Palantir Foundry and Cloudera Data Platform are prominent enterprise data management solutions. Cloudera Data Platform has the upper hand in flexibility and scalability, while Palantir offers a user-friendly experience.
Features: Palantir Foundry offers real-time data integration, agile collaboration capabilities, and comprehensive insights for agile decision-making. Cloudera Data Platform provides open-source architecture, seamless data sharing, and advanced machine learning features. The key differentiation is in Palantir's focus on providing immediate actionable insights, whereas Cloudera emphasizes a powerful, customizable data science toolbox.
Room for Improvement: Palantir Foundry could benefit from more efficient deployment processes and reduced costs for broader accessibility. It needs enhancements in scalability and customization. Cloudera Data Platform should enhance user-friendliness and expand integration options with other data tools. Improved customer support responsiveness and more robust real-time analytics are areas to consider.
Ease of Deployment and Customer Service: Cloudera Data Platform offers a cloud-native deployment model for quick integration with existing systems and comprehensive support services for solving issues efficiently. Palantir Foundry emphasizes data security in its on-premise setup, with dedicated customer service for configuration, though with potentially longer implementation timelines. Cloudera's deployment agility contrasts with Palantir's security-focused approach.
Pricing and ROI: Palantir Foundry involves a higher upfront cost, balanced by a rapid return on investment due to increased productivity and decision-making efficiency. Cloudera Data Platform provides a cost-effective solution with scalable options, appealing to enterprises with large datasets and long-term growth strategies. Palantir's more significant initial expenditure is balanced by its quicker ROI, while Cloudera offers a budget-friendly choice with steady returns.
There are licensing costs that have been saved when we moved some of the data platforms, decommissioned them, and moved on to this platform.
In terms of return on investment, I see great changes in operational effectiveness measured by RTO when comparing on-premises solutions with cloud solutions.
A specific example of the positive impact of Cloudera Data Platform is the clearly saved time and improved performance, which is the main result of it.
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.
I would rate the customer support of Cloudera Data Platform ten out of ten.
I have communicated with technical support, and they are responsive and helpful.
Cloudera support is timely and responsive, adhering to the SLAs they provide.
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.
CDP allows for easy, mostly automated scalability where I can schedule job workflows, fine-tune system resource metrics, and add nodes with just a click.
They have the cloud burst feature available where if the on-premises capacity is not sufficient at a point in time, you can run that Spark job on the cloud itself.
The ability to scale processing capacity on demand for batch jobs without impacting other workloads, and support for a growing number of concurrent users and teams accessing the platform simultaneously are significant advantages.
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.
Sometimes the end user is not experienced or does not have all the expertise related to Cloudera specifically, making it very difficult to manage properly
Sometimes a node goes down, but it automatically returns to a healthy state.
Cloudera Data Platform is pretty stable in my experience; there are not any downtime or reliability issues.
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.
We aim to address these issues with a Kubernetes-based platform that will simplify the task of upgrading services.
Cloudera Data Platform should include additional capabilities and features similar to those offered by other data management solutions like Azure and Databricks.
Cloudera Data Platform can be improved by addressing the feasibility of using it in the cloud; there are some complexities around the components used in cloud by Cloudera Data Platform that are not really convenient.
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.
Initially, CDH had a straightforward pricing model based on nodes, but CDP includes factors like processors, cores, terabytes, and drives, making it difficult to calculate costs.
We find Cloudera Data Platform to be cost-effective.
So far, I would say that it is competitive pricing that we have received.
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.
By using the Hadoop File System for distributed storage, we have 1.5 petabytes of physical storage with 500 terabytes of effective storage due to a replication factor of three.
The Ranger integration makes it more flexible and reliable for me by allowing control over data access, specifying who can access at what level, such as table level, masking, or data layer level.
What stands out the most in Cloudera Manager are SDX, which provide centralized control for governance, security, and data lineage across multiple sources.
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 | 13.5% |
| Cloudera Data Platform | 7.9% |
| Other | 78.6% |
| Company Size | Count |
|---|---|
| Small Business | 8 |
| Midsize Enterprise | 7 |
| Large Enterprise | 26 |
| Company Size | Count |
|---|---|
| Small Business | 11 |
| Midsize Enterprise | 7 |
| Large Enterprise | 49 |
Cloudera Data Platform provides efficient data management through features like Hue, Spark, and Impala. It integrates open-source solutions, supports hybrid environments, and enhances data governance while prioritizing security, scalability, and cost-effectiveness.
Cloudera Data Platform addresses data management needs by supporting large-scale analytics, data science, and ETL processes. It facilitates seamless operation with Ambari UI for deployment and monitoring. Users benefit from robust security via Ranger, open-source compatibility, and a flexible eco-system that uses Hadoop components. While it simplifies setup and supports hybrid workloads, improvements in AI, machine learning, stability in Name Node High Availability, and cost management are ongoing needs. Challenges in tool usability, governance maturity, and scalability call for continued innovation, especially in cloud adoption and staying aligned with open-source technologies.
What are the key features of Cloudera Data Platform?Organizations in banking, healthcare, and hospitality leverage Cloudera Data Platform for data management, analytics, and cross-source integration. It handles complex data structures, bolsters AI workloads, and adheres to data compliance standards while integrating with tools like Spark, Kafka, and machine learning models.
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.
We monitor all Data Management Platforms (DMP) reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
