Palantir Foundry Reviews

Palantir Foundry serves as our primary SaaS platform, providing a single platform where we integrate our data, create data connections, retrieve the data, and build transformations on top of that.

We then create visualizations and reporting by creating Workshop applications or Contour analysis.

In my recent project, we had all those data and reports in the old on-premises system, QlikView. We migrated all that data along with the workflow and dashboards onto Palantir Foundry.

We created all those datasets in Code Repository by ingesting raw data and creating data connections from different sources such as SAP sources and other sources. We then consolidated all that data and performed transformation.

On top of that, we created a workflow using Pipeline Builder, and then we fed that data into the ontologies and created the dashboards in Workshop applications.

This was the entire end-to-end workflow.

Palantir Foundry provides a good platform where we can integrate two different data sources and pull all that data together.

We can find business use cases on top of that by creating applications or dashboards for analysis, supply chain workflow, or any kind of business value we want to find out.

It's a very good platform where we can do all those things on a single platform.

Currently, I believe that integrating AI models and an AI agent on top of the ontology is valuable. This is the best thing that Palantir has launched recently.

The theme is very monotonous and should be improved. Analytics and data engineering platforms such as Databricks and other tools have a very good UI and theme.

Palantir Foundry should work on improving this aspect.

In terms of data integration, we have to create an agent and similar functionality.

If Palantir were able to add more connectors, it would be really helpful. These are some areas where they can improve.

I have been using Palantir Foundry for the last three years.

Palantir Foundry is very stable.

We can increase the cluster or the kind of CPU and computation we want.

For storage, we obviously get a kind of S3 bucket, so this aspect is fine.

In my organization, we have a monthly call with Palantir support where we can sometimes find solutions from them.

For example, they support us on a few new, latest changes made by Palantir or if they are sunsetting any tools.

Earlier, we had a lot of legacy systems for big data processing. Sometimes we did not find the right skill set to deal with all that infrastructure, and it was taking a lot of time and cost for our organization.

When we switched to Palantir Foundry, it became a single platform which we could use for our entire analytics and data engineering workflow.

This was the best thing, where we get all the advantages of other platforms in a single place.

When we were dealing with Excel-based or Midas systems, there were a lot of challenges.

Sometimes we were lagging in data correctness or updated data while we had a reporting call or our weekly performance call.

Sometimes we did not get the updated data, and sometimes we were lacking in terms of correctness.

After we automated the entire workflow on Palantir Foundry, it ran really well in terms of time and correctness, and also data quality.

Palantir Foundry has many options, such as data expectancy checks, schedules, time checks, and health checks, which guarantee data correctness and data quality.

Transformations were taking so much time to perform and run.

When we used Spark-based transformation in Code Repository and applied optimization techniques, it helped significantly.

We reduced the time by fifty percent, and it was a great achievement for us in that project.

We should definitely check Palantir Foundry out once, as it can be really helpful for the business.

My main use case for Palantir Foundry is for building solutions predominantly for different enterprises, mainly for healthcare and manufacturing.

For healthcare, I have built a clinical trial management solution mainly helping the enterprise and the R&D sector. Based on different diseases which patients undergo, I analyze what permutations and combinations are possible. The second part is to monitor the overall lifecycle of the trial and drug trial which happens at an R&D center.

Palantir Foundry's ecosystem, which includes Ontology Manager, data pipeline, and OSDK, allows us to integrate with different other devices as needed. For other industries, specifically manufacturing, I focus on managing the overall supply chain and analyzing overall operations. I would say figuring out the overall time it takes for a particular task to complete rather than optimizing. For example, if there is a machine malfunction due to any reasons and an issue has been raised, I determine how much time a technical person takes, what challenges they face, and eventually if there is a knowledge gap, I provide them with a knowledge base, integrate with real-time knowledge help, and calculate the average time a person takes to complete a task. Then I monitor whether over a period of time any optimization can be done. In order to build this ecosystem, I use Palantir Foundry.

