Inventory management systems are crucial for reducing real-time inventory data drift, improving customer experience, and minimizing out-of-stock events. Apache Kafka®’s real-time data technology provides seamless inventory tracking at scale, saving billions of dollars in the supply chain, making modernized data architectures more important to retailers now more than ever.  

In this episode, we’ll discuss how Apache Kafka allows the implementation of stateful event streaming architectures on a cloud-native platform for application and architecture modernization. 

Sina Sojoodi (Global CTO, Data and Architecture, VMware) and Rohit Kelapure (Principal Advisor, VMware) will discuss data modeling, as well as the architecture design needed to achieve data consistency and correctness while handling the scale and resilience needs of a major retailer in near real time. 

The implemented solution utilizes Spring Boot, Kafka Streams, and Apache Cassandra, and they explain the process of using several services to write to Cassandra instead of trying to use Kafka as a distributed log for enforcing consistency.  

EPISODE LINKS

• How to Run Kafka Streams on Kubernetes ft. Viktor Gamov
• Machine Learning with Kafka Streams, Kafka Connect, and ksqlDB ft. Kai Waehner
• Understand What’s Flying Above You with Kafka Streams ft. Neil Buesing
• Join the Confluent Community Slack
• Learn more with Kafka tutorials, resources, and guides at Confluent Developer
• Use 60PDCAST to get an additional $60 of free Confluent Cloud usage*