Palantir Platform

I use Palantir Foundry to ingest data and create visualizations for decisions.

My favorite thing about Palantir Foundry is the ability to utilize multiple different types of coding languages and the current updated documentation and help features that are in there have improved pretty significantly and make it rather friendly for use.

The scalability of Palantir Foundry is the part that I love the most. That is why I said that is where my primary focus is, trying to get data and then turn it into information and knowledge and understanding, and more importantly, getting decisions out of it. The scalability is phenomenal.

I think the things that I do not like about Palantir Foundry is not a Palantir issue so much as it is from my company side and what they have commissioned for and have not commissioned for.

With Palantir Foundry, it is a part of the user tools that they provide. Their AI Assist that they use is something I have found that sometimes I get better results for when I do need help and aid. I get better results going to outsourced AI assistance sites such as Gemini, because it seems like AI Assist, which my understanding is it is supposed to be searching and utilizing the documentation for Palantir Foundry, but sometimes it gets kind of confused in the capabilities and it will tell you, 'Oh, you can use this and do this.' And then when you try it, the system says, 'We do not support this.' Whereas I can go to Gemini and it will give me a workaround that will actually work.

I have been using Palantir Foundry for about four years now.

I find the stability of Palantir Foundry to be extremely stable. I have had only one time period where there was any downtime that I noticed, and it was for a very short period of time. When I say short period of time, I am talking within hours, not days.

I have contacted the technical support or customer support of Palantir a couple times.

Especially for what they are supporting and doing, I find the quality and the speed of the support to be extremely fast. I usually get within a day turnaround and the support staff are extremely knowledgeable and good at what they are doing.

Prior to using Palantir Foundry, I used various different SAPs and ERPs where I would actually have to export worksheets and then build my own. The closest thing I would be building would be databases that just resided on my computer, and I was not using anything like Palantir Foundry.

The initial deployment of Palantir Foundry was super simple.

It did not take me any time to fully set up Palantir Foundry because it was made available to us and it was basically already set up and rolled out by the time I got permissions to use it. All I had to do is basically create an account. Actually, I did not even do that. Someone else in my organization created the account for me with the initial setup of everything. By the time I logged in and went to use things, most of it was already initialized for me.

Palantir Foundry requires no maintenance on my end as it is taken care of by Palantir. The only thing I ever get, and Palantir already does it, is when there are certain upgrades to transformations that I have made, they will put in the upgrades, but it requires me to actually approve them and merge them in. It is minimal work on my part. Usually I can click it and just approve it right away.

I would rate this product a 9 out of 10.

My main use case for Palantir Foundry was with respect to the customer onboarding process, which had been outsourced to multiple teams, and there were multiple data sets that had been outsourced to different teams. Some of the teams were third parties, which were creating a backlog and elongating the process. My job was to streamline all the data into one universe, utilizing Palantir Foundry to centralize the data and information in one place to find insights where we were having backlogs for smooth customer onboarding. My objective was to utilize Palantir Foundry's data centralization capabilities to identify backlogs and situations where we were lacking.

For example, in this process, data was coming from various teams responsible for different objectives. For onboarding a new customer, there is a process known as KYC, where we need to identify the customer's details, which are usually transferred to third parties, and some in-house KYC teams must review them. Generally, when third-party cases come to the banks, they are unable to find out why a particular customer is not being onboarded. I tried to merge the data into a central place to find out the reasons, such as why the data is not being updated from third-party sources. The information sometimes lacked advance details from third parties and other sources. Additionally, I was able to map the entities, including cases where customers were not onboarded due to specific reasons related to their past profiles with other banks. These scores were not highlighted by the sales representatives, who were told to meet targets. I was able to track these and identify the pain points of why a customer had not been onboarded or entertained by the banks.

One of the best features Palantir Foundry offers is the Ontology part. It allows mapping different data from different domains and teams into one place, creating relationships between them. For example, I was handling sales data, but there was also customer profile data, financial profile data, and previous transactions data, which I mapped in one place.