From my experience, one of the best features Palantir Foundry offers is that you have everything in one ecosystem. The second point is that coding is required. I would not say it is a no-code platform, but it is a low-code platform. With the recent induction of AIFDE, which was eleven months back, it becomes easier for white coding. Any person who understands the concept of coding can easily build or decently build an application or convert their idea to a prototype and then productionize it over a period with a team. The second thing is that with features such as Ontology Manager, Workspace, Data Pipeline, and OSDK, with minimum resources, you can deliver a product or a solution to any enterprise in the best possible optimized time.

Palantir Foundry, specifically from a business perspective, has helped me to create a strong customer pipeline. I see an increase in leads by five percent and faster delivery optimized by ten percent.

Regarding how Palantir Foundry can be improved, it is evolving by adopting different other technologies, but one of the key factors is that it all depends upon the vision of the product. However, Palantir Foundry's license fees comparative to others are quite high. Secondly, Palantir Foundry comes with a different set of products. Apart from Foundry, there is Apollo and Gotham. I can understand the tight coupling between Gotham and Apollo because they cater to more critical missions. But from a perspective of Foundry, it can be a bit loose in terms of adopting different other technologies so that the use case can be widened.

Integration is where they can improve. Support is fine, but the integration to different ecosystems which might be legacy is one area for improvement. One of the major points is pricing.

I have been using Palantir Foundry for roughly around two and a half years.

From a perspective of other platforms, I have majorly worked on open source because it gives me a lot of flexibility as I am also a product manager and I build products. From a perspective of data pipeline, it is good where it becomes very easy to build. Unlike other similar platforms such as Informatica from way back, where you can create a data pipeline or workflow pipeline, here it is much easier and you gain a better understanding of what data is going to flow to the next stage. If I were to use open source, since I understand it, it might not be difficult for me, but from a third person's perspective, it can be quite tricky. Not everyone would be that comfortable. But Palantir Foundry does come with its own caveats.

You should always give Palantir Foundry a try. However, you should not limit yourself to Palantir Foundry. The current capability of Palantir Foundry is good and great. However, it does not mean that there are no other options, so you need to think and take a hybrid approach.

It is a great tool, no doubt about it, but as far as wide adoption is considered, one of the reasons is price. Another reason is that it is not freely integrable with any of the solutions because of its tight coupledness. This creates a certain kind of hurdle in its wide adoption. However, I do understand that a lot of organizations are adopting it in the United States and a part of Europe, but there is the rest of the world who has not yet adopted it or is not even thinking on those grounds because one of the reasons is price.

Regarding Palantir Foundry's AI capabilities, I think the governance and security are good. There is no doubt about it.

I would say the accuracy and reliability of output from Palantir Foundry are pretty decent. You can rely on it. At the end of the day, the AI capability is backed by Claude, so it is pretty decent.

I would rate this review as six out of ten.

I work in an organization which uses Palantir Foundry, not as a customer, integrator, reseller, or consultant.

Our main problem statement with Palantir Foundry was that we have our data in corporate systems, primarily SAP-based, and many other systems managing HR, logistics, finances, and program activities, all working in silos without communication. Whenever we need to integrate something, it has to be done outside those systems, which could involve a lengthy, customized solution process and maintenance hassles. Updating the data becomes problematic as it turns static once extracted, requiring repetition for new updates, which compounds errors and effort across 120 countries and multiple locations. There was no central truth to the data, leading to discrepancies as everyone applies their own algorithms. This ongoing issue is rampant across organizations; I recognized it since joining in 2001, noting that other solutions like Tableau or Power BI merely mask the problem rather than solve it. Palantir Foundry was clearly the solution we needed; it has upgraded our thinking, decision-making processes, data handling, and overall data literacy. Now, the democratization of data occurs as everyone learns to access and analyze it uniformly. This shift has fostered new habits in data sharing and reporting while making previously theoretical efficiencies actionable.

What I appreciate most about Palantir Foundry is its focus away from mere reporting beautification; organizations historically prioritize chart aesthetics and transitions. Tableau exemplifies this with flashy features that, while entertaining, lack substance. The real value lies in key numbers and easy complex calculations, traditionally requiring extensive configuration. With Palantir Foundry we have eliminated the complications of such custom solutions, and it synchronizes data across systems effortlessly. Whether I am handling 10 rows or 10 billion, it processes the data efficiently, employing Spark for computations based on data volume. Moreover, the integration of foundational technologies into Palantir Foundry allows users to work with Python or SQL, maintaining a universally recognizable skillset. Even if you are new to these languages, learning them within Palantir Foundry equips you for future career shifts. While Palantir Foundry is becoming somewhat restrictive, it fundamentally educates users on widely applicable skills.

