Foundry

16 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 Concepts3Data Connection & IngestionDatasets & BranchesTransforms1PySpark & PythonSQL TransformsContour AnalyticsWorkshop AppsSlateFoundry MLOSDK in FoundrySecurity & MarkingsDevTools & CLI

Recent Pages

Core ConceptsNOTE

Saving and Loading Scenario

In this video, "Saving and Loading Scenarios" by Ontologize, former Palantir engineers demonstrate how to persist ephemeral Workshop scenarios by backing them with custom Ontology object types and wiring load/save action mechanics in Palantir Workshop [00:00]. --- Architectura...

Aug 1, 2026
Palantir Pilot AI Application GenerationNOTE

From Prompt to Deployed App: Palantir Foundry Pilot

In this video, "From Prompt to Deployed App: Palantir Foundry Pilot" by Ontologize, former Palantir engineers showcase how to use Palantir Pilot—Foundry's AI application builder—to turn a natural language prompt into a full-stack, production-grade web application built on the...

Palantir FoundryPalantir PilotAI Application BuilderAug 1, 2026
Core ConceptsNOTE

Data Connection in 10min

In this video, "Data Connection in 10min" by Ontologize, former Palantir engineers break down how data enters and exits Palantir Foundry [00:00]. Below is an end-to-end operational guide and use-case analysis based on the concepts, architecture, and realistic examples (like th...

Aug 1, 2026
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

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