I use Palantir Foundry for my primary use case, which involves building and maintaining end-to-end pipelines and operational data products at UHG for our healthcare analytics team. I work on data ingestion and integration from multiple healthcare source systems into Foundry, and I design ELT/ETL pipelines using Code Repositories. My day-to-day work involves data transformation and optimization using Python, PySpark, and SQL. I spend most of my time developing scalable data workflows, automating our data processing, and collaborating with cross-functional stakeholders to deliver reliable healthcare data solutions within the Foundry ecosystem.

A specific project example I built with Palantir Foundry involved building a healthcare claims and member analytics pipeline at UHG. The goal of the project was to consolidate claims, eligibility, provider, and member data coming from multiple upstream systems into a unified operational data model that the business and analytics team used for reporting and care management insights. My work involved designing ingestion pipelines for large-scale healthcare datasets from APIs, SFTP sources, and relational databases. I developed PySpark-based transformation workflows in Foundry Code Repositories and created reusable datasets and modular pipeline components for downstream teams. I also implemented data quality validations and optimized pipeline performance. The overall solution helped business stakeholders get more reliable and near real-time visibility into healthcare operations and reporting metrics.

The main use case for that project was to help business stakeholders and analytics teams use this work for reporting and care management insights. One specific improvement I implemented was optimizing a transformation workflow that was processing millions of claims records daily. By redesigning partitioning logic and reducing unnecessary joins, I significantly improved pipeline execution time and reduced resource consumption.

My main use case for Palantir Foundry was that it served as our go-to platform for business analytics and AI workloads. We mostly used it for analytics and as an operational platform, specifically using Palantir Foundry's Works feature.

We used Palantir Foundry for operational needs and analytics, and there was a team using Palantir Foundry's built reports and dashboards to input data and then execute an approval process using Palantir Foundry's services. The reports were created on the platform following this workflow.

Our team used Palantir Foundry extensively in conjunction with Azure-based services such as Databricks, Azure Data Lake storage, and Azure Data Factory. There was excellent integration between these two services, which really helped automate many of the manual tasks that the downstream teams and business teams were handling.

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.

I have been using Palantir Foundry for close to four years, just two months shy of four years.

My main use case for Palantir Foundry is primarily for data engineering purposes to handle big data. I started working in the pharma domain, but for the past year, I have been working in the auto components domain. Along with data engineering tasks, I am now handling some front-end applications as well. Earlier I was building use cases, but now I am building workflows as well using the Workshop application.

A specific example of a workflow I have built recently with Palantir Foundry is an audit platform. As I work for an auto component Tier 1 supplier company, they have numerous bills from many different suppliers for many different plants which need to be audited based on the agreed upon rates set in the contracts. The Workshop application takes data from users, which can be auditors or plant representatives. In five steps, we obtain approvals from different teams. We upload data using the front end of the Workshop application, accepting bills in the form of Excel and PDF, extracting data from them, and running rules to check if specified categories of bills are duplicated based on rules segregated by different countries. There are set rules for duplicate checks, and we also conduct audits by comparing with rates. If there is a permissible difference and values fall within the acceptable range, that is acceptable, but if values fall outside that range, there are expenses which are not expected in that category. There are rules to check for audits. The workflow then goes for approval at the plant stage where plant representatives verify the information, followed by final approval. We then generate a PDF showing that the bills have been reviewed by particular reviewers at particular times, and indicating whether the bill is acceptable to pay or must be rejected with rejection reasons. Notifications are sent to respective people for respective stages. The main task behind this is to replace the specific application or subscription we were using for that function with Palantir Foundry so we can integrate this system with other projects and obtain insights based on how many bills we identify as faulty or how many bills we process in a certain amount of time, allowing us to generate reports when we collect the data ourselves and maintain it on our side.

Regarding my use cases with Palantir Foundry, while not unique, we need to send emails to not only Palantir Foundry users but also users outside the organization. We have set up service user agents with PySpark code that gathers all the data and sends external and internal emails.

I implemented end-to-end data pipelines on Palantir Foundry to extract data from different sources and create datasets as objects to build a workshop.

We have worked for many organizations, including Airbus and British Petroleum, where we used Palantir Foundry as the main platform to provide solutions to our clients.

