FanDuel Powers Real-Time Personalization During Major Sports Events with Snowplow and AWS

FanDuel stands as America's leading sportsbook and iGaming platform. It operates across 24+ states with a comprehensive digital ecosystem spanning sportsbook, daily fantasy, casino, racing, and broadcast media through FDTV. As part of Flutter Group alongside brands like PokerStars and Sportsbet, FanDuel manages complex cross-domain data challenges while serving millions of customers in a highly regulated environment.

The company's strategic focus on AI-driven personalization and recommendation systems created an urgent need for comprehensive behavioral analytics. During major sporting events like the Super Bowl or NBA Finals, FanDuel experiences massive user engagement within finite time windows, making real-time insights critical for optimizing player experience and business outcomes.
‍

Challenge: Breaking Free from Analytics Limitations

FanDuel's previous packaged analytics infrastructure created fundamental barriers to its personalization objectives. These limitations presented challenges that were both technical and strategic in nature.

Data Silos and Integration Complexity: The company’s third-party analytics tools operated in isolation, failing to integrate directly with FanDuel's data lakehouse, Databricks.. As Tony Cui, Senior Data Engineering Manager at FanDuel explained:   
‍

"Some of our previous analytics tools didn’t deliver data directly into our warehouse. Instead, we had to develop and manage custom ETL pipelines to extract the data from these vendor systems, which created significant engineering overhead and delayed our time to insight."
Tony Cui, Senior Data Engineering Manager, FanDuel 

Cost-Prohibitive Event Tracking: FanDuel used several traditional analytics tools. These solutions had pricing models that made comprehensive behavioral tracking economically unsustainable. High-volume impression events, crucial for understanding user engagement, became prohibitively expensive.  FanDuel was forced to limit data collection at the very time when comprehensive tracking would have provided the greatest value.

Fragmented Schema Management: Without centralized governance, FanDuel’s schema management occurred through Google Docs, Confluence pages, and spreadsheets, resulting in version control issues and communication gaps between teams. This fragmentation led to downstream analytical inaccuracies when upstream changes occurred without proper coordination.

Incomplete User Journey Visibility: Most critically, FanDuel lacked comprehensive visibility into customer behavior patterns. As Tony noted:
‍

"Our systems capture when transactions are completed—when a customer places a bet or makes a wager—but we were missing the complete user journey. We didn’t have visibility into the steps they took to get there: what they clicked, what they considered, how they navigated through our platform before making that final decision."
Tony Cui, Senior Data Engineering Manager, FanDuel ‍

This lack of user journey insight, severely limited the team’s ability to optimize recommendation algorithms and personalization strategies.

Real-Time Processing Requirements In iGaming, timing determines value. During major sporting events, delayed insights means missed opportunities. Tony emphasized this point:
‍

“In our business, timing is everything. During major sporting events like the Super Bowl or NBA Finals, we have a finite window to act on user behavior insights. If we receive that information four hours after the event ends, it's essentially worthless. We've already missed the opportunity to optimize campaigns, adjust promotions, or personalize the experience when it matters most.”
Tony Cui, Senior Data Engineering Manager, FanDuel

Solution: AWS-Powered Behavioral Analytics Platform on a Databricks Lakehouse

To tackle these challenges, FanDuel partnered with Snowplow, AWS, and Databricks to architect a comprehensive data platform. 

Private Cloud Architecture for Regulatory Compliance: FanDuel deployed Snowplow directly within its AWS environment, utilizing native services including Amazon Kinesis and EC2 while maintaining complete data sovereignty. As Tony explained: 
‍

"In the regulated iGaming industry, data security isn't negotiable. We operate under strict compliance requirements across 24+ states, each with their own regulatory frameworks. Having the ability to route all behavioral data through our own VPCs and private networking infrastructure isn't just a preference, it's a fundamental security requirement that ensures we maintain complete control over customer data and meet our regulatory obligations."
Tony Cui, Senior Data Engineering Manager, FanDuel

Not only does this architecture ensure compliance with strict iGaming regulations, but it also delivers the performance required for real-time operations.

Seamless AWS Integration Strategy: Snowplow’s customer data infrastructure (CDI) is deployed directly within FanDuel's AWS environment. It makes use of native AWS services to create a comprehensive behavioral analytics platform. 

