Reimagining a data architecture to provide real-time data flow for sporting events can be complicated, especially for organizations with as much data as World Table Tennis (WTT). Vatsan Rama (Director of IT, ITTF Group) shares why real-time data is essential in the sporting world and how his team reengineered their data system in 18 months, moving from a solely on-premises infrastructure to a cloud-native data system that uses Confluent Cloud with Apache Kafka as its central nervous system.

Scaling Apache Kafka can be tricky, let alone scaling a team. When he was first hired, Domenico Fioravanti of Therapie Clinic was given the challenging task of assembling a sizable tech team from scratch, while simultaneously building a scalable and decoupled architecture from the ground up. In addition, he wanted to deliver value to the company from day one. One way that Domenico ultimately accomplished these goals was by focusing on managed solutions in order to avoid large investments in engineering know-how. Another way was to deliver quickly to production by using the existing knowledge of his team.

What is GraphQL? And how can you combine GraphQL with Apache Kafka to query data in real time? With over 10 years of experience as a backend engineer, Gerard Klijs is a Confluent Community Catalyst, a contributor to the GraphQL project, and also a creator and maintainer of a Rust library to use Confluent Schema Registry with Java client. In this episode, he explains why you want to use Kafka with GraphQL and how they work together to bridge the gap between backend and frontend to make data more easily accessible in the frontend.

In this episode, you’ll get a taste of how Apache Kafka is used at Honeycomb! Liz Fong-Jones (Principal Developer Advocate, Honeycomb) explains how Honeycomb manages Kafka-based telemetry ingestion pipelines and scales Kafka clusters. Honeycomb is an observability platform that helps you visualize, analyze, and improve cloud application quality and performance. Their data volume has grown by a factor of 10 throughout the pandemic, while the total cost of ownership has only gone up by 20%.

What does a ride-hailing app that offers micromobility and food delivery services have to do with data in motion? In this episode, Ruslan Gibaiev (Data Architect, Bolt) shares about Bolt’s road to adopting Apache Kafka and ksqlDB for stream processing to replicate data from transactional databases to analytical warehouses.