Data Architectures in Athlete Management Systems (AMS)
Explore the key differences between data lakes, warehouses, and lakehouses in athlete management systems. Learn how platforms like Smartabase, Kitman Labs, Edge10, and Apollo structure data—and when to buy vs. build your own solution. Perfect for sport scientists and data pros.
🔍 What’s Covered in This Video:
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.
Mastering Data Architectures in Athlete Management Systems
🔍 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.