I believe one significant enhancement for Palantir Foundry could be making it more accessible for managers and decision-makers who often lack time for in-depth analytics. Currently, analysts handle the in-depth analytics and present information to managers. Bridging this gap means making it effortless for users to quickly open the platform and find the information they seek, which could improve with the introduction of AI assistants. Instead of needing to navigate complex queries, users can simply ask questions and receive the necessary data, making managers happier with Palantir Foundry's capabilities.

I have been working with Palantir Foundry for about seven to eight years.

My impression of the stability and reliability of Palantir Foundry is positive; it is well managed and robust, and I have not observed breakdowns. Palantir appears to invest significant resources in support at various levels, allowing problems to be escalated effectively, despite occasional bureaucratic delays. The platform itself is well-crafted, and improvements are continually rolled out.

The scalability of Palantir Foundry is immense, suitable for any industry and can expand to encompass various departments and fields that process data or workflows. It has applications across banking, insurance, logistics, supply chain, and engineering. However, I observe a potential drawback concerning market trends; as software becomes more accessible with individuals developing applications independently, there is a risk posed to Palantir Foundry, which combines multiple core functionalities. Although its significance remains vital, emerging competition can impact its customer base. Microsoft and Anthropic are both exploring ways to capture part of Palantir Foundry's market share by offering solutions that simplify tasks that would otherwise require substantial engineering resources.

I perceive the technical support for Palantir Foundry as standard and adequate; I interact primarily with the deployed engineers, though I cannot distinguish which ones are from Palantir or from us.

I have not evaluated other options before choosing Palantir Foundry because it had no competition seven to eight years ago. Other solutions such as building your own pipeline through IT were difficult to support and upgrade, often failing to connect to APIs. Once organizations grow larger, specialized units emerge, isolating business users from technical expertise, but Palantir Foundry integrates both, empowering technical data analytics for business users. It is increasingly relevant today with the rise of AI, as those sticking with Excel will struggle compared to specialists who can drive efficiency through data. Palantir Foundry enables a mutual understanding between technical and business perspectives. Competitors such as Tableau, while popular, provided only dashboards without addressing core issues. The same goes for BO and Power BI. Recently, Microsoft Fabric might be a real competitor, but I have no experience with it.

I participated in the initial setup of Palantir Foundry from a business perspective, particularly in setting up the ontology. Connecting to corporate systems was handled by Palantir engineers along with our specialized technical staff, ensuring governance regarding security, safety, and the ingestion process. We created the first ontology and applications to demonstrate Palantir Foundry's capabilities. Additionally, we developed APIs that interfaced with legacy applications for data crunching in Palantir Foundry. We implemented code repositories to ensure robust production-level pipelines. As early adopters, our involvement predates Palantir's public launch, making us a key part of its development journey.

I think there are quantifiable efficiency gains from Palantir Foundry, but I cannot specify exact numbers; we have verified that it helps in saving costs.

Navigating Palantir Foundry is straightforward for someone with a technical background, and the provided documentation and learning platform, learn.palantir.com, support various use cases with hands-on experience of new features. The AI assistant, although not perfect, offers assistance for troubleshooting. One of Palantir Foundry's strengths lies in its adherence to industry standards, allowing familiar practices from other applications to be applicable. Its version control, Git usage, and standardized charts provide multiple support avenues for users. I would rate this review a 9 out of 10.

My main use case for Palantir Foundry involves manufacturing, IT/OT integrations, and UNS integration, supply chain control tower.

For those integrations, I handle IT/OT integration of data with enterprise data that has to come to a UNS, unified namespace, and that UNS has to connect with the supply chain control tower so that inventory optimizations can be reduced. Typically, in the manufacturing industry, I want to reduce inventory overheads or inventories that are extremely present, and those are certain of my use cases, including some use cases on utilities that have been solutionized and provided as a POV to the customer.

