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ClickHouse > Case Studies > Leveraging ClickHouse Kafka Engine for Enhanced Data Collection and Analysis: A Case Study of Superology
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Leveraging ClickHouse Kafka Engine for Enhanced Data Collection and Analysis: A Case Study of Superology

Technology Category
  • Analytics & Modeling - Machine Learning
  • Application Infrastructure & Middleware - Event-Driven Application
Applicable Industries
  • Buildings
  • Construction & Infrastructure
Applicable Functions
  • Quality Assurance
Use Cases
  • Experimentation Automation
  • Time Sensitive Networking
Services
  • System Integration
  • Testing & Certification
The Challenge
Superology, a leading product tech company in the sports betting industry, was faced with the challenge of effectively collecting and analyzing quantitative data to improve customer experience and business operations. The company needed to gather metrics such as app or site visits, customer clicks on specific pages, number of comments and followers in their social section, and various conversion events and bounce rates. The data collected varied in structure, requiring a dynamic approach to data collection and analysis. Superology was using Google Protocol Buffers (Protobuf) to collect this data, but needed a more efficient and scalable solution to handle the large volume of data and its dynamic nature.
About The Customer
Superology is a seasoned product tech company that has been innovating in the sports betting industry since 2012. Acquired by Superbet group in 2017, it has become a leading force in the industry, with its platforms being used by hundreds of thousands of people and processing millions of transactions daily. Superology uses a data-informed approach at every level of work to satisfy user needs and accomplish business goals. The company values personal growth as much as company growth, and empowers its people to deploy their talents and own their work end-to-end.
The Solution
Superology adopted the ClickHouse Kafka Engine and Protocol Buffers to enhance their data collection and analysis process. ClickHouse, with its built-in Kafka connector and Protobuf input type, provided a fast and reliable solution. The company was able to easily scale their ClickHouse implementation both horizontally and vertically. The data collected was ingested into a 'big' origin table, with the ClickHouse columnar structure offering great extensibility. Superology also implemented a system to allow changes to their proto scheme, ensuring backward compatibility. The data was then filtered and transformed using a materialized view, enabling a focused analysis of specific aspects of customer behavior. ClickHouse also facilitated efficient AB testing and other experiments, with its extensive set of statistical functions. Superology plans to further enrich their ClickHouse architecture by coupling it with MindsDB, to create a Machine learning architecture at the database level.
Operational Impact
  • The implementation of ClickHouse Kafka Engine and Protocol Buffers has significantly improved Superology's data collection and analysis process. The solution has enabled the company to handle the dynamic nature of their data more effectively, ensuring that only relevant data is analyzed. This has led to more accurate insights into customer behavior, enabling the company to improve the customer experience. The solution has also facilitated more efficient AB testing and other experiments, enhancing the company's decision-making process. Looking ahead, Superology plans to further leverage ClickHouse by integrating it with MindsDB, to create a Machine learning architecture at the database level, and push ClickHouse transformations back to Kafka streams for enriched data streams.
Quantitative Benefit
  • Efficient handling of large volumes of data, with millions of transactions processed daily.
  • Improved customer experience through focused analysis of specific aspects of customer behavior.
  • Enhanced business decision-making through efficient AB testing and other experiments.

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