Formula 1, which engages over 800 million fans globally, faced challenges with its MarTech platform, which required 6 to 8 weeks of manual engineering for each new data source. According to Chris Roberts, Director of IT at Formula 1, the platform had an 18-month backlog just to integrate 12 new sources. The business was generating data faster than the engineering team could wire it up. Matt Kemp, F1 Head of Data Operations, sought a solution that was repeatable, robust, and reliable. AWS worked backwards from their needs to implement an agentic solution that worked end to end, applying business logic at each step. In early 2026, F1 and AWS built the Data Accelerator, a solution that uses agentic AI on Amazon Bedrock AgentCore to transform F1’s MarTech data platform from a manually maintained system into a self-managed, observable, and unified data estate. This solution reduced data source onboarding from up to 8 weeks to approximately 40 minutes of code generation plus hours of deployment. It also identified and fixed data source anomalies in production, tracked data platform operations and agent lineage in a single window, and opened a gateway for analysts, engineers, and scientists to collaborate. Roberts noted that for the first time, they have end-to-end visibility across the entire MarTech platform with data lineage and root cause analysis, not just dashboards full of alerts.

The Data Accelerator addressed three main challenges: manual onboarding of new data sources, keeping pace with evolving upstream feeds, and fragmented visibility. Onboarding each new data source was a heavily manual effort, with engineers writing schema mappings, building ingestion pipelines, configuring data quality checks, defining GDPR classifications, and setting governance policies by hand. This process took 6 to 8 weeks per source. The platform also had to keep pace with constantly evolving upstream feeds, where providers frequently changed column names, added fields, or restructured and rescheduled payloads without notice. These changes often surfaced at the worst possible moment, such as mid race-weekend or during a mission-critical campaign launch. Visibility was fragmented, with logs scattered across services and no unified data lineage. When a stakeholder questioned a metric, engineers spent hours manually tracing the issue across Amazon S3 paths, Amazon Redshift control tables, Airflow logs, and DBT outputs.

Source: awsml