Foundry

13 pages

Data integration, transforms, datasets, Workshop, Contour, ML pipelines, and all things Foundry. Your complete reference for the Palantir Foundry data platform.

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Topics

Core Concepts1Data Connection & IngestionDatasets & BranchesTransforms1PySpark & PythonSQL TransformsContour AnalyticsWorkshop AppsSlateFoundry MLOSDK in FoundrySecurity & MarkingsDevTools & CLI

Recent Pages

Data Pipeline StructureUPLOAD

Recommended Project and Team Structure for Foundry Data Pipelines

This document outlines the recommended structure for data pipelines in Palantir Foundry, emphasizing the use of Projects for organized permissions, collaboration, and maintainability. It details five key pipeline stages: Data Connection, Datasource Project, Transform Project, Ontology Project, and Workflow Project, each serving a distinct purpose in the data processing lifecycle.

data pipelineproject structurefoundryMar 21, 2026
AI Systems DesignUPLOAD

Overview and Applications of Multi-Agent Systems

This document provides a technical overview of Multi-Agent Systems (MAS), detailing their core components (LLM, Tools, Reasoning Framework), common organizational structures (decentralized, hierarchical, dynamic), and key advantages like flexibility and domain specialization. It also outlines challenges such as coordination complexity and unpredictable behavior, suggesting MAS are best suited for highly complex, multi-domain problems.

multi-agent systemsAI agentsLLMMar 21, 2026
AI/ML IntegrationUPLOAD

Using LLMs for Sentiment Analysis in Pipeline Builder

This document outlines how to perform sentiment analysis using Large Language Models (LLMs) within Palantir Foundry's Pipeline Builder. It details accessing and manipulating the 'Emails' dataset to integrate LLM capabilities for analyzing sentiment directly within the no-code pipeline environment.

Pipeline BuilderLLMsentiment analysisMar 16, 2026
Data TransformationUPLOAD

Use LLM in Pipeline Builder to parse text

This document explains how to leverage Large Language Models (LLMs) within Palantir Foundry's Pipeline Builder. It demonstrates a practical application of LLMs for parsing unstructured text data directly within the data transformation environment.

LLMPipeline Buildertext parsingMar 16, 2026
Financial Audit AnalysisUPLOAD

Analysis of DoD Audit Findings and Root Causes

This document synthesizes audit findings and root causes from recent U.S. Department of Defense (DoD) financial reports. It highlights the DoD's consistent failure to receive a clean audit opinion since 2018, with approximately 28 material weaknesses affecting over $2.1 trillion in assets for FY2024. Key systemic issues include inability to verify asset existence, unsupported accounting adjustments, weak internal controls, incomplete financial data, fragmented IT systems, and failures in property accountability.

DoDauditfinancial reportingMar 13, 2026

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