This project focuses on analyzing Olympic Games data using dbt, Google Cloud, and BigQuery. The dataset includes records of athletes, events, and medal winners spanning multiple Olympic Games. The goal is to identify patterns, outliers, and trends in the data, such as:

• Athlete performance trends over time
• Country-wise medal distributions
• Event-specific insights
• Outlier detection (e.g., unexpected performance spikes, anomalies in medal counts)

• dbt (Data Build Tool) for data transformation and modeling
• Google Cloud & BigQuery for data storage and querying
• SQL for data aggregation and cleaning
• Python (Pandas, Matplotlib, Seaborn) for statistical analysis & visualization

• Medal Trends: Analysis of how medal counts have changed over decades
• Country Dominance: Identification of countries with consistent high performances
• Outliers: Detecting unexpected performances (e.g., lesser-known athletes winning multiple medals)
• Event Popularity: Evaluating which sports/events have gained/lost popularity