Reference repository for the Kyle Chalmers Data & AI YouTube channel. Provides structured templates for managing data tasks with quality-first SQL development, standardized ticket workflows, automated QC validation, and multi-layer architecture patterns. Use as a foundation for reproducible analytics work.
The ticket-driven workflow this repo pioneered now lives as its own tool: Ticketwright, a portable, tool-agnostic version you can install into any repo.
This repository serves two purposes:
-
📺 Video Demonstrations - Contains real examples of data analysis work featured in YouTube videos, showing practical applications of:
- AI-assisted data analysis with Claude Code
- Snowflake data warehouse development
- Quality-first SQL development practices
- Data ticket resolution workflows
-
📋 Template for Your Own Work - Provides a structured framework you can adopt for your own data analysis projects:
- Standardized folder structures
- Quality control patterns
- Documentation templates
- AI assistant instructions (CLAUDE.md)
The videos/ folder contains complete examples from YouTube videos:
- Claude Code Overview - Complete guide to Claude Code for data teams including:
- Installation, setup, and modes
- Compaction and context management
- Custom commands and agents
- Settings and configuration
- Integrating AI and Snowflake - Using Claude Code with Snowflake MCP server for data analysis
- Integrating Claude and Databricks - Databricks CLI workflows including:
- Unity Catalog exploration
- Notebook creation and job scheduling
- Job troubleshooting and error resolution
- Integrating Jira and Ticket Taking - Atlassian integration including:
- Atlassian CLI setup and configuration
- Atlassian MCP server setup
- Ticket workflow automation
- Integrating AWS S3 and Athena - CLI + Claude Code workflow for AWS data lakes
- PRP Data Object Workflow - Context engineering framework for building Snowflake data objects:
- Product Requirements Prompt (PRP) methodology
- AI-assisted data object creation and QC
- Four-phase workflow from definition to production deployment
- Claude Code vs Cursor - Head-to-head comparison building the same Databricks job with both tools:
- AGENTS.md universal standard for AI coding tools
- Context engineering systems compared (CLAUDE.md vs AGENTS.md + .cursorrules)
- When to use Claude Code vs Cursor based on workflow
Core template files you can adapt for your own projects:
CLAUDE.md- Comprehensive AI assistant instructions for data analysis workdocumentation/- Template documentation structures:data_catalog.md- Schema documentation templatedata_business_context.md- Business context documentation templatehelpful_mac_installations.md- CLI tool setup guide
.claude/agents/- Custom Claude Code agents for specialized tasks:code-review-agent.md- SQL, Python, and notebook reviewsql-quality-agent.md- Query optimization and best practicesqc-validator-agent.md- Quality control validationdocs-review-agent.md- Video documentation review, URL validation, and indexing
your-project/
├── README.md # Project overview and documentation
├── CLAUDE.md # AI assistant instructions
├── documentation/ # Technical documentation
│ ├── data_catalog.md # Database schema reference
│ └── data_business_context.md # Business definitions
└── tickets/ # Organized work by ticket/task
└── [team_member]/
└── [TICKET-ID]/
├── README.md # Task documentation
├── source_materials/ # Original requirements
├── final_deliverables/ # Production outputs
│ ├── sql_queries/ # Final SQL scripts
│ └── qc_queries/ # Quality validation
└── exploratory_analysis/ # Development work
- Watch the corresponding YouTube videos for context
- Explore the
videos/folder to see real implementations - Study the quality control patterns and documentation approaches
- Review
CLAUDE.mdto understand AI-assisted workflows
- Fork or clone this repository
- Customize CLAUDE.md with your specific:
- Database architecture
- Business context
- Team workflows
- Tool configurations
- Adapt folder structures to match your needs
- Use as foundation for your data analysis ticket system
This template showcases integration with:
- Snowflake - Cloud data warehouse and SQL development
- Databricks - Unified analytics platform and job orchestration
- Claude Code - AI-assisted coding and analysis
- Snowflake MCP Server - Model Context Protocol for database integration
- Databricks CLI - Workspace management, job scheduling, and troubleshooting
- Git workflows - Version control and collaboration patterns
- Quality control frameworks - Automated validation approaches
Check the Kyle Chalmers Data & AI YouTube channel for videos demonstrating these workflows:
| Video | Description |
|---|---|
| 5 Lessons for Every Data Professional Wondering About AI | Introduction to the repository and AI-assisted data workflows |
| Claude Code + Snowflake: The Productivity Game-Changer | Claude Code + Snowflake workflow demo |
| Claude Code vs Manual Jira Ticket Work | Atlassian CLI and MCP integration guide |
| Claude Code Makes Databricks Easy | Jobs, Notebooks, SQL & Unity Catalog via CLI |
| FUTURE PROOF Your Data Career with this Claude Code Deep Dive | Complete Claude Code guide for data teams |
| UPDATE to settings.json Chapter from FUTURE PROOF Deep Dive | Settings update supplement to the Claude Code Deep Dive |
| Stop Waiting: Use AI to Build Better Data Infrastructure | PRP context engineering framework for Snowflake data objects |
| The Data Skills AI Can't Replace (And the Ones It Already Has) | Analysis of data skills in the age of AI |
| Skip S3 and Athena in the AWS Console | CLI + Claude Code workflow for AWS data lakes |
| I Let Claude Code Handle Our Data Team's Workflow | End-to-end data team workflow automation with Claude Code |
| I Tested Claude Code vs Cursor for Building Databricks Jobs | Head-to-head comparison with context engineering focus |
| Claude Code Storage Bug? Set This Up Once and Never Worry Again | Solving Claude Code's storage and context persistence |
| I Prompted Claude to Build My YouTube Analytics BigQuery Pipeline | Building an automated YouTube analytics pipeline from a single prompt |
| Claude Code Built This Azure Pipeline in Minutes | AI-powered Azure data pipeline development |
| 5 Small Coding Agent Tips That Make a Big Difference | Small habits and setup steps that make Claude Code sessions go smoother |
| Semantic Layers: The Skill Data Professionals Need Next | Bundled vs standalone semantic layers with Snowflake and dbt MetricFlow demos |
- QC validation as core requirement, not afterthought
- Automated quality checks in dedicated folders
- Clear documentation of assumptions and business logic
- Standardized folder organization for reproducibility
- Numbered files for logical review progression
- Comprehensive documentation templates
- Detailed AI assistant instructions in CLAUDE.md
- Integration patterns with data tools and CLIs
- Automated quality validation approaches
This is a personal reference repository for YouTube content. However, if you:
- Find issues with the templates
- Have suggestions for improvements
- Want to share how you've adapted it
Feel free to open an issue or reach out!
This template is provided as-is for educational and reference purposes. Adapt freely for your own data analysis work.
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