u/Business-Journalist7

Hi everyone,

Over the past month, I've been building an analytics platform around the Wikimedia Pageviews dataset. The original goal was to learn dbt and improve my data engineering skills, but along the way I became fascinated by a simple question:

What is actually popular on Wikipedia?

The result is an interactive dashboard where you can explore trends, compare language communities, and analyze how attention evolves.

You can explore the full dashboard here:
Dashboard Link

If you're interested in how it was built, the repository (including the data model and documentation) is available here:
Github Link

Datasources: Bigquery public datasets wikipedia
Visualisation tool : Power BI

Don't hesitate to give me your opinion on what I could improve !

Hi everyone,

I've been working as a data engineer for about a year, and over the past month I wanted to push myself by building something significantly larger than the usual tutorial project.

The result is an end-to-end analytics platform built around the Wikimedia Pageviews dataset.

The original objective was simply to improve my dbt skills, but the project gradually expanded into something much larger.

One of the biggest challenges turned out not to be the scale (roughly 2 TB of public data), but the data model itself. Wikimedia publishes pageviews at the page level, while I wanted to analyze attention at the entity level. Solving that required reconciling pages across more than 300 language editions using Wikidata before building the analytical warehouse.

The project now includes:

• BigQuery as the analytical warehouse
• dbt for semantic modeling and testing
• Terraform for Infrastructure as Code
• AWS Fargate for orchestration
• GitHub Actions CI/CD with path-based deployments
• CloudWatch dashboards for operational monitoring and BigQuery cost attribution
• Optional Power BI deployment through the REST API
• Fully deployed dbt documentation
• End-to-end reproducible infrastructure

Everything is documented, including the modeling decisions, infrastructure architecture, dashboard methodology, and deployment process.

Repository: Github Link

Live dashboard: Dashboard Link

dbt documentation: Dbt docs link

I'm posting here because I'd genuinely appreciate feedback from other data engineers.

If this project landed in front of you during a hiring process:

• What would stand out positively?
• What would you improve?
• Are there parts that feel over-engineered or under-engineered?
• Is there anything missing that you'd expect from an end-to-end data engineering project?

I'm especially interested in feedback on the architecture, modeling choices, CI/CD pipeline, documentation, and overall project structure.

Thanks in advance!