Palantir Foundry gives me a unified view of AI and my engineering space while I have been doing a lot of data engineering in a couple of technologies, bringing that data together and stitching them and putting together AI, enabling AI use cases, which makes me see a holistic view of data coming from various platforms.

In manufacturing, my data is not only on one single platform, and Palantir Foundry enables me to make use of all the different data sources together, and the agentic AI platform where AIP can help make certain conscious decisions based on KPIs, allowing me to look at the right place rather than having two different platforms talking to each other. Instead of building or re-engineering, I prefer using Palantir Foundry, which is the main goal, and that is where we have been solutionizing with the customer.

One of the best features Palantir Foundry offers, in my experience, is the application; AIP applications are built in a very quick and fast, intuitive way, which is something very unique I have seen.

Palantir Foundry has positively impacted my organization very much since now they know Palantir Foundry and view it as a kind of industry standard.

I suggest that Palantir Foundry could be improved by providing additional data federations that may already exist, but I believe more of a data connect rather than complementing each other's technologies is needed instead of being competitive. Palantir Foundry can provide additional value at a reasonable cost in this area.

I think much of the work within Palantir Foundry is still manual, so I might want to write certain automations or develop conversational interfaces rapidly. For instance, 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.

I have been using Palantir Foundry for about a year.

In my experience, Palantir Foundry is stable.

Palantir Foundry's scalability is very good.

The customer support for Palantir Foundry is very good.

I previously used Databricks before Palantir Foundry.

I switched from Databricks to Palantir Foundry because I found Palantir Foundry to be more unique, with a clearer proposition and clarity of AI.

While project delivery was not fully productionized, I at least facilitated solutioning that identifies the kinds of art of possibilities that a customer can roll out as part of our practice and PO group.

I have not seen a return on investment with Palantir Foundry and cannot share any relevant metrics, such as time saved, money saved, or fewer employees.

I noticed improvements in efficiency and productivity since using Palantir Foundry, but cost-wise, we got a trial license, and I still need to learn about the licensing and the costs that have been impacted.

I need to draw parallels with Databricks and Snowflake, while some of those tools are already adopted. I would like to look at more white papers and pursue additional certifications and trainings on Palantir Foundry, including in-person classroom trainings, so that we can consult it in the right way regarding Palantir Foundry use, as it is still a gray area right now concerning costing.

I did not evaluate any other options before choosing Palantir Foundry.

My advice to others considering using Palantir Foundry is that it is a good tool that, if you are okay with the licensing and costing, and if you can try a POC and actual value, it provides true business value rather than discussing technicalities regarding your data. If you truly want to create value, this is the right tool.

Palantir Foundry is deployed in my organization using a public cloud.

In terms of Palantir Foundry's AI capabilities, its accuracy and reliability of output is good, with reasonable output. I would rate this review a 10.

My main use case for Palantir Foundry is to solve business problems, such as in healthcare. I also worked on a project for a law firm where thousands of PDFs were coming in, and we needed to check the Excel and PDF data to match everything correctly and ensure all data was accurate.

I would outline the law firm project in detail. There were thousands of PDFs and Excel sheets which needed to be checked against the PDF data and the Excel data to verify if the data was correct. We used Pipeline Builder to extract the PDF and then run a query to find the extracted data from the PDF and check it against the Excel sheet. This created significant time savings for people and was also faster than manual processes.

We are also running our own company's operations inside Palantir Foundry, including leaves, opportunities, leads, accounts, salary, and working times, with everything documented there. That represents another use case.

The best features Palantir Foundry offers is the ability to connect data from multiple sources and to create a single-layer ontology that basically differentiates the edited data from the real data. We don't lose the real data at any point, and that is the best feature I can think of.

With the single-layer ontology and data connection feature, there are multiple software systems including many datasets, such as Salesforce with sales data and Azure with accounts data and everything else. Basically, what ontology does is it connects everything so that we can run a layer on that and create a dashboard to see what is actually happening across the whole company, not just for a specific department.

There are multiple additional features, including embedded maps. We can do vector embedding and run a rank against the vector. There is a machinery process which helps us to find the bottleneck of the company, determining at which stage the company is lagging and what stage takes the most time, so that we can focus on that and solve the issue. That literally helps a lot.

