ETL Project: Soccer Data/Winner Predictions

Overview

This project is focused on building a robust and scalable ETL pipeline to process, analyze, and visualize soccer game data in real-time. It leverages batch and streaming data processing to deliver actionable insights to end users.

Architecture

• Data Flow: Data is ingested from various APIs into Kafka, processed using Flink for real-time streaming analytics, and Spark for batch processing and transformations.
• Orchestration: Airflow is used to coordinate and manage the workflows seamlessly.
• Data Storage: Processed data is stored in AWS S3 for raw data and Google BigQuery as a batch data warehouse. Metrics are also stored in ClickHouse for real-time analysis.
• Visualization: Dashboards are created using Superset and Grafana to enable end users to interact with the data and gain valuable insights.

Technologies Used

• Languages: Python
• ETL Tools: Kafka, Flink, Spark, Airflow
• Storage: AWS S3, BigQuery, ClickHouse
• Visualization: Superset, Grafana
• Cloud Platform: AWS

Data Flow Diagram

Future Plans

• Integrating AI-driven predictive models for game outcome forecasting.
• Developing a user-friendly frontend to enhance accessibility.

This project showcases modern data engineering and processing techniques to deliver meaningful and timely analytics to end users.