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Building Real-time Retail & Supply Chain IoT Platforms with Confluent Cloud

The level of credibility that Confluent has built in terms of modern, scalable and flexible stream processing does much of our blocking and tackling for us.

Gustavo Rivera | Senior Vice President, Software Development


Provide retailers with a solution to improve inventory accuracy, enable operational intelligence and support omnichannel sales.


Use Confluent Cloud and Kafka to build a scalable, highly available platform for real-time data streaming and complex event processing both on-premises and in the cloud.


  • Inventory accuracy increased from 65% to 99%
  • Expert training and support received
  • Fault-tolerant, scalable architecture
  • Focus on development sharpened

For many large retailers, there is a significant gap between the actual inventory in stores and warehouses and the digital records used to represent that inventory. Diminished inventory accuracy, which is often as low as 65%, makes it particularly difficult for retailers with a brick-and-mortar presence to pursue omnichannel strategies designed to improve the customer experience by enabling, for example, shoppers to order a product online and pick it up in the store. With its ViZixⓇ item chain management platform, Mojix is helping major retailers store, analyze and act on inventory data collected from IoT sensor streams in real time. Built using Confluent Cloud and Apache Kafka®, the ViZix platform supports real-time operational intelligence with complex event processing for a hybrid cloud across the edge – at retail stores and distribution centers – and the cloud.

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Nationwide Building Society

Nationwide Building Society is a British mutual financial institution, the seventh largest cooperative financial institution and the largest building society in the world with over 15 million members.


A team of engineers and developers at Nuuly designed, built and deployed a real-time event streaming architecture based on Confluent Cloud and Apache Kafka in just six months.