Palantir Foundry has impacted my organization positively because projects that would have taken months or years to build can now be completed in six to seven weeks with an end-to-end workflow. There is an easy layer of AI in every feature that helps us to do the work faster and also helps the organization make decisions faster regarding how we should go about the feature and what use cases we are solving.

For creating a website or dashboard, it would have taken a lot of effort to build by taking the data from everywhere and maintaining a dashboard. That is not an easy task, but using Palantir Foundry's pipeline, data connection, and ontology, it is faster because we have all the data at one place, so we can just have the updates on a real-time basis.

The Workshop application could be improved because it is not very customizable, but it is still very strong. We also have the React OSK apps, but it does not allow the inbuilt applications such as Maps and AIP Analyst and everything else inside the React OSK. They should make Workshop a little more customizable or should add the applications to be embedded into the React app.

I have been using Palantir Foundry for the last 1.5 years.

It is pretty fast, and I don't think there are stability issues.

I believe they should get started by completing all the free certificates, then they could apply to the paid certificates to get a master of Palantir Foundry, solve some real use cases, do examples that Palantir Foundry has provided, reverse-engineer the examples, and then create it from scratch. That basically helps to build the foundation and learn how the platform works. Palantir Foundry is one of the most advanced tools right now, in my opinion, and it would take over the world very soon.

I think Palantir Foundry is a great platform, but the platform is still building, so I have very high hopes from Palantir Foundry that it will take over the world very soon and will be the most useful product available. In the future, I believe Palantir Foundry will do more on it and will succeed.

Palantir Foundry's AI capabilities are pretty safe because the AI is inside our instance. I don't think there is any risk of data leakage and everything stays in our instance. I feel pretty comfortable sharing my data and everything with the AI inside my instance.

Regarding Palantir Foundry's AI capabilities, basically no AI is completely reliable. We still need to review things after the AI has done its work. I would rate it a seven for accuracy and reliability. It does most of the heavy lifting for us, and we just need to review and give the prompts or maybe fix it manually for some parts. That helps.

I would rate this review an 8 out of 10.

My main use case for Palantir Foundry involves performing data engineering while also working on the front-end part, utilizing most of the applications in Foundry.

A specific example of how I use Palantir Foundry in my work involves a front-end application where we have several ontology objects that link to it so that users can edit or modify the objects. These functions include several action types, and I primarily handle these functions. When changes are needed in the UI or when logic changes occur, we have particular environments in Marketplace, specifically UAT, dev, and prod. By using the developer console, we make these available functions possible for React to fetch and use them. It is essentially a front-end position that we support using ontology and action types.

I also worked on another use case where I dealt with Code Repos and SAP data, which involved migration from another data platform. I migrated all the data, and we wrote the use cases and completed modifications of the data in the Code Repository using PySpark. After modifying the data, we developed ontology based on that.

The best features Palantir Foundry offers include ontology.

What I appreciate most about the ontology feature is that it simplifies the process. We do not need to configure linking objects or linking two different objects. Compared to backend databases or other external platforms where we need to link based on values, Palantir Foundry automatically checks whenever we upload new data, ensuring we do not have any duplicate data or mismatches. This feature helps significantly as we do not need to manually validate it.

Additionally, I find that AI helps us significantly. Palantir Foundry has integrated AI on top of every application we use. Whenever issues arise, we can directly use AI to understand the main issue, eliminating the need to go through every log. AI provides a brief summary of what the issue is, allowing us to directly solve the problem without deciphering logs. AIFD allows us to write our code directly, creating and merging pull requests, which significantly reduces our workload.

Palantir Foundry has positively impacted my organization mainly by consolidating our data management. We do not need multiple accounts for different applications. All our data can reside within Palantir Foundry, and we can access all applications from there without changing the data from one location to another. On top of that, all these applications have integrated AI, which understands the data we possess in other applications and allows us to utilize that information seamlessly.

Palantir Foundry could be improved in several areas. Sometimes it takes time to refresh the UI, especially since it is cloud-based. At times, it provides non-specific issues instead of the exact problems we are facing. I believe that is the primary issue to be fixed, along with tracking previous deployments in the developer console. Marketplace DevOps also presents challenges in tracing updates made.

