Building a Serverless production-ready service to service Python Data API on Google Cloud

Learn how to start handling security, scalability, access, and documentation in a modern Data API.

Imagine, you’ve developed a simple python data API following many tutorials, and now some questions come to your mind.

• How our API could handle hundreds or even thousands of requests?
• How could you establish a minimum security level?
• What is the easy way to share your API with other departments
• Generate and share documentation in a simple way

This post aims to answer these questions. Let’s start with the architecture we will develop. This architecture is focused that the API will be consumed by another service, not a final user (service to service).

Architecture to be developed

Understanding GCP components

First It’s important to understand the components:

• BigQuery: BigQuery is Google’s fully managed, petabyte-scale, low-cost analytics data warehouse. BigQuery is NoOps — there is no infrastructure to manage and you don’t need a database administrator [BigQuery Doc]. BigQuery is our data warehouse so all the data needed by the APIs live here. It is not designed for consuming APIs so the data will be…