Course Overview

This three-day, bootcamp-style intensive is designed for technical users looking to master the developer tools within the Palantir ecosystem. The curriculum focuses on the synergy between Code Repositories for sophisticated data engineering and Workshop for building frontline operational applications.

Instruction is led by an expert and supported by guided, step-by-step demonstrations and walkthroughs conducted within a live Palantir and AIP instance. You will follow each guided walkthrough using your own Palantir environment, applying the same technical steps to transition from writing transformation code to deploying a functional, interactive application.

Technical Requirement: Access to a Palantir system is not provided as part of this course. Participants are expected to utilize their own organizational environment to complete all hands-on activities and exercises.

Core Objectives

The primary mission of this program is to bridge the gap between data science/engineering and end-user operations. Key objectives include:

• Advanced Transformation: Mastering SQL and Python within Code Repositories for complex data logic.
• Application Architecture: Designing intuitive user interfaces in Workshop to drive operational workflows.
• Action Logic: Implementing write-back capabilities to allow users to update the Ontology directly from an app.
• UI/UX Excellence: Utilizing widgets, layouts, and variables to create highly responsive user experiences.
• AIP Augmentation: Integrating AI models to provide intelligent recommendations within the application interface.

Comprehensive Training Phase Model

Phase 1: Engineering in Code Repositories

This phase focuses on the “code-first” approach to data transformation and management.

• Environment Setup: Initializing repositories, managing branches, and understanding the Palantir build system.
• Sophisticated Transforms: Writing and testing complex SQL or Python transformations to prepare high-fidelity datasets.
• CI/CD Fundamentals: Managing code reviews, merging changes, and troubleshooting build failures within the repository.
• Unit Testing: Implementing tests to ensure transformation logic remains robust across pipeline updates.

Phase 2: Application Development in Workshop

Phase 2 shifts to the frontend, building the interface where users interact with the data.

• Workshop Fundamentals: Designing layouts and navigating the interface builder to create a “Home” for your operations.
• Widget Configuration: Implementing object tables, property views, and charts to visualize the Ontology.
• Interactivity & Variables: Using variables and events to create dynamic filters and interconnected widget behaviors.
• Forms & Input: Designing structured input forms to capture user data accurately.

Phase 3: Operationalizing Actions and AI

The final phase focuses on making the application interactive and intelligent.

• Implementing Action Logic: Configuring buttons and forms to trigger changes in the Ontology, enabling real-time record updates.
• AIP-Driven Workflows: Embedding AI-supported insights directly into the app interface to assist user decision-making.
• Permissions & Security: Managing application access and ensuring data governance is maintained for end-users.
• Deployment & Iteration: Releasing the application and managing version updates based on user feedback.

Real-World Project Competencies

As a direct result of this three-day intensive, learners will be equipped to architect and deploy end-to-end applications such as:

• Field Service Management Apps: A mobile-friendly interface where technicians can view equipment “Objects,” update maintenance logs, and use AI to troubleshoot issues.
• Live Inventory Optimization Tools: An application that identifies stock shortages via code-based logic and allows managers to trigger “Reorder” Actions with a single click.
• Case Management Workspaces: A unified view for investigators to link evidence, update case statuses, and generate AI-summarized reports.
• Financial Oversight Portals: Dashboards built on complex Python-based reconciliation logic that allow users to flag and resolve transaction anomalies directly in the UI.