Palantir Foundry helped in reducing the time for analyzing simple use cases. For example, for our media and entertainment clients, they wanted to know which particular show was getting popular or was at the top that week. The client was taking too much time to determine which show was getting to the top and which age group was watching those shows for longer periods. The data was scattered across different sources, and they were taking one week to analyze these things. When we used Palantir Foundry as a source, we used different data connections to collect all that data and bring it into Palantir Foundry. Then we performed transformations and joins on that data to make this possible. This reduced the time from one week to four hours.

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.

There are several use cases that we are working on with Palantir Foundry. The first thing is for data model creation for all our data engineering pipelines. That is one use case. Palantir Foundry also has an ontology, more of a semantic layer, so that we can directly hand over the data model to the end users. That is another use case that we have, creating the semantic layer ontology. Recently, we have started working on some AI use cases as well. Palantir Foundry has very good wrappers such as AIP Agent Studio and AIP Logic, where you can choose any model and build your own chatbot or any AI function or generative AI function. These are a few use cases we are working on.

I work with different types of data in Palantir Foundry, including structured and unstructured data. We process PDFs and Word documents, but I have not worked on any use case with video and audio, although there are a few teams in our company that actually process video and audio as well. When it comes to textual information, I have worked on several use cases, and Palantir Foundry has made it very simple. There are some built-in functions, and you can also use Python libraries if you want. Additionally, there are no-code tools to parse unstructured information.

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.

My main use case for Palantir Foundry is creating data engineering pipelines including ingestion of data, transformation of data to the final dashboarding tools used in Palantir Foundry.

A specific example of a project I have handled using Palantir Foundry involves working for a pharma-based company that had data coming in from different vendors. The data was located across different data sources including GCS, BigQuery, Redshift, and Snowflake. We ingested all of them into Palantir Foundry and built three separate layers of transformation: bronze, silver, and gold for cleanup of data, aggregation of data, and post-processing. At the end, we used the final data sets which we thought were ontology-worthy to create ontology objects on top of them and then used them in the dashboard building steps. Those dashboards were used by high-stakeholders to make very critical decisions.

My main use case for Palantir Foundry is from the data engineering perspective.

A specific example of how I use Palantir Foundry for data engineering involves raw data stored in Redshift AWS, which we are using those tables in the form of a dataset in Foundry. We are ingesting that data into Foundry and using it for cleaning purposes. After cleaning the data, we create Ontology objects and use them for operational applications in the Workshop.

One of the use cases that I found with Palantir Foundry is when I worked on the supplier scorecard, which is dedicated to understanding supplier reviews based on the goods supplied. The company assigns ratings to their products through a supplier scorecard, providing scores to their suppliers. We used multiple datasets and created objects, adding our own logic in the Code Repository to check supplier goods by percentage and count, generating aggregated values in the Workshop app. Based on these parameters, business management can make decisions and take actions to update the supplier's score.

My main use case for Palantir Foundry is to modernize the data infrastructure. One of the modernization projects I have worked on involved getting all the telemetry data collected from IoT devices that had been sitting in the field and then streaming it to Foundry while using the AIP capabilities to perform predictive maintenance and forecast performance degradation of the metrics. This allows the AIP agents to send out remote fixes to address the actual issues.

Palantir Foundry helps with predictive maintenance and forecasting performance degradation by providing a layer of abstractions so that I do not have to worry about piecing together all the different frameworks. Rather, everything is integrated beneath Foundry and the AIP. I can focus on the data part, integration, and data integrity, which means I worry less about modeling and optimization.

In my recent project work, I have been extending all the AIP agents to derivatively send remote fixes. Rather than keeping autonomous operations confined within the platform, the agents can now interact with the real world to fix issues or conduct extended analysis so that the issue can be briefed in the ontology.

I have used Palantir Foundry in multiple cases, such as creating data pipelines and ingesting data into Palantir Foundry from various data sources, including structured, unstructured, and semi-structured data. After ingesting into Palantir Foundry, I have cleaned the data using PySpark and code repositories. I have written Python and PySpark scripts that clean the data using various transformations, schema changes such as converting boolean and string fields to boolean, data type changes, dropping unwanted data, and filtering the data. I am building end-to-end pipelines in which I have joined and integrated multiple data sets. I have also applied various health checks, scheduled jobs using Cron expressions, and created ontologies, including actions such as create, edit, and delete. On top of ontologies, I have created Workshop applications for the UI perspective and multiple kinds of UI applications. I have also worked on the Slate part. Regarding industries, I have worked in the aerospace industry, healthcare, and the gas and energy sector, with major clients in three industries.

