Course Syllabus

1. Module 1: Introduction to Palantir Foundry (Beginner) – Overview of the Foundry platform, architecture and core components. Participants learn the Foundry UI, workspace navigation and the concept of the Foundry Ontology (semantic data model). Topics include platform interfaces (Code Workbook, Object Explorer), data concepts, and sample data exploration. Lab: Load a sample dataset and create a simple Workbook for basic data queries and charts.
2. Module 2: Data Integration & Ingestion (Beginner/Intermediate) – Connecting Foundry to data sources and ingesting data. Covers linking databases, filesystems or APIs, and ingesting structured and unstructured data (CSV, JSON, images). Demonstrations show how to create Datasets, define schemas, and use Foundry’s data connectors. Lab: Ingest a sample dataset from a web API or database into Foundry and validate the dataset.
3. Module 3: Basic Workbooks & Visualization (Analyst Track) – (For business users/analysts) Introduction to Foundry’s Code Workbook and simple visualizations. Participants create query-based workbooks using drag-and-drop or formula editors, build charts/dashboards, and share insights with stakeholders. Lab: Build a Workbook that computes summary metrics (using SQL or Python) and present them in a dashboard-style report.
4. Module 4: Data Transformation and Pipelines (Intermediate/Developer) – Building data pipelines to process and transform data at scale. Covers writing transformation logic using PySpark or SQL in Foundry, creating Code Workbooks, and chaining transformations into pipelines. Best practices for scalable processing (partitioning, optimizing joins) are discussed. Lab: Create a multi-step pipeline that cleans and aggregates a large dataset (e.g. event logs) and inspect data lineage.
5. Module 5: Ontology & Data Modeling (Intermediate) – Working with Foundry’s ontology for semantic data modeling. Participants learn to define object types (entities, events) and link data fields to business concepts. This module emphasizes modeling the “business meaning” of data to enable consistent analysis across teams. Lab: Design an ontology schema (e.g. customers, transactions) and map existing datasets to that model, illustrating relationships.
6. Module 6: Collaboration & Code Repositories (Intermediate) – Team-based development workflows in Foundry. Topics include using Foundry’s code repository for version control (Git-based), branching strategy, code review, and collaboration on workbooks and pipelines. Covers best practices for team projects and co-editing. Lab: Simulate a team environment: clone a Foundry project repo, implement a feature, and submit a pull request for review.
7. Module 7: Visualization & Reporting (Intermediate/Analyst) – Advanced reporting features and BI integrations. Users learn to create interactive dashboards (with charts, maps, graphs) and share them. Integration with external BI tools (Tableau, PowerBI) and embedding Foundry reports into presentations are covered. Lab: Develop a dashboard combining multiple charts from transformed data, and simulate sharing it with a decision-maker.
8. Module 8: Automation & Workflow Orchestration (Advanced/Developer) – Automating Foundry data workflows. This includes event-driven pipelines, scheduling jobs, and managing dependencies. Participants learn to use triggers, alerts, and monitoring tools to make pipelines self-running. Lab: Set up a scheduled pipeline that ingests daily data and triggers a notification on failure.
9. Module 9: Data Governance & Security (Advanced/Admin) – Role-based access control, lineage and governance in Foundry. Topics include user/project permissions, data access policies, audit logging, and encryption. Emphasis on compliance features (e.g. tracking data lineage for audits). Lab: Define user roles and permissions for a project, and demonstrate how lineage tracking shows data provenance.
10. Module 10: Capstone Project (All Levels) – A comprehensive, real-world use case combining previous modules. Teams develop an end-to-end solution (e.g. an analytics pipeline from data ingestion to dashboard) under instructor guidance. This project reinforces concepts like ontology modeling, pipeline orchestration, and reporting. Outcome: A live Foundry application or report that solves a business problem.
11. Module 11: Foundry Certification Preparation (Optional) – (If applicable) Guidance on Palantir Foundry certification. Review of key topics, sample exam questions, and study resources. This session helps consolidate learning and highlight areas to focus for any official Foundry certification exam.