How Rapyd solved observability gaps to cut Kubernetes costs by 40%
Learn how Rapyd overcame observability limits with PerfectScale by DoiT to cut Kubernetes costs and boost app stability.
Recognized as a Top 10 Mobile Games Publisher by App Annie, Crazy Labs apps have been downloaded over 2 billion times and repeatedly reach top ranks in the app stores. In the last 8 years, Crazy Labs has established itself as a leading mobile game developer with operations in 7 countries.
Crazy Labs’ marketing team needed a solution to effectively collect, securely store and conveniently analyze these digital marketing metrics so the team can make better decisions on how traffic acquisition and how the marketing budgets would be spent most effectively.
Like many Amazon Web Services (AWS) customers, Crazy Labs uses streaming data to gain real-time insight into user’s activity and business trends. Streaming data, which is generated continuously from millions of data sources such as Crazy Labs’ mobile or like in-game player activity, enrichment information from social networks, and telemetry from backend servers. This data helps Crazy Labs’ to make well-informed decisions and proactively respond to changing business conditions.
Amazon Kinesis, a platform for streaming data on AWS, offers powerful services that make it easier to build data processing applications, load massive volumes of streaming data, and analyze it in real time.
Crazy Labs’ solution uses Amazon Kinesis Streams and Amazon Kinesis Firehose to load streaming data into Amazon Kinesis Analytics. An Amazon Kinesis Analytics application filters and pre-aggregates the data, and puts it into Amazon Kinesis Firehose. Then, an Amazon Kinesis Firehose delivery stream delivers the analyzed data to various data stores for search, storage, or further analytics.
Crazy Labs is putting pre-aggregated data into an Amazon S3 bucket and an Amazon Redshift cluster as well as another Amazon Kinesis stream to be reused in other data pipelines. Finally, Crazy Labs are using Amazon Athena to interactively analyze the collected data stored in Amazon S3 bucket.
Improvement achieved as a result of this project is estimated at 15% which is equivalent to $700,000 of additional profit contribution per annum. The ROI of the project was achieved in under just first three months after the deployment.
Learn how Rapyd overcame observability limits with PerfectScale by DoiT to cut Kubernetes costs and boost app stability.
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