Football Insights 360 Building An End To End Etl Analytics Medium

Bonisiwe Shabane

-Nov 18, 2025, 6:07 AM

football insights 360 building an end to end etl analytics medium

Ever wondered how football insights are powered behind the scenes? From predicting match trends to analyzing player performance — data makes it all possible! In this step-by-step guide, I’ll show you how I built Football Insights 360 — a fully automated ETL pipeline using AWS services. We’ll go from raw match data to real-time dashboards that uncover fascinating insights using:AWS Lambda for data extractionAWS Glue (PySpark) for transformationAmazon S3 for Data StorageAmazon Redshift for Data WarehouseLooker Studio for visualization This project brings my passion for data engineering and football together, because who doesn’t love a game powered by data? Ready to dive in and see how it all comes together?

Let’s kick off! My name is Anuraag Gujje, and I am passionate about data engineering, analytics, and building scalable ETL solutions. Link to the project repository: Football Insights 360 Git To develop a robust data pipeline that automatically collect, store, and preprocess the 950+ football leagues data, facilitating advanced analysis and predictive modeling. Data Analytics Engineer | Supply Chain Consulting | AI/ML powered Demand Planning & Forecasting | Business Intelligence | MS, Business Analytics and Information Systems | NITW ‘21 | Ex o9 ⚽ Football Insights 360: Building an End-to-End ETL & Analytics Pipeline Excited to share my latest project - Football Insights 360, a perfect blend of my passion for data engineering and football!

This end-to-end solution automates data extraction, transformation, and visualization to generate real-time analytics and insights from football data. In this project, I built a fully automated AWS ETL pipeline leveraging AWS Lambda, Glue (PySpark), S3, Redshift, Airflow and Looker Studio to extract, transform, and analyze football match data from API-SPORTS. The dashboard provides real-time insights into team standings, player statistics, and match trends. 🔗 If you are interested, check out the full breakdown of my project here: https://lnkd.in/eM449Tfe This journey was truly an amazing practical learning experience! Faced multiple challenges like handling API rate limits, processing semi-structured and nested JSON data, optimizing query performance, and Looker Studio response times. But through iterative optimizations like scheduled Lambda triggers, PySpark transformations, and Redshift Materialized Views, I was able to enhance performance significantly.

Would love to hear your thoughts and feedback! Drop a comment if you have anything to discuss or suggestions. 🙌 #AWS #ETL #BigData #DataEngineering #FootballAnalytics #Redshift #PySpark #LookerStudio Football enthusiasts often crave detailed insights into matches, and providing a dynamic and interactive platform for this is a game-changer. In this project, I built a full-stack football analytics pipeline that allows users to search for specific matches by entering team names and match dates. The backend fetches data dynamically, processes it, and updates a Power BI dashboard in real time.

The pipeline has been deployed on cloud platoform, GCP, allowing scalability and making it easy to be globally accessed. The pipeline consists of the following key components: The frontend provides users with a simple interface to: Once users enter this information, it is sent to the backend via an HTTP POST request. Get clone repo: get clone https://github.com/trungbac11/football-etl-pipeline.git SHOW GLOBAL VARIABLES LIKE 'LOCAL_INFILE';

Dagster will be running on: http://localhost:3001 start the Streamlit app: http://localhost:8501 If you’re passionate about football and data, this Arsenal FC data pipeline project is an ideal way to practice and learn essential data engineering skills. In this Medium article, we will walk through the entire pipeline setup, from raw data extraction to insightful visualizations, using real-world technologies. This project is a comprehensive end-to-end data engineering pipeline designed to analyze historical performance data of Arsenal FC. It utilizes cutting-edge technologies and tools like Docker, Apache Spark, PostgreSQL, Apache Airflow, and PowerBI for data extraction, transformation, orchestration, and visualization.

Let’s dive into setting up this project on your local machine. Before getting started, you’ll need the following installed: Docker is essential here for setting up an isolated, consistent environment to run the necessary components like PostgreSQL, Apache Spark, and Airflow. In this project, we build an ETL (Extract, Transform, Load) pipeline using the Football-Data API on AWS. The pipeline retrieves Premier League match data, transforms it into a structured format, and loads it into AWS data stores for querying and analysis. We are using the Premier League match data from the Football-Data.org API.

The dataset contains information such as: To access the API, sign up at football-data.org and use the provided token with the header: You can integrate Amazon QuickSight, Power BI, or Tableau with Athena for visualizations like: Since our inception, Hudl Statsbomb has been at the forefront of educating the next generation of football analysts. We’re committed to providing materials and resources to develop the skills needed to enter the industry, from free datasets and code to industry-standard education and training courses. We’ve opened the doors to our extensive knowledge base, all to help aspiring analysts hone their craft.

One of the most common questions we receive is, “How do I get started in football analytics?” This article is our answer — a comprehensive guide that gathers all our resources in one place,... Whether you’re just starting out or looking to upskill, you’ll find everything you need here. For those at the very beginning of their journey in football analytics, understanding the roots of the field is crucial. Both historical context and foundational knowledge are needed to build your skills. The Hudl Statsbomb Archive is an excellent starting point: a curated collection of 10 of our most popular articles dating back to 2013. These pieces capture the evolution of analytics over the past decade, providing a view into how the field has developed and progressed since it began.

There is a lot to learn from here. For instance, Colin Trainor’s work on PPDA in 2014 remains one of the best resources for seeing the thought process behind creating football-relevant metrics – one that is still used in the field today. James Yorke’s work on Pass Footedness and Euan Dewar’s deep dive on Aston Villa in 2020/21 demonstrate how to produce highly effective visualisations and stories from football data. And there’s plenty more. The Archive is a treasure trove for anyone looking to understand the foundations of the industry.

Football Insights 360 Building An End To End Etl Analytics Medium

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