Walt Disney World's Approach to a data-driven guest experience is what initially interested me in the field of data. 🏰🎢 I've always been amazed by how seamlessly it can impact my Disney day for the better. Now that I have the skills and knowledge to do so, I started to dive deeper into this passion and interest. This started with the Capstone project for my Masters Degree. I'd love for you to read more about that here.
This project is intended to be an extension to my capstone. I'm taking it one step further. I'll be building out a full end-to-end cloud data project, from resource creation with Terraform, to data ingestion with GCP Cloud Scheduler, Pub/Sub, and Cloud Functions.
🚧 This is a work in-progress, so feel free to share any feedback or ideas! 🚧 👷♀️
🏗️ Terraform: Generates & maintaines all cloud resources required for this project.
💽 Cloud Scheduler, Pub/Sub & Cloud Function: Ingests wait time data every 15 minutes during park hours and writes to cloud storage.
🗄️ Cloud Storage: Acts as a external table to the BigQuery staging table.
☁️ BigQuery: This is the main structured data source for the project.
Dataflow Pipeline: This pipeline will clean the data and combine data from other sources like ride metadata, park metadata and more.
Looker Dashboard: This will be a user friendly view of current wait times, historical wait times and predictions.
Machine Learning Model: This model will predict wait times to help theme park guests plan their day efficiently.