Organizations use it to unify disparate data systems, enabling real-time analytics and supporting cloud migrations. Denodo's platform is ideal for businesses needing to aggregate, transform, and utilize diverse data efficiently, optimizing operations and enhancing governance.
Palantir Foundry offers intuitive data management and application development, prioritizing accessibility through low-code/no-code tools, enabling users to integrate, analyze, and collaborate efficiently.
Palantir Foundry centers on user accessibility, data governance, and real-time capabilities, streamlining processes with low-code/no-code development. It supports comprehensive data analysis and integration, enhanced by digital twin features that align virtual and physical interactions. Despite high costs and performance challenges with large datasets, it remains a prime choice for sectors needing structured and unstructured data integration. Key areas include robust data security, lineage tracking, and predictive analytics, promoted through a unified management platform adaptable to diverse needs.
What are the key features of Palantir Foundry?In manufacturing, Palantir Foundry aids in engineering pipeline models and semantic frameworks, while utilities utilize its analytics to enhance service delivery. Insurance firms leverage its capability to assess and predict customer behavior. Throughout these industries, Foundry integrates across cloud environments, bridging structured and unstructured data from various sources.
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