FanDuel’s Snowplow implementation utilizes Amazon Kinesis Data Streams for real-time user interaction capture and Amazon S3 for cost-effective storage of massive behavioral datasets. 

Snowplow also integrates seamlessly with FanDuel's existing data architecture. This includes the company’s Kafka backbone and Databricks lakehouse, fitting naturally into its established medallion architecture.

‍

‍

Centralized Data Governance Framework: FanDuel replaced its fragmented documentation with Snowplow’s Data Product Studio, providing the company with codified, version-controlled schema management. This centralized approach provides the governance framework necessary for regulated operations while eliminating the communication gaps that previously caused downstream analytical inaccuracies.

Cost-Optimized Event Processing Unlike volume-based pricing models that constrained comprehensive tracking, the AWS-native architecture enables economically viable capture of high-frequency events. FanDuel now instruments impression events and behavioral micro-moments without cost limitations affecting data collection scope.

Expert Partnership Model: Beyond technology delivery, FanDuel’s collaboration with Snowplow and AWS provided strategic behavioral analytics expertise that extended the company’s internal capabilities. As Tony explained:
‍

"Having partners who are experts in understanding behavioral information and application-level data really helps when it comes to bouncing ideas and making sure we're implementing things the right way."
Tony Cui, Senior Data Engineering Manager, FanDuel

Transformational Business Impact

FanDuel has completely transformed its behavioral analytics capabilities. With Snowplow on AWS, the company has delivered immediate operational improvements while establishing the foundation for advanced personalization.

Key Technical Achievements:

• Comprehensive User Journey Visibility: Complete behavioral tracking across all customer touchpoints and applications
• Real-Time Processing Capability: Insights delivered within minutes rather than hours during critical sporting events
• Eliminated ETL Overhead: Direct integration with Databricks removed complex pipeline management requirements
• Cost-Effective Scale: High-volume event tracking without prohibitive pricing constraints
• Enterprise Data Governance: Centralized, version-controlled schema management within a secure AWS environment

Strategic Business Outcomes: FanDuel’s new data set up has enabled greater analytical democratization across the organization. Product managers, marketing teams, and promotional teams have gained access to comprehensive user journey data. As a result, they can carry out sophisticated campaign optimization and personalization strategies while maintaining cost efficiency through AWS's scalable infrastructure. Tony explained: 
‍

"With comprehensive behavioral data, we can analyze user journeys and conversion funnels much faster than before. This speed enables us to be significantly more dynamic—not just in powering our recommendation algorithms and personalization engines, but also in delivering actionable insights to our marketing teams for campaign optimization and promotional strategy development."
Tony Cui, Senior Data Engineering Manager, FanDuel

Operational Transformation: FanDuel is now positioned to develop advanced recommendation systems leveraging comprehensive behavioral data. With its new data infrastructure in place, teams are exploring integration opportunities with Amazon SageMaker for real-time inferencing and model retraining capabilities–sophisticated AI applications that would have been architecturally impossible with the company’s previous fragmented analytics infrastructure.

Future Outlook: AI-Powered Personalization at Scale

FanDuel views its Snowplow and AWS-powered behavioral analytics platform as the foundation for sophisticated AI-driven customer experience optimization. 

The company follows a strategic crawl-walk-run approach, progressing from comprehensive data collection toward real-time model optimization and dynamic personalization. As Tony explained:
‍

"Our approach follows a deliberate progression: first, we need comprehensive behavioral data to understand complete user journeys in depth. Once we have that foundation established, we can then advance to more sophisticated applications such as exploring real-time model optimization, automated retraining systems, and dynamic inferencing capabilities that will power our next-generation personalization engines."
Tony Cui, Senior Data Engineering Manager, FanDuel

This measured approach, built on AWS's scalable infrastructure and Snowplow's behavioral data expertise, positions FanDuel to maximize its data investment while maintaining the operational reliability essential in regulated industries. 

FanDuel’s implementation offers a blueprint for enterprise-scale personalization. It demonstrates how combining specialized behavioral data expertise with cloud-native infrastructure can eliminate traditional analytics limitations. And crucially, it shows how organizations can accelerate AI-driven customer experience innovation.