Moreover, there is a limitation in supporting only up to three-dimensional data. My organization needs around four or five-dimensional data, which is why there was consideration to move out of Foundry for that capability. However, my higher-ups prefer not to move data outside of Foundry due to security concerns, which is the limitation we face.

The reason I rate it an eight out of ten is mainly due to the public UI limitations. I did not find options to make the UI public. Anyone using the UI must have a Palantir Foundry account, and there is no way to make the UI or link accessible publicly.

I have been using Palantir Foundry for the past four years.

In terms of metrics, in my previous project using PySpark, we saved around thirty percent of effort and also thirty percent of the time it took compared to the entire run and build. I later built an automation that reduced manual testing every time a new logic or parameters were included. Automation testing reduced our time by about fifty percent, requiring us only to provide the parameters for validation through a build.

My advice for others looking into using Palantir Foundry is that it is beneficial because we do not have to learn multiple tools. Everything can be handled within a single application, allowing us to use various tools without shifting focus to learn additional applications.

Overall, I view Palantir Foundry as a great tool that combines security with all the necessary features within a single platform. I rate Palantir Foundry an eight out of ten.

My main use case for Palantir Foundry is for my current client, where I am dealing with the customer claims system, using both the data engineering and UI sides of Palantir Foundry, managing the end-to-end application flow from the ingestion to the UI side.

With my current company, we have claims sent to the system by the dealers, as it is an automobile engineering company, and we track all these claims, creating ontologies for part returns, incidents, and recurring issues, which allows us to effectively manage the tracking process.

The best features Palantir Foundry offers are data transparency and data flow, allowing us to easily navigate and understand the flow of data from the ingestion stage to the UI stage, alongside the ontology feature which helps in understanding data in layman and business terms.

Palantir Foundry's ontology feature helps my team and the pre-analysts who use the UI workshop by making it easier for us to understand the objects and use cases, allowing us to identify what is behind the issues and gain business-level awareness.

Palantir Foundry has positively impacted our organization by making our processes straightforward and fast-moving, especially with features like AIP, an AI-powered assistance, and the low-code ability to create transformations quickly, which has benefited me significantly.

I am not very aware of the current advancements with Palantir Foundry, but I think improvements could come from proper AI assistance, such as giving prompts to execute the whole end-to-end flow or debugging issues, which would greatly enhance the current workflow.

We do have vast documentation, but I believe it should be more easily accessible, allowing me to quickly find the documents I need without navigating the entire Palantir Foundry learning system.

Palantir Foundry is very accurate, but I would have doubts about its reliability because there have been instances in my current job where we experience untimely application downtime, which has impacted the business significantly, so I think reliability needs improvement.

I have been using Palantir Foundry for three or more years.

Palantir Foundry is very stable.

Palantir Foundry's scalability is substantial; I manage huge data with large transformations, so I believe it is highly scalable.

Customer support is an area they need to improve on; currently, we create tickets, and they respond within twenty-four hours, but I think quicker responses are essential for such a fast-paced tool.

I have never used a similar solution before Palantir Foundry, which is where I got introduced to data engineering, making this my first tool in that field.

Palantir Foundry is far better than all the current competitors or tools out there, though there is still room for improvement. I believe Palantir Foundry is very well governed, as indicated by its use among large companies and government entities, which suggests it is safe and highly governed.

I think Palantir Foundry is deployed on-premise in my organization.

I do not have much insight on pricing, setup costs, and licensing, as I am just a developer.

I would recommend Palantir Foundry; it is a top-notch tool currently available in the market and is a valuable tool for all data engineers. I would rate Palantir Foundry an eight out of ten.

I have worked in two different projects using Palantir Foundry. For the first project, I used to create reports using the AT&T client broadband customers to build dashboards about customer sales development and related items in a single report. For that project, I used some of the transformations and other features in Palantir Foundry using Code Repository. Currently, I am using Workshop to build the application. The second project involves an alert and case manager for the banking alerting system for any AML projects.

