Real-time statistics and rankings engine for global sports events
This project is a web-based sports analytics platform that aggregates, processes, and displays detailed statistics for sports events and teams in real time. It allows users to explore event dashboards, rankings, and historical performance metrics through an intuitive web interface. The platform delivers fast and reliable access to large volumes of live and historical sports data, helping end users make informed, data-driven decisions and track performance across multiple tournaments and seasons
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Quick facts
  • Real-time statistics for hundreds of concurrent sports events
  • Unified ranking engine aggregating multi-source event data
  • Low-latency data delivery using Redis Cache and RabbitMQ
  • Distributed storage combining MariaDB and Apache Cassandra
  • Automated business workflows powered by Camunda Process Engine
Client info

The client is an international company operating in the sports data and digital media domain. They provide online products that present structured information about sports events, teams, and competitions to end users across several markets. Due to NDA restrictions, the company name and specific brands cannot be disclosed, but their audience includes sports enthusiasts and professionals who rely on accurate and timely statistics

Challenge
  • Fragmented data flow from multiple external sports data providers
  • Lack of a unified statistics and ranking engine across different sports and competitions
  • Performance bottlenecks when processing and displaying large volumes of live event data
  • Limited internal capacity to design and implement a robust, scalable backend in a short timeframe
  • Need to orchestrate complex business workflows from data ingestion to user-facing presentation
Solutions
  • Designed and implemented a Java 15–based backend responsible for ingesting, normalizing, and aggregating sports event statistics
  • Built a distributed data layer using MariaDB for transactional data and Apache Cassandra for large-scale, time-series and historical statistics
  • Implemented Redis Cache to reduce response times for frequently accessed rankings, event pages, and statistics summaries
  • Used RabbitMQ for asynchronous communication between services, ensuring resilient data processing pipelines and decoupled components
  • Integrated Camunda Process Engine to orchestrate complex workflows, such as data ingestion, validation, aggregation, and publishing to the frontend
  • Developed a web UI using JavaScript, HTML, CSS, and Apache Velocity templates to present rankings, event overviews, and team statistics in a clear, user-friendly format
  • Ensured extensible architecture so new sports, competitions, or data sources can be added with minimal impact on existing functionality
Technologies
Apache Cassandra
Apache Velocity
Camunda
CSS
HTML
Java
JavaScript
MariaDB
RabbitMQ
Redis Cache
Business impact
  • Achieved stable, low-latency access to real-time statistics for ongoing sports events
  • Improved reliability of rankings and statistics thanks to a unified aggregation and validation workflow
  • Reduced time-to-market for launching new sports and competitions due to modular architecture and configurable processes
  • Lowered operational risks by using a scalable, distributed data storage solution and message-driven communication
  • Strengthened the client’s position in the sports data market by providing richer, more responsive analytics to their end users
Team
  • 2 Developers
Looking to build or modernize a high-performance sports analytics or data-heavy web platform?
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