Data Architectures in Athlete Management Systems (AMS)

In this video, I go deep into the technical guts of data architecture for athlete management systems — breaking down what data warehouses, data lakes, and data lakehouses are, and why it all matters for elite sport performance environments.

🎯 Whether you're a sport scientist, data engineer, or just a performance nerd with a love for SQL, this session walks you through not just the theory — but how architecture choices impact the way performance data is stored, queried, visualized, and turned into insight.

🔍 What’s Covered in This Video:
✅ Data Lakes vs. Warehouses vs. Lakehouses
— Definitions, core differences, strengths, and tradeoffs
— Schema-on-read vs. schema-on-write
— Programming patterns and tooling (SQL, Python, Apache Spark, Delta Lake, etc.)

✅ Real-World Application to Athlete Management Systems (AMS):
— How AMS platforms like Smartabase (Teamworks), Kitman Labs, Edge10, and Apollo structure and process athlete data
— What architecture they likely use (data lake, warehouse, or lakehouse)

✅ Commercial Platforms vs. Custom Builds:
— When it makes sense to buy vs. build
— Cost, scalability, flexibility, security, and support considerations
— Tech stacks, system integration, and maintaining infrastructure

✅ Tech Deep Dive for Data Pros:
— SQL-based modeling vs. raw ingestion pipelines
— ACID compliance in athletic data contexts
— Managing performance, health, GPS, wellness, and biometric data across different storage layers

🧠 Who Is This For?
— Sport scientists and high-performance staff building their own data pipelines
— Data engineers working in elite sport or health analytics
— Directors of performance evaluating AMS vendors
— Anyone fluent in data architecture, SQL, or sports technology

📈 Tools, Systems & Languages Mentioned:
— Smartabase, Kitman Labs, Edge10, Apollo (as AMS examples)
— Power BI, Tableau, Python, SQL, Apache Iceberg, Delta Lake
— Data lakehouse platforms like Databricks and Snowflake

👉 If you’re deciding how to build or buy an AMS, architecting your next data stack, or just want to geek out on sport-specific infrastructure decisions — this one’s for you.

💬 Drop your thoughts or questions in the comments. Let’s talk architecture, latency, data normalization, and athlete availability metrics.

🔔 Like + subscribe for more tech + sport science breakdowns like this.