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Guillem Solé & Àngel Vilardell – Kafka and ksqlDB at The Hotels Network
Discover how The Hotels Network leveraged Kafka and ksqlDB to transform their data processing, achieving 25-30% faster query times, real-time analysis, and scalable insights.
- KSQL significantly improved the performance of The Hotels Network’s data processing, reducing query times by 25-30%.
- The company moved from a batch processing model to a streaming model using Kafka and ksqlDB, which allowed for real-time data processing and analysis.
- The use of ksqlDB enabled easier querying and analysis of large datasets, with a single SQL query able to process millions of events.
- TinyVid was chosen for its ability to materialize states and perform complex aggregations, such as counting visits to a website.
- Red Panda was used to connect to and query the Kafka topic, allowing for efficient extraction of data.
- The company had to add ACLs to control access to the TinyVid cluster, as well as ensure that the cluster was properly secured.
- The use of Kafka Streams was considered, but the company chose ksqlDB for its ease of use and performance.
- The company faced challenges with memory and knowledge of the technology, but was able to overcome these issues with the help of Red Panda.
- The company’s data processing pipeline is now more efficient, with fewer issues and faster query times.
- The use of ksqlDB and TinyVid has enabled the company to process large datasets in real-time, providing more accurate and timely insights.
- The company’s data engineers were able to learn and adapt to the new technology, resolving issues and optimizing the pipeline.
- The company’s data processing pipeline is now more scalable, with the ability to handle large amounts of data and multiple concurrent queries.
- The use of ksqlDB and TinyVid has enabled the company to integrate with other tools and technologies, such as Datadog and ClickHouse.