Summary

This chapter introduced the scientific side of FastAPI by showing that API services can provide numerical computation, symbolic formulation, and graphical interpretation of data via the numpy, pandas, sympy, and matplotlib modules. This chapter also helped us understand how far we can integrate FastAPI with new technology and design strategies to provide new ideas for the microservice architecture, such as using GraphQL to manage CRUD transactions and Neo4j for real-time and node-based data management. We also introduced the basic approach that FastAPI can apply to solve various BPMN workflows using Celery tasks. With this, we have started to understand the power and flexibility of the framework in building microservice applications.

The next chapter will cover the last set of topics to complete our deep dive into FastAPI. We will cover some deployment strategies, Django and Flask integrations, and other microservice design patterns that haven’t been discussed in...