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

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.

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.

My main use case for Palantir Foundry involves building data pipelines, creating workshop apps, and constructing Gaia maps.

Another example of my main use case with Palantir Foundry is obtaining different data sources and combining them so that they can be visualized either in a workshop app or a Gaia map.

I have been using Palantir Foundry for about two to two and a half years. My main use case for Palantir Foundry is data analytics.

I get asked to do a particular project for data analytics. I research Palantir Foundry for the datasets that I am looking for. Sometimes I create datasets from other datasets, and then I either export the file or create a report on Palantir Foundry.

I think you have to exercise using Palantir Foundry to better understand how it works. However, there are tutorials and AI assistance with Palantir Foundry, which makes things easier.

My main use case for Palantir Foundry is primarily focused on time series related information, visualization, and a few applications where the background involves AI.

I receive different sensor data through time series and apply business logic on top of that. With the aggregated data, I perform visualization according to business requirements. The logics and everything are implemented both as core native logic within Palantir Foundry itself.

This is the most common use case I work through. Apart from that, there are a couple of additional projects involving workflows where resource management needs to be handled. This includes resourcing schedules as well as job allocation.

Palantir Foundry is deployed in my organization as a public cloud only.

My main use case for Palantir Foundry is for decision intelligence. It functions as an operational operating system that connects fragmented data into a single ontology for our insurance provider, utilizing principal and component analysis.

An example of how I use Palantir Foundry for decision inclusions or connecting fragmented data is through the core mechanism of data connections to building Pipeline Builder, then using ontology chain. Data connection ingests data from different disparate sources such as Zabbix, health signals from Zabbix, and PostgreSQL external APIs. Now living in separate systems, Foundry pulls this into a unified raw dataset layer without needing custom ETL glue code. I transform those raw inputs into clean, joined datasets, and the ontology is where fragmentation is truly resolved. Instead of querying disparate tables, every entity, device, and incident becomes a pipeline run that functions as an object type with properties and links to related objects. A network object, for example, links to its Zabbix alert objects, its health metrics time series, and its owner, all the exact type of multi-source join that I can rebuild, rather than writing SQL joins every time. The ontology makes these relationships first-class.

In our infrastructure data spread across Zabbix, PostgreSQL, and Zyco, the classic problem is that each system has its own ID scheme, update cadence, and schema. Palantir Foundry's ontology solves this by creating a canonical object model on top. I do not migrate or replace the source systems; I overlay a unified semantic layer. Every downstream consumer reads from that layer and not from the raw source directly.

My main use case for Palantir Foundry is working for a client, Airbus, where I work on the datasets. Currently, we are working on multiple services such as Code Repo, Slate, and Workshop.

We have Contour, where we analyze the data and find the anomalies. We look into the graphs, plot graphs, and investigate how the data is behaving. If the data is not up to date, we investigate that on Contour. In the data lineage part, we backtrack to the point of failure where the failure point is occurring.

I have worked on a project called CMA, wherein we have a Slate application. The dataset is a writeback dataset wherein we get the data as a writeback phonograph sync, and we get the output from the users, process the data, transform the data, and export the data to FTS+.

I have used Palantir Foundry for three years.

My main use case for Palantir Foundry involves data engineering-related work, specifically ingesting data from multiple sources, cleaning, transforming, and loading it into ontology.

For multiple projects I have worked on, the process with Palantir Foundry follows the same basic flow: getting the data from multiple sources such as an API or AWS S3, different databases, and file systems. We ingest the data from those sources, then perform cleanup on the data, and then load it into ontology from which users will be using the data.

For day-to-day operations, this was a data migration project for an ERP rollout implementation where we automated the data pipelines for the migration.

We mainly used the low-code solution that they had for the data engineering pipeline, which is a pipeline builder that helped us ramp up the project timelines rapidly. It had automated refreshes from both the source and the target, and everything was automated once it was built. It had everything in place from analytics, data, and solution to BI solution. The low-code solutions made our lives easier because not everybody is too technical to get started and the barrier to entry is very low. In that sense, we had a lot of pre-built solutions offered which helped us ease out our timelines and bring non-technical stakeholders into the work to get things done.

