Modernizing Inventory Management Technology ft. Sina Sojoodi and Rohit Kelapure

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 LINKSHow to Run Kafka Streams on Kubernetes ft. Viktor GamovMachine Learning with Kafka Streams, Kafka Connect, and ksqlDB ft. Kai WaehnerUnderstand What’s Flying Above You with Kafka Streams ft. Neil BuesingJoin the Confluent Community SlackLearn more with Kafka tutorials, resources, and guides at Confluent DeveloperUse 60PDCAST to get an additional $60 of free Confluent Cloud usage*SEASON 2 Hosted by Tim Berglund, Adi Polak and Viktor Gamov Produced and Edited by Noelle Gallagher, Peter Furia and Nurie Mohamed Music by Coastal Kites Artwork by Phil Vo 🎧 Subscribe to Confluent Developer wherever you listen to podcasts. ▶️ Subscribe on YouTube, and hit the 🔔 to catch new episodes. 👍 If you enjoyed this, please leave us a rating. 🎧 Confluent also has a podcast for tech leaders: "Life Is But A Stream" hosted by our friend, Joseph Morais.

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