Easygo accelerates analytics by 95% by building a lakehouse on AWS

As Easygo expanded into new markets and grew its global user base, the volume of data generated across its entertainment platforms increased dramatically. Teams responsible for fraud detection, customer experience, and business strategy needed timely, accurate analytics to support decisions in a 24/7 operating model. At peak periods, large numbers of internal users were querying the same datasets simultaneously, which slowed performance and made it harder to deliver the rapid insights needed for critical use cases like fraud alerts. “When data isn’t available at the moment it’s needed—especially for things like monitoring thresholds or reviewing suspicious activity—it delays the decisions the business will make,” says Carine Oliveira, engineering manager at Easygo.

Easygo’s engineering and business intelligence (BI) teams were also managing this growth with lean resources while supporting operations across several regions. Expanding into additional regulated markets also increased the need for stronger data consistency, governance, and region-specific controls. To continue scaling its products and maintain strong data reliability, Easygo needed an approach that reduced maintenance effort, improved performance, and supported a consistent architecture that could be replicated in new markets.

As an existing AWS customer, Easygo turned to the cloud provider to modernize its data foundation and support near real-time analytics at scale, adopting a lakehouse architecture built entirely on AWS to improve performance, reduce operational load, and strengthen data reliability. The company implemented a streaming-first pattern using Amazon Kinesis to capture and process events in real time, Amazon Simple Storage Service (Amazon S3) as the central data lake, and Amazon Athena for faster querying on partitioned datasets. Easygo also incorporated Amazon Redshift Serverless to give analysts consistent performance during peak periods without manual capacity management.

Many of Easygo’s core operational workloads run on Amazon Aurora PostgreSQL, and historically the analytics team relied on separate extract, transform, and load (ETL) processes to move this data into Amazon Redshift. By introducing Amazon Aurora zero-ETL Integration with Amazon Redshift, Easygo now moves data from Aurora PostgreSQL into Amazon Redshift automatically, reducing complexity and helping analysts access fresher data without maintaining additional pipelines.

Security and governance were also central to the new design. Easygo implemented AWS Key Management Service (AWS KMS) to encrypt data in transit and at rest using its own private keys, and deployed services within private Amazon Virtual Private Cloud (Amazon VPC) environments to maintain region-specific controls.

By modernizing its data platform, Easygo increased both the speed and reliability of its core analytics workloads. Query execution times improved by 95 percent, with workloads that previously required up to 1 hour now completed in about 10 minutes. The company also saw an 80 percent reduction in data ingestion-related incidents, as the new Amazon Aurora zero-ETL approach removed many of the disruptions that previously occurred during maintenance of its older pipelines. With fresher data available more consistently, teams responsible for fraud detection no longer wait 15–30 minutes for updates to thresholds or suspicious-activity indicators, giving them access to insights in near real time. “Changing our architecture to a more modern lakehouse on AWS helped us improve performance and set up a foundation we can reuse as we grow into other markets,” says Oliveira.

The shift to the new lakehouse architecture also helped reduce the amount of time engineers spent supporting legacy data processes. Previously, recurring operational issues required at least 3 engineers from different departments to spend roughly 3 hours each week addressing pipeline interruptions. With the consolidated architecture and zero-ETL integrations, those interruptions are no longer a recurring concern, empowering teams to focus on business priorities such as machine learning model development and supporting additional AI-driven initiatives across the organization.

As Easygo continues expanding into regulated markets, the design of the lakehouse on AWS provides a consistent pattern that the company can deploy across multiple regions. This unified approach supports clearer governance practices, stronger access controls, and more predictable deployments as the organization grows its global footprint. Easygo is also evaluating AWS Lake Formation to centralize governance and further streamline access management across its data estate.