Mimir, Keeper of the Well of Wisdom
Palantir Foundry Quick Start: Connect, Transform and Build
https://www.udemy.com/course/palantir-foundry-for-beginners-
training/
Year : 2026
Language : English
Level : Beginner Level
Category : Development
Subcategory : Software Engineering
Duration : 3h 2m
Lectures : 52
Rating : 4.3/5 (40 reviews)
Students : 343
INSTRUCTOR(S)
HEADLINE
Master Palantir Foundry by Building Data Pipelines, Analytics
and AI Applications
WHAT YOU'LL LEARN
* Understand what Palantir Foundry is, the problems it solves,
and its core components.
* Develop code-based data transformations with Python and
PySpark using Code Repositories.
* Create a free AIP Developer account and confidently navigate
the Foundry interface.
* Understand batch and incremental processing and design
appropriate data-loading strategies.
* Clean, transform, union, and join datasets using Pipeline
Builder.
REQUIREMENTS
* No previous Palantir Foundry experience is required.
* Basic understanding of data, tables, rows, and columns is
helpful.
* Basic SQL or data-engineering knowledge is beneficial but
not
mandatory.
* A computer with a reliable internet connection and modern
web
browser is required.
* Learners should create a free Palantir AIP Developer account
by following the course instructions.
* A willingness to follow practical demonstrations and explore
Foundry is all you need.
WHO IS THIS COURSE FOR
* Complete beginners who want to learn Palantir Foundry from
scratch.
* Data engineers and aspiring data engineers.
* ETL developers familiar with tools such as Azure Data
Factory
or Alteryx.
* SQL, Python, PySpark, cloud, and data-platform learners.
* Professionals who want to understand data connections,
pipelines, ontology, and operational applications.
DESCRIPTION
Learn Palantir Foundry through hands-on data engineering,
analytics, Ontology, and AI projects. This practical course
introduces you to Palantir Foundry and shows how its different
capabilities work together to solve real-world enterprise data
and AI challenges. Instead of focusing heavily on theory, you
will work directly with Foundry to ingest data, build
pipelines,
transform and integrate datasets, model business data with
Ontology, create analytics, and build AI-powered applications
with Palantir AIP. What You'll Learn By the end of this
course,
you will be able to: Understand what Palantir Foundry is, the
problems it solves, and how it differs from traditional data
engineering platforms Import and integrate data from files,
APIs, Amazon S3, PostgreSQL, and other data sources Build end-
to-end data pipelines using Pipeline Builder and visual
transformations Develop code-based transformations using
Python
and PySpark with Code Repositories Understand batch vs.
incremental processing and design efficient data-loading
strategies Work with security and access control for
enterprise
datasets Build business-oriented data models using Foundry
Ontology, objects, properties, and relationships Create
analytics, dashboards, and visualizations with Contour
Understand how Workshop supports operational applications
Build
practical AI solutions using Palantir AIP, AIP Logic, LLMs,
and
Ontology Evaluate AI workflows, compare LLM results, and
validate applications with new data Understand when to choose
visual transformations vs. Python vs. PySpark Hands-On
Learning
This is not just an introduction to Foundry features. You will
work through practical demonstrations, real-world scenarios,
and
role-play activities designed to help you think like a Data
Engineer working with Foundry in an enterprise environment.
The
course concludes with an end-to-end Agentic AIP project where
you move from business requirements and enterprise data to
Ontology, AIP Logic, LLM configuration, evaluation,
automation,
and validation with live data. Who Is This Course For? This
course is ideal for Data Engineers, Data Analysts, Analytics
Engineers, Python/PySpark developers, Cloud Engineers, AI
Engineers, and technical professionals who want practical
experience with the Palantir Foundry ecosystem. No previous
Palantir Foundry experience is required. Basic familiarity
with
data engineering concepts will be helpful. What Makes This
Course Different? The goal is not simply to show you where
different buttons are located in Foundry. You will learn how
the
major pieces fit together: Data ? Pipelines ? Code ? Ontology
?