Palantir Foundry helped by creating a reporting system that was fast. Previously, we waited for at least a week, but now everything was available in one place with all insights.

Palantir Foundry's governance and security are good, which is a main aspect that people prefer. Providing online data of in-house security to clients would be beneficial amidst increasing cyberattacks. Palantir Foundry's use has been impactful as the company is a vendor for a banking client who utilized it effectively.

To improve Palantir Foundry, I would recommend integrating AI capabilities. Although AI is currently available, having simple general language query features would be useful for end-users. Palantir Foundry's unique selling point is creating a universe, which should remain, but adding a query option would be beneficial.

Palantir Foundry's reporting capabilities can also be enhanced to function more in the manner of a BI reporting tool, which would be a valuable addition.

One downside is the absence of many users familiar with the platform. The cost of using Palantir Foundry is another factor that clients consider, which I accounted for when rating the solution as eight out of ten. Palantir Foundry should address pricing to make it more competitive.

Training resources for Palantir Foundry should be increased to facilitate product growth and build a community that can expand its use.

I have used Palantir Foundry for one and a half years in one of the projects for my banking client, where I worked upon the creation of pipelines in Palantir Foundry, replacing their absolute systems.

Palantir Foundry's scalability is such that it can handle growing data and user needs easily. The implementation was not very complicated because our in-house domain data was not that large. I did not find any issues with stability.

Palantir Foundry's scalability is such that it can handle growing data and user needs easily. The implementation was not very complicated because our in-house domain data was not that large. I did not find any issues with scalability.

My experience with customer support for Palantir Foundry is that I have not interacted with them much. I did have a single interaction when I faced an issue with my login credentials. The chat system available was helpful, and the problem was solved efficiently. It was a satisfactory experience.

Before using Palantir Foundry, we had our in-house centralized reports, but there was no centralized system that connected different domains and data in the manner that Palantir Foundry does. This connectivity is a key benefit that Palantir Foundry brought to us.

Before choosing Palantir Foundry, we considered Microsoft Fabric as we were using Power BI. However, I found Palantir Foundry more useful and stable, leading us to choose it over the former.

Both time and money were saved since a project that would usually take six months was completed in two to three months. This allowed us to allocate time to other projects as well.

My experience with pricing, setup cost, and licensing for Palantir Foundry was challenging for us. Getting approval for pricing and setup cost was difficult. We obtained initial phase approval, but later budget requests for other projects were not accepted by the board. Palantir Foundry should reconsider its pricing strategy to become more competitive.

Before choosing Palantir Foundry, I truly thought of Microsoft Fabric because we were already using Power BI. In comparison to Palantir Foundry, I found it more useful, and it was stable, so we went with Palantir Foundry.

My advice to others looking into using Palantir Foundry is that they need to be clear on what aspect they want to utilize Palantir Foundry for, whether for app development or data connection. Without a clear objective, using Palantir Foundry might not be beneficial. Those with the need to integrate different partners and domains into a common platform will find it valuable.

Palantir Foundry should provide more training resources for people who are in the market so that the product can grow. Additionally, consideration should be given to building a community to expand Palantir Foundry's use. I rated this solution an eight out of ten.

I am currently a data analyst using Palantir Foundry as an end-user for developing apps, forums, dashboards, and manipulating the data internally.

My usual use cases for Palantir Foundry mostly involve developing specialized apps, particularly in Palantir Slate. I am deeply using Palantir Slate, developing forums, dashboards, and specific applications. I currently have four short videos on my PC showing what I have accomplished in Palantir, although they are blurred due to company policy. On the technical side, I do the coding in JavaScript and develop using HTML and CSS to create my own widgets. Additionally, my first use case for Palantir Foundry was using the Contour app and the report app, and I can share those videos if you write to me on my LinkedIn profile.

The most valuable features of Palantir Foundry for my use cases are the Slate application and Contour, as the latter allows me to manipulate data ninety-five percent without knowing coding. I can transform data however I want and use the data set along with the pipeline builder to manipulate data flow and updates. My favorite tool remains Slate for developing forums and dashboards exactly how I want them to look.

