AppsFlyer: Leveraging Real-Time Data for Mobile App Marketing Analytics
- Platform as a Service (PaaS) - Application Development Platforms
- Robots - Wheeled Robots
- Buildings
- Telecommunications
- Product Research & Development
- Sales & Marketing
- Real-Time Location System (RTLS)
- Track & Trace of Assets
- Cloud Planning, Design & Implementation Services
Founded in 2011, AppsFlyer is a leading mobile attribution and marketing analytics platform. It is used by top B2C brands worldwide to measure the effectiveness of their marketing campaigns, as well as the adoption and usage of their mobile apps. The platform serves more than 100,000 users and handles traffic of more than 80 billion events a day. AppsFlyer provides its customers with the ability to retrieve raw datasets from the platform in real time, which informs their business decisions. The company is trusted by the world's leading B2C companies for the precision and availability of the data it provides.
AppsFlyer, a leading mobile attribution and marketing analytics platform, was faced with the challenge of providing real-time access to raw datasets for over 100,000 users. The platform, which processes more than 80 billion events daily, was experiencing a 10% monthly growth in traffic. This rapid growth necessitated a service that could scale to match the exceptional demand. Additionally, AppsFlyer's internal teams required access to the raw data for analytics to inform various aspects such as product performance and research and development. The company needed a data warehouse that could scale quickly while providing real-time access to data with high availability.
To address these challenges, AppsFlyer deployed BigQuery as a data lake. BigQuery was able to handle the volume and scale required by AppsFlyer, storing and serving more than 65 billion new events daily. The tool enabled AppsFlyer's analysts to query the petabytes of data collected, which was crucial for testing new ideas and driving innovation in the platform. Beyond BigQuery, AppsFlyer also started exploring other Google Cloud managed services. The company began experimenting with Cloud Dataflow and Cloud Dataproc to free up time and resources for developing new features and optimizing the platform.
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