Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
Data Science
Towards Data Science·Sep 9·5 min read
When One Process Becomes Too Much: Splitting a Pipeline into MCP Services
TL;DR
A tightly coupled Python pipeline was successfully split into independently deployable microservices, enhancing scalability and maintainability. This transition allowed for more efficient updates and reduced downtime.
✦ Why It Matters
Consider breaking down your monolithic applications into microservices for improved scalability and easier maintenance.
Key Takeaways
- Identify tightly coupled services that can be separated.
- Implement MCP to define clear service boundaries.
- Avoid shared dependencies to maintain service isolation.
- Test each service independently to ensure reliability.
How It Works
MCP services are designed to operate independently, with each service defined by its own schema and dependencies. FastMCP facilitates the creation of these services by managing interactions without shared code, ensuring that changes in one service do not impact others.