I have noticed that Palantir Foundry cuts down the time of usage by automating forms and dashboards. However, a disadvantage is the challenge of connecting data to and from Palantir, which requires coordination with data engineers. It takes time, and a data analyst should not be responsible for connecting data; that is the role of the data engineer. I handle data manipulations myself based on customer requirements, ensuring I know how to aggregate, manipulate, and summarize it.

While I do not have specific suggestions for improvements or enhancements, after using Slate, I realized it provides the freedom to create websites. However, I found the Object Explorer and related apps limiting, as they confine me within boxes, restricting proper manipulation. If the Object Explorer and its related apps offered the same flexibility as Slate, it would greatly improve the experience. Although one does not need coding knowledge for Object Explorer, an understanding of primary and secondary keys is still required. Those unfamiliar with coding may require specific training for Object Explorer. Slate, by contrast, requires coding knowledge, including JavaScript, HTML, CSS, and how to connect data within and outside of Palantir Foundry. With this knowledge, I gain full flexibility in developing websites and can create my own widgets using CodeSandbox, which works much faster than using the predefined Palantir Slate widgets. For example, Palantir Slate allows about one thousand two hundred widgets, and performance suffers when limits are exceeded. However, by creating an HTML table with multiple widgets in CodeSandbox, performance remains excellent, illustrated by my application with over one thousand five hundred widgets on one page.

I have been using Palantir Foundry for about seven years.

The reliability and stability of Palantir Foundry is great; it remains stable as long as I do not accidentally create an infinite loop, which can cause the application to freeze. With a stable internet connection, everything works perfectly.

Palantir Foundry has proven to be a great tool in terms of scalability for me, especially compared to Power BI, which felt inadequate. Its scalability depends on the Slate application; essentially, I am only limited by my imagination. I have not explored machine learning features yet, such as three-dimensional visualization in Palantir Foundry, but in two-dimensional space, when developing forms or dashboards, I can copy and paste data from Excel, which then builds visual representations and drawings, demonstrating that the only limit is my creativity.

I communicate with the technical support of Palantir Foundry less frequently these days. My last interaction occurred when I created a forum for BP's data request support, where some users did not receive email notifications. I discovered that the issue stemmed from a Microsoft connection limitation affecting users connected to more than one thousand distribution lists. I have advised those affected to reduce their connections to stay below the threshold, which should allow the system to properly update and ensure they receive notifications.

Regarding the technical support team at Palantir Foundry, I would rate it around seven. It varies depending on the level of support and the responsiveness of the team. Sometimes it may take days to hear back, while other times I receive answers in as little as fifteen to twenty minutes. Developing apps using Slate can be particularly challenging since not many people have experience with it compared to other applications. 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.

Before Palantir Foundry, I had some experience with HTML and CSS, as well as Macromedia Flash long ago. However, I mainly used Microsoft Excel, which I enjoyed and used to develop a forum in Excel.

I decided to stop using Excel and start using Palantir Foundry because my ex-line manager suggested I try using Palantir to manipulate data and create reports. Gradually, with some support and visits from Palantir representatives in Azerbaijan every few months, I learned more about how to use it. I even took a Python course on Coursera from the University of Michigan and completed an eighteen-month data analytics course supported by BP, where I also learned about machine learning that I may use in Palantir Foundry someday.

I did not participate in the initial setup of Palantir Foundry or the installation process. I consider myself an experienced user, as colleagues come to me in Azerbaijan for questions about applications. While I can help with applications I have developed, if responsibility falls to someone else, I need to take time to review and consult with my line manager to understand it better.

Palantir Foundry was suggested to me by my manager, and as I started using it, the adoption increased, with more apps being integrated. I have heard that BP also uses AWS, but it seems somewhat limited. I directly asked if there is anything in AWS that is similar to the Slate application, and they mentioned there is one, but BP management did not want to pursue it for some reason. I am a developer and coder, so I cannot elaborate on that.