For example, in a bank, they need to identify and live monitor the customers' risk categories. If a customer is involved in any AML or money laundering or something suspicious, then using Palantir Foundry, we can stream and get the data from the sources. We can do the transformation using Code Repositories and Pipeline Builder. We can easily identify high-risk customers. Using that, we can create alerts and generate those alerts. Using those generated alerts, we can create the application to check customer details and related information in Workshop to build the application. This will be easy to handle in a single, end-to-end data solution using Palantir Foundry. This will give data ingestion, transformations, and visualization, everything in a single platform. This will be the end-to-end data solution using Palantir Foundry.

We can create any dashboards using Palantir Foundry that will be easy to track our sales or other pipeline developments regarding transactions and other items. This will be more helpful for those purposes.

I would definitely go with the Workshop tool in Palantir Foundry because we can easily create any application, and it will be more user-friendly and also developer-friendly. The Code Repository is a main part because we can develop any of the transformations within Palantir Foundry. This will be more helpful to maintain the ETL transformations and pipeline developments. I can highlight two different tools: one is Code Repository and the other is the Workshop tool.

If I don't know about front-end technologies, I can directly dive into the Workshop tool in Palantir Foundry and easily understand how they are figuring things out. Using the components, I can see how they are building the application. Using Workshop, I can easily get to know about the different features. As a fresher or as a non-tech front-end technology developer, I can easily get to know those things.

Feature-wise in Palantir Foundry, it uses a drag and drop interface. We don't need to do any complex things. It's easy, user-friendly, and also developer-friendly to develop the application.

Palantir Foundry needs more resources to understand and train new, incoming resources to understand the basic knowledge of the entire platform. I think there is a lack of resources on their network or website. They need to upload more up-to-date videos and documents about Palantir Foundry. This will be helpful for new resources.

There are some limitations in Palantir Foundry because sometimes when I need to share an email to any of the features, I face some restrictions. They need to develop some more involved, flexible capabilities to do within Palantir Foundry for the developers.

I have been using Palantir Foundry for four years since 2020.

We can easily get the resources to work in Palantir Foundry and we can easily train resources about Palantir Foundry. There are lots of training courses provided by Palantir Foundry. This will be more helpful for the company. They can easily afford it and they can easily get the resources. We can train them without prior knowledge. This will be more time-consuming and also have good deliverable times. From that point of view, Palantir Foundry is a good solution for working with the project.

In my current project, we assumed we would complete the application development within 16 months. However, we completed the development and achieved the milestones before that. We are currently in the testing environment and completed almost everything in the development in 9 to 10 months instead of 16. We are currently in the testing phase. From my point of view, we more effectively increased the project delivery and the training time. Because of Palantir Foundry's smoothness and features, we were able to achieve that.

Palantir Foundry is one of the most leading and comfortable tools to develop end-to-end data solutions. I would rate this review an 8 out of 10.

My main use case for Palantir Foundry is that we work on the ETL process and create agents and dashboards, so my work mainly involves ETL, Extract Transformation Load, and data processing.

A specific example of an ETL process I worked on with Palantir Foundry involves health sector data where we track various KPIs, such as how much time a patient adheres to a particular therapy. We start with raw data that we process using various transformations, and then we present that data in an aggregated form as a reporting table consumable by dashboards. This is how we used Palantir Foundry for the entire data transformation logic.

The best features Palantir Foundry offers are creating agents and dashboards, as there are many predefined models we can directly use when creating agents, which are very useful. Similarly, creating dashboards is easier in Palantir Foundry.

While creating agents in Palantir Foundry, we use CLAUDE, which simplifies the process significantly. We select the model from a dropdown and define the dataset the agent will utilize. It is a very straightforward task in Palantir Foundry.

We mainly used three things from Palantir Foundry—creating agents, dashboards, and ontology, which is a great feature that connects various data sources. Once the ontology is created, we can create our agents and dashboards and everything else needed.

Palantir Foundry has positively impacted my organization by saving time in creating agents and dashboards and definitely enhancing collaboration. The time consumed in Palantir Foundry is significantly less compared to creating agents using traditional coding languages.

Generally, if it takes around one week to create a full-fledged agent through coding, with Palantir Foundry it takes approximately one day, meaning a rough estimate of saving four days in a week. Regarding collaboration, when you work on a common platform, another great feature is that Palantir Foundry allows collaboration without needing separate Git accounts or other tools since everything is on a single platform.

