IoT data platform: Efficiency and innovation in the sports and health sector

A leading company in the sports and healthcare sector implemented a cloud-based data platform to optimise the analysis and processing of performance and tracking data. The goal was to create a scalable, flexible and cost-efficient solution that integrates various data sources and provides domain-specific dashboards for coaches, analysts and medical staff.

The customer faced the challenge of consolidating data from different systems (e.g. GPS tracking, medical devices and scouting databases). The data was available in various formats and had to be converted into a uniform schema. In addition, the data had to be available quickly for analysis and reporting to enable timely decisions.

The solution is based on a modern, cloud-based architecture that has been specially developed for processing and analysing IoT data. It combines serverless technologies with scalable data processing and automation tools to ensure efficiency, flexibility and cost-effectiveness. The most important components are:

Data integration and storage

AWS S3: Serves as the central storage medium in a multi-layered data lake architecture. Structured and unstructured data is stored here securely and scalably.

AWS Lambda: Used for data ingestion and transformation. Lambda's serverless architecture enables automatic scaling and reduces operating costs, as you only pay for what you actually use. Lambda processes data from IoT sources such as GPS tracking devices and medical sensors in real time.

Amazon SQS: Used to buffer and manage data events. This is particularly useful for mass updates or for deduplicating events to ensure reliable data processing.

Data processing and orchestration

• AWS Step Functions: Orchestrates the various processes within the data lake layers. This enables clear separation and automation of tasks such as data ingestion, transformation and storage.
• AWS Glue: Used to catalogue and transform data to prepare it for queries and analysis.
• Amazon Athena: Enables fast SQL queries on the catalogued data in the SERVED layer without having to manage your own database.
• Snowflake: A separate data warehouse used for in-depth analysis and reporting. It complements the AWS architecture with its powerful query engine and scalability.

Visualisation and user access

• Tableau: Serves as a visualisation tool to provide user-friendly dashboards for trainers, analysts and medical staff. The dashboards access data from Snowflake, Athena and Google Sheets.

Automation and DevOps

• GitLab CI/CD: Automates the deployment of the solution, including AWS Lambda functions, Step Functions and other infrastructure components.
• Automated testing and validation in the CI/CD pipeline ensure the quality and stability of the solution.

Monitoring and security

• AWS CloudWatch Logs: Monitors the solution's activities and provides insights into performance and error analysis.
• AWS X-Ray: Used for tracing and optimising the pipeline during the development and operational phases.
• VPC Flow Logs: Provide additional security and transparency by monitoring network traffic within the AWS environment.

This solution enables IoT data from various sources to be processed, analysed and presented in user-friendly dashboards in real time. It is scalable, cost-effective and supports data-driven decisions in the sports and healthcare sectors.

• Increased efficiency: Automating data integration significantly reduced manual tasks. Data is available for analysis within 2-3 minutes of a change in the source system.
• Scalability and flexibility: The architecture is virtually unlimited in its scalability and supports both small and large amounts of data.
• Cost efficiency: Operating costs were minimised thanks to the serverless architecture of AWS Lambda and the use of S3.
• Fast decision-making: Coaches and analysts can analyse performance data immediately after training sessions, competitions or games and provide feedback.

The AWS-based solution enabled the customer to establish a modern, cloud-based data platform that meets all requirements for availability, consistency, scalability and security. Close cooperation with the customer and the use of agile methods resulted in a sustainable and future-proof solution.