Building a Production-Ready Serverless App on Google Cloud (Part 1: Architecture)
The Problem In my previous post, I shared how I used an AI agent framework during a train ride to build a Proof of Concept (POC) for a project called the Dog Finder App. The response was great, but...
Source: DEV Community
The Problem In my previous post, I shared how I used an AI agent framework during a train ride to build a Proof of Concept (POC) for a project called the Dog Finder App. The response was great, but the experiment raised a technical question: How do you build a POC quickly without creating a messy monolith that you'll have to rewrite later? When building a data-intensive application, engineers usually face a harsh trade-off. You can either build it fast to prove the concept (and inherit massive technical debt), or you can build it "right" (and spend weeks provisioning infrastructure and writing boilerplate). By leveraging serverless services on Google Cloud Platform (GCP), we can break that trade-off. This is the first in a three-part series where I will show you how to architect, automate, and deploy a complete, decoupled data application. We will look at how combining serverless tools with strict Data Engineering practices allows you to spin up a solution that is both incredibly fast
