Operating at the intersection of athlete performance and data insights, a leading sports analytics company, renowned for its cutting-edge analytical capabilities, struggled with the limits of its data architecture. With growing operations and a user base that relied on real-time insights into sports performance, the client accumulated over 10TB of production database from biomechanic sensors, with a complex mix of SQL and key-value datasets.
To help its customers with live performance breakthroughs that come in millisecond motion and speed alterations, the client needed to overcome issues in its data landscape that were slowing down customer-facing applications and delaying analysis, which could ultimately undermine its value proposition.
A data deluge that was harder to manage in real time
The client struggled to manage a vast production database, which was critical to both analytics and customer-facing applications. Running extract, transform, and load (ETL) jobs on the production database, required for moving or modifying data in real time, created bottlenecks that resulted in sluggish API performance, causing delays and inefficiencies in mobile and web applications crucial for customer interaction.
Additionally, the heavy analytics workload during peak hours clashed with customer-facing services, leading to slow response times that impacted user experiences. The monolithic architecture further complicated operations by merging transactional, analytical, and key-value workloads, preventing effective optimization and scalability. These issues collectively threatened the client’s ability to provide timely, high-quality insights, potentially undermining their competitive edge in the sports analytics market.
Recognizing the need to overhaul its data architecture, the client turned to Persistent to reimagine how data was processed and insights delivered in real time.
Real-time insights with data transformation powered by Persistent & AWS
Persistent partnered with AWS to create a scalable system that addressed the client’s business challenges head-on. By replicating data from the production database to a read replica using AWS Database Migration Service (DMS), we eliminated performance bottlenecks and ensured seamless operation of customer-facing applications.
Using AWS Step Functions and Lambda functions, we orchestrated ETL processes to enable efficient data transformation, which directly mitigated delays in delivering real-time insights. Storing transformed data in Parquet format on Amazon S3 provided a scalable, cost-effective solution that supported more extensive and complex analytics without compromising performance.
Leveraging AWS Simple Queue Service (SQS) and EventBridge to manage task scheduling and event-driven execution further streamlined workflows, reduced manual intervention, and minimized errors. These solutions collectively enhanced the client’s ability to deliver high-quality, timely insights, thereby strengthening its competitive edge in the sports analytics market.
High Performance at Scale
The AWS-backed data architecture was transformative for the client with business outcomes, such as improved system performance with decoupled transactional and analytical workloads. This allowed for a significant enhancement in system performance, making real-time applications faster and more reliable.
The use of cloud-native services for data orchestration and storage laid the foundation for scalable, cost-effective analytics. The client could now handle larger datasets and more complex queries without performance degradation.
Automated workflows through AWS services ensured that data processing was not only efficient but also more streamlined, reducing the need for manual intervention and minimizing errors.
This journey showcases the profound impact that a well-designed data architecture can have on business operations, paving the way for innovation and growth in the competitive world of sports analytics.