Even the integration was mostly pre-built. All we had to do was use point and click solutions. It was quite smooth to begin with and we were able to do a lot of use cases, including building a front-end application, BI application, and troubleshooting. Tracking the lineage of the data is also very robust.

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.

My main use case for Palantir Foundry is to build complete end-to-end workflows. I have worked on different POCs, a couple of POC projects, and some company projects with various scenarios. I have completed all the certifications, all the trainings, and most of the examples I have built on Palantir Foundry. I dive deep into Palantir Foundry and its architectural design to learn more, so I have covered most of its applications and I understand it well.

I can tell you about my personal project because the company project I cannot share with you. I have built my own finance OS where I manage all my finance transactions, my income, my expenses, and my budgets. I set my budgets and track all these things using Pipeline Builder, Ontology Manager, AIP Logic, Code Repo, Workshop modules, Automations, workflow diagram, and workflow lineage.

Using Palantir Foundry has helped me manage and build my finance OS because it is far easier. I got my records using AI agents and gained deep insights into my expense habits, my category-based expenses, and I also have a dashboard where I can see different graphs that show my expense and income records.

I would like to add that my main use case with the expense management is significant to my overall workflow.

My main use case for Palantir Foundry involves end-to-end solutions, starting from data ingestion and transformation. I also use AIP logics for data transformation and ontology management.

In my day-to-day work, I create or modify back-end functions using TypeScript to create action types under the ontologies to add new rows, which means adding new objects or updating existing ones. I also use AIP logics to accomplish the same tasks.

While using AIP logics, I work with machine learning LLM prompts to get results based on text from various unstructured files, such as PDFs. I use the LLM to extract data for analysis in workshops for Contour.

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.

I used Palantir Foundry in a different role prior to my current role; however, I do not use it in my current position.

In my previous role, my main use case for Palantir Foundry was data extraction, ingestion, and cleansing for use with machine learning models. A specific use case involved extracting data from multiple public health sources of patients and providing healthcare providers with legal data and logistical data about healthcare providers to analyze which healthcare providers were guilty of fraudulent billing.

Other use cases included ingesting veteran data for different kinds of machine learning using clinical data for veterans; however, the primary use case most recently was fraud detection for prevention of fraud, waste, and abuse in federal health agencies using data from NPI, National Provider Identification, for various providers along with state licensure data for fraud detection.

My main use case for Palantir Foundry is that I am learning it through all the resources at learn.palantir.com, and I currently have one certificate while working towards two more certificates. Additionally, I am trying to create the ontology of the ticket management for the vertical of my company.

I am using Palantir Foundry to assign the best or the perfect developer for every ticket. When a client creates a ticket, there are many developers who could work on it, but some developers perform better with certain types of tickets, know the client, or have more available time. Currently, this decision is a human one, but I am creating an ontology in Foundry to enable an AI agent to propose the best choice.

I am very excited to create a more comprehensive ontology for my company.

My main use case for Palantir Foundry is using AI to connect the shipyard and build AI agents that improve the operational throughput.

A specific example of how I'm using Palantir Foundry to connect the shipyard and improve operations is that the entire asset ecosystem in Drydocks has been revamped by using Palantir Foundry. I have loaded all the IoT data from those assets into Palantir Foundry and am using it to track all the live updates from all the assets to ensure there are no outages and no shortage of materials to maintain these assets.

Regarding my main use case, I am using engineering drawings, which usually require a lot of human intervention and human study to build items when requirements come from clients. I am using state-of-the-art AI models in Palantir Foundry to read these engineering drawings and provide much quicker analysis and insights to the engineers before they review the drawing themselves.

My main use case for Palantir Foundry is document processing, taking large volumes of documents, scanning and parsing them, and using AIP to generate insights and applications based on that work.

I continue to expand the functionality and leverage ontology to provide even more accurate and business-friendly metrics.

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.

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.

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.

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.

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

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.

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.

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 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.

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.

In my job, I use Palantir Foundry exclusively to create multiple dashboards. For example, I use Palantir Foundry to create a dashboard corresponding to the visualization of many charts by extracting the dataset, which is Skywise, putting this dataset in ontology, and using the different tools in Palantir Foundry. This is my typical use case in my job.

My last dashboard created with Palantir Foundry is regarding the Project Speed Project Dashboard, which helps analyze more programs because this dataset comes from Skywise, where my principal customer is Airbus. This project clarifies all the X-tracker, enabling tracking of multiple defects in programs such as the A320, and visualizing all action plans for non-quality across multiple programs. This is my first job for the dashboard speed, where I also plan to add, modify, and delete actions we want to track including all performance analysis for the high to left performance.

