[OC] Representation of attention on wikipedia over the last ten days Visualization

What This Visualization Shows

This data visualization displays "[OC] Representation of attention on wikipedia over the last ten days" and provides a clear visual representation of the underlying data patterns and trends. The visualization focuses on 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](https://app.fabric.microsoft.com/view?r=eyJrIjoiYzVhYmFlYTctOWE4Zi00ZjgxLWIzYjgtNjI4NWRjODIzNGRiIiwidCI6ImNjNTA2ODBjLTBmZTctNGQ5YS04ZWVkLTRjNWE1NjZkYzYxNSJ9)

If you're interested in how it was built, the repository (including the data model and documentation) is available here: [Github Link](https://github.com/Edgar454/wikipedia-analytics-warehouse/tree/main)

Datasources: Bigquery public datasets wikipedia Visualisation tool : Power BI

Don't hesitate to give me your opinion on what I could improve !, which allows us to understand complex relationships and insights within the data through visual storytelling.

Deep Dive into the Topic

This data visualization represents a sophisticated analysis of complex information patterns that provide valuable insights into underlying trends and relationships. Data visualization serves as a bridge between raw numerical data and human understanding, transforming abstract statistics into comprehensible visual narratives.

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Data Analysis and Insights

The patterns revealed in this visualization demonstrate the importance of systematic data analysis in understanding complex phenomena. By examining different data segments, time periods, and categorical breakdowns, we can identify trends that inform strategic planning and decision-making processes.

Statistical analysis of this data reveals variations across different dimensions that provide insights into underlying drivers and relationships. These patterns help identify areas of opportunity, potential risks, and key performance indicators that can guide future actions and resource allocation.

The analytical approach used in this visualization enables comparison across different categories, time periods, or geographic regions, revealing insights that support evidence-based decision-making. This type of analysis is essential for organizations seeking to optimize performance and understand complex market dynamics.

Significance and Applications

This data visualization has important implications for understanding trends and patterns that affect decision-making across multiple sectors. The insights derived from this analysis can inform policy development, business strategy, resource allocation, and operational improvements.

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