I do not know anything about the pricing of Palantir Foundry; it is managed by the company's finance management. I am a user, but after using Palantir Slate, I noticed a large advertisement within the company concerning Power BI, which I strongly dislike. It seems that anything free, such as Power BI, cannot be sufficiently effective. I would rate this review with a rating of nine overall.

Palantir Foundry serves as our data platform for the company, which means we have numerous use cases and business cases that cross all the different business groups, subsidiaries of the company, and also different support functions and business functions of the corporate. We have more than 200 use cases in the corporate because Forvia is a very big company. The main use case is to enable the data value and data product for our corporate and for our business.

The main purpose of the data platform is to have a good return on investment based on IT digital dependencies. From a business point of view, I will give you a good example of purchasing. For the purchasing side, purchasing has two types: direct purchasing and indirect purchasing. Especially for the direct purchasing part, previously, we could not know that all the purchasing data management was quite siloed. With Palantir Foundry, we break the data silo to make all the different data which comes from the purchasing department globally, which have acceleration with the data sourcing assistant and AI sourcing assistant, to help our business accelerate their purchasing business transformation and to achieve excellence in terms of purchasing goods price. This helps us, at the same time, to speed up for the purpose of time saving, and it helps our business to accelerate all the price transformation strategy with our suppliers. That is a good benefit.

Not only for the purchasing part, it is also for the processing side in the operation and for the industrial operation, because Forvia is a manufacturing company. We have many data use cases in the plant. Globally, we have 500 plants and factories globally, which have many critical operations on the factory plant side. For example, the predictive maintenance with the data coming from the shop floor from the plant side helps us to have a good level of understanding of the different machine statuses of the plant.

This is the data-driven enterprise strategy. Since five years ago, we started our data program and launched the data-driven enterprise. This strategy has changed our HR organization, meaning we need to apply change management to accelerate because we are facing the change of data and AI. With Palantir Foundry, it helps us to accelerate this change management in our corporate, which is quite positive.

Time saving, budget saving, and cost reduction are benefits we have experienced, along with accelerating decision-making for the target, because Palantir Foundry with the data is quite useful. It helps management make the right decisions in the market, especially in the current situation where all the competition in the automotive market is quite complex. With Palantir Foundry, it helps us have better benefits and better return on investment, and also accelerates the right decision in the market.

There are three good features which we have applied until today in Palantir Foundry. The first one is, of course, all the data product features from Palantir Foundry, with all the different data pipelines, which helps us to have end-to-end data product experience with Palantir Foundry. The second one, relative to the previous benefits about data product, is that we have a good level of data ontology, which is a data catalog that helps the business people to understand better their data in a functional way. The third part is the AIP usage, because Palantir Foundry has the AIP feature, AI platform feature. With AIP features, we could accelerate our AI transformation and also develop our own AI agent with Palantir Foundry.

Palantir Foundry needs two points for improvement regarding the data product. First, Palantir Foundry needs to improve their clear resume about their product features roadmap. Second, Palantir Foundry needs to have a closer connection with the enterprise corporate application, which means the business application, because big companies have a very huge ecosystem of business applications. In my personal perspective, I think Palantir Foundry still has some space to improve in integrating with the IT landscape of the corporate.

I want to say that Palantir Foundry is quite expensive. It is not so easy for budget review and budget transformation of the company, which is quite expensive.

In terms of my experience with Palantir Foundry, I have been using the Foundry product from Palantir for more than five years already.

It is stable.

The scalability is good, but we need to pay for the compute and the resource.

The customer support is fine. We have the Forward Deployment Engineer, FDE, with us on site, but once again, it is quite expensive for the daily price of the FDE engineer. I think we need to rely on classical support by using a ticketing system of Palantir.

We previously had Cloudera in the company as the data lake solution.

At this stage, it is fine.

We don't have a migration plan.

I cannot give you the details of the money saved because it is quite confidential. What I can tell you is that return on investment is quite good, but Palantir Foundry is quite expensive and it is difficult to have a good budget for Palantir Foundry. That is the reality.

