My main use case for Palantir Foundry is pipelining and analyzing data there.

I replace the existing pipelines with Pipeline Builder in Palantir Foundry. I have various data flows and production of national reports, and I am replacing that using Palantir as part of an NHS Federated Data Platform. In terms of analytics, I use it to check data consistency and test it against what I have in other systems. People also use Quiver and Contour.

That is pretty much everything I have to add about my main use case or the way my team interacts with Palantir Foundry.

My background is in Databricks, and if I compare Palantir Foundry to Databricks, I see benefits of Palantir Foundry in that they make it simpler to configure clusters or at least to manage some infrastructure. If I think of Foundry as being an implementation of Apache Spark and compare that to Databricks, it is easier for an organization to use Foundry. I would also say that pipelining itself is more drag-and-drop style.

It is obviously easier to start with Palantir Foundry. I get more things managed by Palantir themselves. If I have a team with mostly SQL background and I want to move them to a Python, PySpark environment to use clusters, obviously using Palantir Foundry is an easier option than using Databricks.

There are pros and cons, obviously, regarding the features of Palantir Foundry. If I get stuck with the drag-and-drop nature of Pipeline Builder, it is going to be more difficult to migrate that to a different platform. From a Python coding perspective, even if I don't use much of that, I would say Databricks is probably better.

It is difficult to say how Palantir Foundry has impacted my organization positively. Palantir helped me migrate some data into the cloud. Whether they indeed impacted my organization positively is not clear because of Palantir's appalling reputation. So it is not that easy to say. If it was my choice, I wouldn't sign the contract with Palantir in the first place. I would probably stick to standard Databricks.

I wouldn't add more about the needed improvements, either on the technical side or regarding compatibility and integration.

Obviously the company's reputation needs to be improved regarding Palantir Foundry, or ideally, Palantir needs to get away from the appalling views on human rights and improve the reputation. Whether it can be improved, I don't know. This is not a technological problem; it is a problem of company image, so I wouldn't be surprised if the NHS actually triggers a break clause in the contract in February next year. That is not linked to the product itself. From a technical perspective, maybe to make Palantir Foundry more compatible with Databricks could be one option, or maybe more integrated with Azure. It is difficult to say because they might lose some of their competitive advantage in doing so.

I have been using Palantir Foundry for about nine months.

Palantir Foundry is okay in terms of stability. It gets sometimes occasional issues, but compared to Databricks, it is probably the same or may be better. However, as maybe one of early adopters, I get more technical support from Palantir, and I am maybe in a honeymoon phase. It is stable.

I don't know the answer to the question about Palantir Foundry's scalability, really, because I didn't test that. My assumption is that it is correct. I don't know the financial side of it, the cost. I can't judge that. To the best of my knowledge, it is not worse than Databricks.

Customer support for Palantir Foundry is okay. However, I am in this potentially better supported phase by Palantir Foundry. I am not admins on my tenant. What I have in the current environment is that Palantir Foundry themselves are running my tenant and configuring clusters and doing things that I need, simply because I don't have permissions. So when I get to that point of being admins on my own tenant, then I may be able to provide more information about that.

In the current organization, I used legacy on-premises and cloud databases, and I used and still use Microsoft Azure before I switched to Palantir Foundry. I haven't fully switched yet, but the decision to overall switch was based on the momentum to go to this FDP, a Federated Data Platform. I believe it was a financial incentive to do so because from my organizational perspective, I am not paying for cloud space that I use in Palantir. I only pay for computing, but even that is probably covered by a bulk contract.

I didn't purchase Palantir Foundry through the AWS Marketplace because in my case, it is a part of the contract between NHS England and Palantir. I don't know if they procured it via AWS Marketplace or not.

My company does not have a business relationship with this vendor other than being a customer.

I haven't seen a return on investment with Palantir Foundry. I wouldn't even see the financial data. It is very difficult to judge for me. That is why if somebody would ask me whether Palantir Foundry in the NHS is value for money, it is difficult to answer that question.

I have no experience with pricing, setup cost, and licensing for Palantir Foundry because for me, essentially, it is free. NHS England pays for that as part of their procurement process. That is why I can't answer this question. But 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.

I didn't choose Palantir Foundry; I wouldn't choose them. If it was my choice, I would probably go for Databricks or even stay with Azure and try to see if I could use Spark there even without Databricks.

First of all, I would advise others looking into using Palantir Foundry to ask themselves if they want to use it given the reputation of the company. You don't need to use Palantir Foundry per se. The second consideration would be whether you want to use Databricks or any other implementation of Apache Spark. It would be interesting to see if you prefer the drag-and-drop nature of Pipeline Builder as opposed to, say, notebook structure of Databricks. So it might be a choice. I would probably say talk to your data engineers and ask for their opinion. Take that into consideration as well. Factors to consider include if you implement Palantir Foundry as what they consider a default option, you are likely to be very entangled into the product. It would be difficult to decouple in the future. That is why they are very sticky. That is probably one of the issues the NHS will get with the product in the future. My overall rating for Palantir Foundry is 6.

