Palantir Foundry Reviews

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.

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.

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.

I did not purchase Palantir Foundry through the AWS Marketplace.

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.

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 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.

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 building a targeting system for NATO and allied forces using Maven. A specific example of how I'm using Palantir Foundry in that targeting system project involves ingesting data from ISR drones and from satellite systems, streaming those data as part of data ingestion, building the pipelines, running the analytics, and building the dashboard.

The best features Palantir Foundry offers for my work include that building the ontology is very easy and it is easy to use.

The easy ontology feature helps my team because it is faster to build, and when anything goes wrong, I can see that it is easy to do the debugging.

Building the analytics dashboard helps and makes it very visual for the end user, and it is very easily customizable. Palantir Foundry has positively impacted my organization because we are mostly still building POCs for the end client, so when we show the working solution, it is easier to get feedback and iterate.

When I demo the solution, there is a faster feedback loop and more engagement from clients, which impacts my team's workflow and project timelines because we can get a solution built in a couple of weeks.

Palantir Foundry can be improved by providing more information online. For example, it would help if Palantir could put more updated tutorials and knowledge articles on YouTube since some of those articles are outdated, and up-to-date information will actually help.

I have been using Palantir Foundry for more than a year.

Palantir Foundry is stable in my experience. I would add that live data streaming is very hard and it keeps breaking, so it is not very stable and depends a lot on the satellite network.

The scalability of Palantir Foundry is very good.

The customer support for Palantir Foundry is good.

I have not used any previous solution; what I am working on is very unique to Palantir Foundry.

I have seen a return on investment; specifically, fewer employees are needed.

My experience with pricing, setup cost, and licensing is that it is flexible.

I did not evaluate other options before choosing Palantir Foundry.

My advice for others looking into using Palantir Foundry is to start building and get quick feedback. I would rate this product an 8 out of 10.

The main use case for Palantir Foundry is to build dashboards and pie charts, with applications like Workshop where I use Pipeline and Workshop on a daily basis for data integration, making objects, and building dashboards.

In my daily work, I ingest the data from the data connection into the pipeline builder, clean in the pipeline builder, and make separate pipelines for the cleaning. After cleaning the pipelines, I integrate them. Following the data set creation, I integrate those and make the object by combining it with other objects as per the client's requirement, such as showing images of the dashboard's results. I use Ontology to show dashboards, pie charts, maps, and quick results from Workshop by bringing images from S3 to display in Workshop.

For the data sets, if I have a new website for mining, I have to make an identical data set for it. Thus, I have used Pipeline Builder mostly to make a couple of data sets with similar identical fields present in the website, which must also be in the data sets, focusing on two crucial joins and unions.

The best features Palantir Foundry offers include the semantic layer providing schema-level understanding about the data, low-code and no-code integration for ease without coding in the pipeline builder, AI Assist for assistance, Ontology for digital twin relationships, branching in pipeline level and Foundry branching for better management, zero-copy architecture for querying without massive data, data lineage for troubleshooting, and security changes that can be made in the pipeline builder and Ontology Workshop.

I find myself using the branching mostly in the pipeline branching because it is very helpful. In Workshop, there is versioning that saves every draft. If I make a mistake in the current production, I can easily take the previous version and correct the current version, preventing errors.

What I found interesting about Palantir Foundry is its media set capability to ingest unstructured data and apply changes, making it simpler for showing in Workshop, which I use frequently.

The low-code and AI assist help me get unstuck, make changes in configuration, and reduce complexity, aiding better time efficiency for the team.

Sometimes, when I am handling a huge amount of data, I notice areas for improvement.

The performance of Palantir Foundry could improve, particularly due to the high cost of building data pipelines, detailed security policies, and the heavy setup for small to medium-sized data sets.

Regarding complex data, improvements are needed for efficiency.

I have been working in this area for three months.

Palantir Foundry is reliable and stable, but handling huge data and complex data can make operations slower.

When dealing with huge data sets, it takes time to load and handle those larger workloads.

While using Palantir Foundry, I have not faced any issues requiring customer support.

I switched from Azure Data Factory, which focused on cleaning the data and pipelines, to Palantir Foundry due to its more efficient features like branching and versioning in the pipeline builder.

I switched to Palantir Foundry during a mining project after comparing it with other applications used for replicating data sets and creating dashboards.

I have not seen a return on investment with Palantir Foundry since that information was not shared, and currently, we have only two employees for the project, leading to needed savings.

Pricing for Palantir Foundry is high compared to other cloud solutions. Setting up typically utilizes existing Palantir setups from clients to integrate data for building dashboards and insights.

I cannot advise someone to use Palantir Foundry due to cost efficiency and the complexity it introduces in handling large amounts of data. I gave this review an overall rating of eight.

My main use case for Palantir Foundry is for data transformation using the PySpark code through the Code Repo or Pipeline Builder, so the majority of the time, I am transforming data based on business needs.

A specific example of a project where I used Palantir Foundry for data transformation involves cleaning data from across the world in the first phase before processing any of the business logic, which depends on the quality of the data received. I adjust timestamps, understand data types, and filter out inactive records, so all these cleaning-related tasks are very important when building pipelines in Palantir Foundry. With respect to business logic, it varies case by case, where I might need to conduct time series analysis, patient records, or work with product-related datasets that include product information and sales countries, making the classification and organization of data a complex task I am involved with.

I believe the best features Palantir Foundry offers are Ontology Manager, Workshop, and the data connection, which combined provide powerful capabilities that should be utilized in all projects. The data connection is seamlessly helpful for connecting with various data sources, while the ontology is a valuable concept from Palantir, where data is created as a layer and utilized by many affiliates. This assists the front-end team or those who build dashboards using Workshop, simplifying the work. Workshop executes designs into dashboards with ease, which I find beneficial.

Palantir Foundry has positively impacted my organization by enabling us to deliver projects quicker, as it organizes data from across the world into a dashboard that can be managed in one single location, accelerating business decisions made by the leadership team.

When I refer to it accelerating business decisions, I am referring to our marketing campaigns, where users take real-time actions in the field, and it is crucial that our data remains accurate. If our data deviates from the standard, it must be addressed immediately as this directly impacts the marketing and sales teams, and I ensure everything is in place so the business runs smoothly.

I do not think Palantir Foundry needs improvement; I feel everything is good as of now and I have no specific comments.

I do not think there are needed improvements, but I feel that since tools are continuously upgraded, conducting training sessions provided by Palantir would enhance our tool usage, even if documentation is provided.

I have been using Palantir Foundry for five years, and I continue to use Palantir Foundry to date.

My advice for others looking into using Palantir Foundry is that they should first get trained with the basics, as it is a powerful tool, and understanding its capabilities provides a strong foundation for effective use. Decision-making trusted by leadership requires accuracy, so it is better to get trained and then practically apply the knowledge. I would rate this product a 10 out of 10.