Hike’s growing platform generated petabytes of gaming data that needed to be processed and analyzed in real time. The network throughput and CPU performance of their previous machines lagged behind the requirements of their expanding platform, leading to data processing bottlenecks and delaying critical insights for product enhancements and user engagement.

Hike chose Google Cloud for its ability to effortlessly integrate key services like BigQuery, Google Cloud Storage, and Dataflow. BigQuery stood out for its ability to process vast datasets quickly and cost-effectively. This enabled Hike to calculate key metrics such as daily active users, revenue, and retention performance with unmatched speed. Cloud Storage and Dataflow ensured data workflows were efficient, reliable, and capable of handling Hike’s growing data demands.

“BigQuery’s performance has been impressive. It allows us to process petabytes of data quickly, make better decisions, faster, and become more efficient,” says Gaurav Arora, VP of Engineering and Data Analytics at Hike.

Using the built BI engine, Hike was able to reduce visualization query times from minutes to a few seconds for their live query-based dashboards, further improving performance for aggregate live queries, boosting dashboard responsiveness. Using the Data Canvas feature, other employees using the NLQ feature can now find relevant data themselves, such as the UXR team being able to finetune their user groups without requiring the help of analysts. The team also used Dataform, which helped to optimize hourly batch jobs, reducing runtimes from over an hour to under 10 minutes. These streamlined workflows with modular, dependency-aware pipelines help reduce job failure rate to almost zero.

Hike’s data pipeline employs Kafka to ingest data in real time. The data, after being validated by a Kafka consumer, is stored in Cloud Storage as raw data for scalable and secure storage. Cloud Storage enables integration with downstream processing tools like Airflow and Dataflow, ensuring that raw data ingested by Kafka is efficiently routed for further transformations. Airflow jobs process this data and prepare it for analysis in BigQuery, while Dataflow jobs perform additional transformations, such as standardizing formats and aggregating data to make it analytics-ready. This ensures that data entering BigQuery is optimized for generating actionable insights.

As its gaming platform expanded, Hike needed to balance performance and costs. Resource-intensive processes, such as maintaining its self-deployed Redis infrastructure, consumed valuable engineering time and diverted focus from core innovation. Switching to a managed Redis service provided the performance and scalability Hike needed to meet user demands without compromise.

“Using Redis as a managed service has ensured high availability and reliability, which is critical for handling peak traffic and maintaining an optimal and uninterrupted gameplay experience,” says Arora. By eliminating the operational overhead of managing Redis deployments, Hike’s engineering team was freed to focus on strategic projects and developing new features.

In parallel, Hike used Google Kubernetes Engine (GKE) to dynamically allocate resources and scale up during high-demand gaming events and scale down during quieter periods, avoiding unnecessary expenses. During seasonal gaming promotions, Hike used GKE to handle sudden spikes in traffic without compromising performance or incurring excessive costs. This flexibility enabled Hike to create a highly scalable and cost-efficient infrastructure ready to handle any peak in demand.

By transitioning to Google Cloud, Hike lowered its hardware and resource costs by approximately 15%. These savings, combined with improved CPU capacity and performance, allowed Hike to focus on platform improvements and invest more in delivering value to its users.

To keep growing in a highly competitive gaming market, Hike needed an analytics platform capable of providing deep insights into player behaviors. The team sought to identify disengaged cohorts, understand how users interacted with games, and take timely actions to re-engage them before they churned.

Hike used BigQuery to analyze vast datasets in real-time, uncovering patterns in user activity, cohort performance, and retention rates. For example, Hike identified cohorts of players who disengaged after completing specific game levels. Based on these insights, the team introduced targeted deposit offers, adjusted level mechanics to make progression more rewarding, and tested features such as enhanced in-game rewards to keep players engaged.

“BigQuery helps us track user behavior, identify disengaged cohorts, and take timely actions to re-engage them. This has been essential for improving retention and delivering a better gaming experience,” says Arora.

By analyzing which games or features performed best, Hike prioritized updates that delivered the most value to users. For instance, they enhanced the progression system in top-performing games based on player feedback and retention metrics. This approach ensured users kept returning to the platform, increasing engagement and strengthening loyalty.

By leveraging data-driven insights to enhance player experiences and retention, Hike has taken significant steps toward building a platform where players can earn, grow, and become active stakeholders in the network’s success, bringing to life its mission of turning gaming into a new source of economic opportunity.