When we were using ETL with Palantir Foundry, we found we had less freedom compared to Cloudera, where we had more liberty in using various configuration parameters of Spark, allowing us to tune our jobs accordingly. In Palantir Foundry, we realized that ETL is not a great use case while creating agents and dashboards was better. We faced many challenges in ETL that we did not face earlier in a big data Hadoop environment, such as Cloudera; we eventually removed that ETL process.

I feel that if developers had more freedom to experiment, it would enhance Palantir Foundry. Specifically, if the ETL process could provide more freedom concerning Spark configuration parameters, that would be beneficial.

I worked on three parameters: dashboards, ETL, and creating agents. The latter two functions really work well, but we faced challenges specifically in ETL. Additionally, the costs are substantial; I believe making Palantir Foundry more approachable for the general public would be beneficial.

I have been using Palantir Foundry for around one year.

Palantir Foundry is stable.

Its scalability is good and pretty scalable.

Customer support from Palantir Foundry is also good; we collaborated well with their team.

We were not using any different solution before; we were using Cloudera for ETL and big data and wanted to give Palantir Foundry a try.

We did not evaluate any other options before choosing Palantir Foundry, as these decisions were mandated by our company, and we heard good reviews about it.

I don't have information on return on investment metrics; it's too early for such statements, as we have only been using it for one year and spent much time experimenting, so I don't think we have ROI at this stage.

Regarding pricing, setup cost, and licensing, while those are determined by our admins and upper management, I know we are paying substantial charges, and if those could be reduced, it would be more accessible for people.

I would advise others to start using Palantir Foundry and experiment thoroughly before signing any contracts. My review rating for Palantir Foundry is six out of ten.

I serve as an integrator with Palantir Foundry, using the application on behalf of my company.

In my usual use cases of Palantir Foundry, there are two to three categories, with one being data governance. For that in Palantir, I have administrative purposes and two tools through which I keep control over who can access different data. For data integration, I use a data connector to bring sources from other places, such as Excels or different sources to Palantir Foundry through data integration. I also perform code conversion in the Code Repository, migrating SQL formats to PySpark.

We built around 120 datasets, using them to create schedules in Data Lineage by adding the pipeline sequence. Data Lineage helps us schedule and conduct health checks in Palantir Foundry based on data freshness and building statuses. Additionally, I validate data using Code Workbook, where I can import sources for transformation using SQL, Python, or R code based on requirement, though I mostly deal with SQL codes. In terms of application development, I work with Pipeline, Workshop, and Ontology Manager.

I prepare the Workshop front end using Ontology and Pipeline to build a back-end application structure, managing what columns will retrieve data from the back end. Ontology Manager intermediates between Pipeline and Workshop, allowing us to design user-relevant widgets for the front end quickly, within an hour if needed, working mostly in a no-code environment.

From my experience with Palantir Foundry, the most useful feature is its no-code, low-code environment, allowing me to develop applications without extensive coding, unlike my past as an Oracle developer where I had to write code for various functions.

I participated in the initial setup of Palantir Foundry by working with the Code Repository and Code Workbook, which involved a lot of code using PySpark, and later transitioned to the Workshop, learning through courses and YouTube videos available on the Palantir Learn portal.

I found the learning curve of Palantir Foundry initially difficult as it was new to me, but once I started to follow the videos, I began to grasp the core functions and that made it easier to progress through all applications, including Workshop and Pipeline Builder.

Palantir Foundry could be improved by addressing the need for some coding in the Workshop since we cannot expect 100% no-code functionality, especially when dealing with dynamic user input, which requires writing functions in the Code Repository.

I have been using Palantir Foundry for around four years.

I have communicated with the technical support of Palantir Foundry since my company directly connects with Palantir collaborators for assistance with implementation challenges, receiving help through ticketing and weekly staff calls where we address any issues or concerns.

Before using Palantir Foundry, I used Oracle for my projects.

I decided to stop working with Oracle based on my project requirements, and once those projects were completed, I transitioned into Palantir Foundry and discovered its range of features for application development.

I participated in the initial setup of Palantir Foundry by working with the Code Repository and Code Workbook, which involved a lot of code using PySpark, and later transitioned to the Workshop, learning through courses and YouTube videos available on the Palantir Learn portal.