At this stage, it is fine.

At this stage, it is fine.

I have two suggestions for other companies looking to use Palantir Foundry. First, you need to understand how Palantir Foundry integrates with your IT system landscape before choosing to use Palantir Foundry. Second, you need to define and design good governance for Palantir Foundry usage for your data platform. I have rated this review with a score of 8.

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.

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.

My name is Emy, and I come from Morocco. I was with Stellantis, and my key responsibility is to collaborate with our clients to centralize data in Palantir Foundry because their data is fragmented, and they want to put it in one platform. I am responsible for end-to-end data, performing the ETL process. I am also responsible for creating dashboards, KPIs, and other tasks like automation and sending notifications and engineering reports, and I can say that I covered 90% of Palantir Foundry while working with many services.

I was responsible for data engineering, meaning I treat the data that comes from other platforms like Databricks or Snowflake, making a pipeline ETL to get insight data and generate the object type.

We built a tool similar to Jira that is responsible for following up on requests and all issues our users find in Palantir Foundry. Our clients undertake many projects, tools, and dashboards, and we try to standardize them in one tool called release case, which we developed from scratch. For example, if a user finds a problem or wants to add a new feature, they can create a request, and then I receive a notification. I can switch the request to in progress and work on it, and when I finish, I will switch it to done and send a notification to the user that the feature is added.

In our case for the ETL process, we use PySpark in the Code Repository. After cleaning our data and getting good data, we switch it to the object type using ontology. We also use various services like Workshop to create dashboards, Slate for a custom homepage, and Automate to send notifications, take action, or schedule actions. Additionally, we use multiple data pipelines to check the links and interactions with the data set and show all the transformations and governance permissions. We also use Notepath to generate our PDFs or weekly reports. Other services such as Object Explorer help us to show our data and investigate issues to understand the data better, which is really useful. Moreover, we use TypeScript to generate the user interface (UI), which is great.

I must mention that we have found difficulties in generating reports because Notepath is limited. For example, you develop a dashboard using workshops, and when everything works well, the key reference or the client needs to generate a report based on that dashboard, but in Palantir Foundry, there is no solution currently available. I searched and found no solution. I sincerely hope to see that feature in the future, which would allow switching a workshop to a report.

The strength of Palantir Foundry is that there are no limitations. It is well organized, and the data is very secure. One strong point is that you can add restricted views on each data set by using marking and security levels. This helps us to organize critical data.

We save time in creating dashboards using Workshop and also in the KPIs. There is also another service, a no-code pipeline called Pipeline Builder, which I personally have not used, but it appears really useful for saving time and achieving good results.

Palantir Foundry offers AI, which is a really useful tool to create your agent or model, but I have not had a chance to use it due to a lack of permission. I also want them to add a forum for learners to practice more in Palantir Foundry. For example, I often find limitations and boundaries that slow down my learning and hinder my discovery of new features they add.

I believe that the AI or agent needs improvement because sometimes we face difficulties when looking for solutions, and when we ask the agent, AIP, it does not understand our queries and occasionally provides wrong solutions.

We really need to add a service dedicated to documentation because, in our case, we have developed many dashboards, but there is no documentation. I genuinely hope in the future, there will be a service purely for documentation that can be linked with a dashboard. When you go to the dashboards, it should provide you with all the coding history, all the added functions, and also the pipeline, offering deep insights into the project or dashboard.

I have been working as a data engineer for about three years since 2021.

Palantir Foundry's scalability is really useful. You can scale in and out, and you can control your metrics and resources such as the amount of compute and storage you require.

My rating for customer service is 3.

I would advise that Palantir Foundry gives us the permission to get a trial and practice more. It is really useful and opens the door to learning more about other features. For example, in my country, Morocco, I do not have permission to get the trial of Palantir Foundry, which significantly slows down my learning and impedes development and creativity.

Overall, the platform of Palantir Foundry is really strong compared to other platforms, but it can be quite complicated. I sincerely hope to see more attractive learning resources or documentation that can assist users. My overall rating for this review is 7.