Performing ETL and analyzing Olympic data using Azure services

The exponential growth of sports data has resulted in an increased requirement for effective data engineering solutions to extract useful insights. To facilitate smooth data integration, transformation, and visualization, this study proposes an end-to-end extract, transform, and load (ETL) pipeline for Olympic data using Microsoft Azure services. Visualizing and analyzing using Power BI for the same data. To replicate the usage of private datasets, the data is sourced from Olympic datasets from Kaggle and safely saves them in GitHub. A scalable and cloud-native storage solution is ensured by Azure Data Factory (ADF), which makes data entry into Azure Data Lake Storage Gen2 (ADLS Gen2) easier. The raw data is cleaned, transformed, and structured using Azure Databricks to improve its usability for analytical applications. After processing, the data is saved back into ADLS Gen2, where it is readily accessible for data visualization and business intelligence. Finally, trends, patterns, and performance measures throughout Olympic history are examined using interactive dashboards created with Power BI. The study shows how effective cloud-based ETL operations are at handling massive amounts of data while providing deeper insights. The results demonstrate how big data, cloud computing, and AI-driven analytics may transform data-driven decision-making in the sports sector, which is in line with the future potential of data analytics in sports.