In the context of that use case, we have to show information about the customers in a dashboard, and for that we have the user datasets, some other information that comes to us in raw form, we have to clean it, and we show all of that using Palantir Workshop in an application that the other users access.

One of the leading European manufacturing plants uses Palantir Foundry for manufacturing interior parts of various car brands such as Honda, Hyundai, Ford, Mercedes-Benz, and BMW. This involves highly secured information that is not supposed to be shared with any competitors.

I am getting into the ontology space using Palantir Foundry. The primary use case is for developing a common business model that includes data, people, and processes, essentially describing how businesses operate. We are applying this model in the utilities sector.

Our use cases are mostly related to data analytics. We are building some dashboards and ETL pipelines on the Palantir side. Palantir Foundry is a low-code/no-code platform with a user-friendly UI. It is better than Databricks, where you need to code. Palantir Foundry has better data lineage. However, Databricks also provides many features with Databricks Unity Catalog.

We mainly used the solution to ingest our internal data. 

We have a client which uses it for insurance. The insurance company needed to calculate the premiums. In order to do that, we basically get our data from many regions, maybe New Zealand or Australia, Asia, and India. We focus on those regions. 

From those regions, get data in the form of Excel sheets or CSV files. We ingest those formats into the Palantir Foundry platform. And after ingesting, our data scientists upload the stream. Basically, they are trying to calculate the premiums on the basis of the data funneled over to them.

I didn't use Foundry, but I went through some training, and my team became certified in it. When I left the company, there were probably 100 research projects that had been added to it. I did it project by project. Around 30 were completed. About 40 or 50 were in progress, while there were 20 more in the queue. You could reuse data and leverage data that had been imported. We imported lots of Epic data.

You needed permission to see the Epic data.  Someone with a research project approved by the institution could ask permission to join it with other data. In a relational world, you could say, "I'll give you database permissions, but I'll need to mask these columns that are based on those." It's similar to an SQL database.

People submitted their project requests to a project review committee. The capacity was limited because people needed to understand the platform, but I'm sure they have trained more people on it since then.

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.

I use Palantir to build pipelines and ingest data from external sources like data lakes and bring that data from the data lake to the Palantir environment. Then, I use it to process data using some languages like Spark or SQL. The solution uses Spark for data processing. I can use Spark in the Palantir environment and create reports based on it.

Our primary use case is for data engineering and some data analysis, bringing in data from several sources and using data wrangling and data managing to support the reporting tools we have. We use the reporting apps for some of our basic reporting. We are customers of Palantir. 

We use Palantir Foundry for data engineering and self-service tools. Palantir is a great service tool for business users who don't have the necessary IT skills. It helps them to easily draw up their own models and use cases with data by simply using Palantir's drag and drop tool. 

It's a great tool for us to say, "Here's your data. You can play around it, build models with it, aggregate tables, and check everything on your own." It's a self-service tool.

It's deployed on cloud. The cloud provider is AWS.

Over 300 people are using this solution in my organization. It's used on a daily basis.

Palantir Foundry builds full applications for us. We can build an end-to-end application from data ingestion and data pipelining, to application building.

Early on, we built an internal application that ingested data from our ERP and BI environment that is used by around 700 users. 

Our company uses the solution to elaborate off PySpark. We run PySpark scripts for our products but use the solution to leverage the workflows that automatically run time bound and trigger bound. When a data trigger is there, the solution executes and the script starts running. 

Our scripts were long so they ran for two to three hours because they were generating some kind of commentary. The solution takes raw data as input, processes it, and converts it into a fine commentary for products.

More than 100 staff use the solution. 

We used it for a utility company for preventative maintenance of physical utility assets such as poles, electrical wires and transformers.

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.

This is a data integration tool with multiple components that link to multiple sources to create repositories, transform data and make it available for dashboards or management purposes. We're based in the UAE and I'm a senior manager, customer and user of this solution. 

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. 

This solution is used more for the analytics available on the platform.

The main use was for a COVID-19 White House initiative that was handled by the Vice President, Michael Pence.

We use this solution for everything, including sales. One of our use cases is performing machine learning to gives us an understanding of customer behavior, and which message should be used to target different customers.