The AI engine that comes with Palantir Foundry is quite interesting. We have a lot of data from various trials and analyses. We need a machine learning and analytical feature that can push huge amounts of data into the application based on pre-set rules.

Palantir Foundry is emerging as an appealing healthcare platform. Instead of having multiple tools, we can leverage this platform for our data ingestion. The solution has a couple of modules, and we haven't evaluated the entire spectrum. We are taking it one bit at a time. I don't yet have a complete vision or impression, but the tool has served our purposes.

The AI engine that comes with Palantir Foundry is quite interesting. The solution provides good flexibility and scalability.

Palantir Foundry is very good for someone technical. The tool still needs to work on the non-technical part, where people can use its flexibility. The business user should not end up writing huge queries to get small snippets of data. The solution's visualization and analysis could be improved.

I have been using Palantir Foundry for over a month.

I rate the solution a seven out of ten for stability.

Palantir Foundry is a scalable solution. Medium-level businesses can be a good starting point for Palantir Foundry, but it is definitely for enterprise businesses.

The solution’s initial setup is very simple since it is cloud based.

Palantir Foundry has been very forthcoming, but we don't have a full picture of their roadmap and what it would be built upon. It's more of a partnership where we talk about our use cases, and their team comes and tells us about the features we can use. So, it is a work in progress.

You can connect Palantir Foundry to various LLNs like Google Bard, Llama, or OpenAI.

Overall, I rate the solution a six out of ten.

Palantir Foundry is being used for multiple hybrid cloud integrations in one of the services we provide for an existing US-based customer. It's all about getting together data from Azure and Amazon and then providing a hybrid platform through Palantir Foundry. We then provide the analytics or insights enablement for the customer.

Palantir Foundry is a robust platform that has really strong plugin connectors and provides features for real-time integration. The solution is more scalable and robust compared to other hyperscalers.

The solution's pricing is high. Compared to other hyperscalers, Palantir Foundry is complex and not so user-intuitive. There could possibly be a little bit of overhead concerning the maintainability of the platform.

We started using Palantir Foundry since last year.

Palantir Foundry is a stable solution.

Palantir Foundry is a scalable solution. We are supporting the implementation of Palantir Foundry for at least three or four customers who have chosen to go ahead with it.

The solution’s pricing is high.

Palantir Foundry is a cloud-based solution. I would recommend Palantir Foundry to other users based on their use cases, the complexity, and the scale of the platform they're looking for. Palantir Foundry has all the right ingredients in terms of the overall data platform, but it depends on the kind of use cases that customers are looking at.

Overall, I rate Palantir Foundry a seven to eight out of ten.

Our company uses the solution as a big data lake for storage and cherry-picking data sets using multiple languages. We create ETL pipelines, run them on a schedule, and export the data to visualize it.

We perform functional tests on the data sets using Excel in a Fusion Sheet. A schema is created that shows all data in columns and can be manipulated to extract meaningful information.

The Code Workbook is used to import data and write code using R, Python, Spark SQL, or PySpark. From there, you can perform calculations and create data sets.

Contour is the graphical user interface that gives us the available basic or automatic operations. You do not need a technical grasp because it is easy to use with knowledge of the basics and filters.

Across our company, there are 3,000 users who access our data lake.

The Code Workbook gives you the option to switch across built-in languages such as Spark SQL, PySpark, R, or Python.

Live video sessions enhance the available documentation and allow you to ask questions directly. There are a multitude of sessions within each framework that occur weekly. At the end of a session, you have the option to read other user's questions or ask questions yourself.

The GUI is easy to use and does not require advanced technical knowledge.

There is not a wide user base for the solution's online documentation so it is sometimes difficult to find answers. It is easy to find answers for code issues because Spark SQL and Python have wide user bases. There is a certain probability you won't find a solution-specific answer if you search for it. For example, there are certain errors that are specific to the solution. The more you use the solution, the more you understand it. The learning curve could be reduced with online documentation that includes the meaning of and troubleshooting for error codes.

Predefined code templates or informational prompts would help with writing syntaxes.

I have been using the solution for fourteen months.

The solution is very stable. On occasion, we receive an error but it is rectified within a few hours.

We create use cases that do not have processing limits. The solution is a big data tool so should handle any scalability.

I have not needed technical support.

I previously used Microsoft SQL which is a traditional database. The solution is an advancement because it is a direct jump to a big data source.

Comparing the solution to traditional databases is liking comparing an apple to a banana.

A different team handles the solution and our data lake so I don't have knowledge of the setup. Our team accesses the solution via a web link and creates use cases.

The solution has many features but I am only using the Code Workbook and Contour. Another feature called Slate allows you to create websites or record user data.

Based on my current usage, I rate the solution a seven out of ten.