![]() In our case, we did it with multiple disks using RAID configurations. Up until the latest versions of Kafka, partitions could not be split between multiple brokers and needed to point to a single location of storage in a broker, which could be a single mount point. It’s important to understand the underlying physical space and configurations in order to increase partitions. While adding partitions is a step in the right direction, understanding the underlying hardware, filesystem type, available storage, memory, and more, is also essential. Kafka extends scalability through its partitions. Today, the ability to scale is a must-have feature of any event streaming architecture, or batch architecture for that matter. The canonical data would then get streamed further down the system to become part of the debit/credit system, ultimately leading to the creation of an inventory state for an item store. To do so, we developed a smart transformation engine, which would read input data from various source topics and convert it into a common inventory. ![]() The responsibility to read and convert data into a common inventory was the duty of our central system. With more than 10 sources of event streaming data, and one or more events likely derived from inventory data, this meant we needed a few sets of input data, such as item, store, quantity, and type of event. The primary goal was to accelerate delivery and simplify integration. So, the team elected to implement a canonical approach to streamline data sources. With Walmart’s inventory architecture, it was near impossible to mandate source teams to send events to the same schema. Furthermore, like any supply chain network, our infrastructure involved a plethora of event sources with all different types of data contributing to net change to inventory positions, so we leveraged Kafka Streams to house the data and a Kafka connector to take the data and ingest it into Apache Cassandra and other data stores. We at Walmart have solved this at scale by designing an event-streaming-based, real-time inventory system leveraging Apache Kafka ®. Having an up to date snapshot of inventory position on every item is a very important aspect to deal with these challenges. Retail shopping experiences have evolved to include multiple channels, both online and offline, and have added to a unique set of challenges in this digital era. Shopping in a physical store is no longer the only way. ![]() Consumer shopping patterns have changed drastically in the last few years.
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