Until recently, it was difficult to automate complicated data pipelines using Google Analytics 4. That has changed with Dataform, a component of the Google Cloud Platform that allows automating data pipelines.
One of the benefits of Google Analytics 4 is that free accounts also can export data to BigQuery. Before you can export data to BigQuery you have to create a Google Cloud Platform (GCP) which is a set of cloud computing services. In this article, we will go through the process of creating a GCP project.
It started out as a small project. A few lines of code here, a simple database there. Nothing too complicated, nothing that couldn’t be handled by a couple of enthusiastic developers with some spare time on their hands. But as the weeks went by, the project grew. New features were added, the user base expanded, and the data started pouring in.
Before long, the once-simple codebase had become a tangled mess of spaghetti code and poorly designed systems. It was slow, inefficient, and prone to crashes. The developers were overwhelmed, and the project was in danger of collapsing under its own weight.
That’s when the Cloud Architect stepped in. With a deep understanding of software architecture, a strong grasp of the latest technologies, and a laser focus on scalability and efficiency, the Architect quickly identified the problems with the existing system and set to work designing a solution that would meet the needs of the project.