Analytics & Applications ? AIP By the end, you should have a
clear understanding of how Palantir Foundry can be used to
build
integrated enterprise data, analytics, and AI solutions. This
course has been designed and developed based on our
experience,
research, and practical understanding of Palantir Foundry. We
have made every reasonable effort to ensure that the course
content is accurate and useful; however, no educational
material
can be guaranteed to be completely error-free. Palantir
Foundry
is an evolving platform, and features, interfaces,
terminology,
capabilities, and workflows may change over time. Learners are
encouraged to refer to the official Palantir documentation for
the latest information and to verify details before applying
them in production or enterprise environments. This course is
intended for educational and learning purposes. Examples,
datasets, scenarios, configurations, and demonstrations used
throughout the course are designed primarily to explain
concepts
and may need to be adapted for real-world environments. AI
tools
were used as part of the course creation process to assist
with
activities such as research, brainstorming, content
refinement,
examples, and supporting educational materials. The course
structure, demonstrations, explanations, and final content
were
developed and reviewed by the course creators based on their
experience and learning objectives. This course is an
independent educational resource and is not an official
Palantir
training program. Unless explicitly stated otherwise, any
product names, trademarks, and platform references belong to
their respective owners. For production implementations,
security configurations, architectural decisions, and
platform-
specific best practices, always consult the latest official
documentation and your organization's requirements and
policies.
COURSE CONTENT
Chapter 1: Introduction
1. Introduction
Chapter 2: Introduction to Palantir Foundry
2. What Is Palantir Foundry
3. What Problems Does Palantir Foundry Solve?
4. Explore the Core Components of Palantir Foundry
5. Create Your Palantir Foundry Free Account
Chapter 3: Data Ingestion & Integration
6. Importing Data (CSV, JSON, APIs) & Data Connections
7. Data Sync Concepts & Connection Configurations
8. Data Ingestion Best Practices for High-Volume Datasets
Chapter 4: Data Pipelines & Core Transformations
9. Introduction to Pipeline Builder & Visual Transformations
10. Create a Project for Your Data Pipeline
11. Create Your First Pipeline in Pipeline Builder
12. Create Derived Columns with Visual Transformations
13. Filter Data in Pipeline Builder
14. Fix Data Type Issues with Cast
15. Combine Datasets Using Union
16. Join Datasets in Pipeline Builder
17. Clean and Standardize Data ? Part 1
18. Clean and Standardize Data ? Part 2
19. Explore Additional Pipeline Builder Transformations
20. Build, Deploy, and Run Your Pipeline
21. Build Data Transformations with Python Using Code
Repositories
22. Build Scalable Data Transformations with PySpark Using
Code Repositories
Chapter 5: External Data Connections
23. Connect Foundry to Amazon S3
24. Connect Foundry to a PostgreSQL Database
25. Incremental vs. Batch Processing & Dataset Optimization
26. Dataset Security & Access Control
Chapter 6: Object Modeling & Operational Applications
27. What Is the Foundry Ontology?
28. Create Your First Object Type Manually
29. Create an Object Type from an Existing Dataset
30. Explore the Ontology Manager Interface
Chapter 7: Building Practical AI Applications with AIP
31. Set Up Source Data and Transformations for the
Application
32. Apply Additional Transformations to Derive New Columns
33. Set Up the Ontology for Our AI Application
34. Configure Inputs in AIP Chatbot Studio
35. Publish Your AIP Application
36. Fix Common Issues in the AIP Application
Chapter 8: Building Practical AI Applications with AIP
37. Palantir AIP Architecture & Enterprise LLM Integration
38. Create Your First Dashboard and Visualization with
Contour
39. Explore the Contour Interface
40. Transform and Prepare Data for Your Dashboard
Chapter 9: End-to-End Project
41. Project Introduction: Build an End-to-End Agentic AIP
Workflow
42. Understand the Use Case and Business Requirements
43. Install the Dataset and Workshop Application
44. Explore and Manually Review the Data
45. Create Your First AIP Logic Application
46. Configure AIP Logic and Select an LLM
47. Run and Preview the AIP Logic Workflow
48. Evaluate Your AIP Logic Application
49. Evaluate with Different LLM Models
50. Publish and Automate the Application
51. Validate the Application Using Live Data
52. Project Summary
DATES
Published : 2026-09-03
Last Updated : 2026-09-03
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