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Wondering where RabbitMQ fits into your architecture? This article expands on the most commonly used RabbitMQ use cases, from microservices to real-time notifications and IoT. RabbitMQ in IoT Applications The Internet of Things (IoT) ecosystem depends greatly on effective inter-device communication.
The population of intelligent IoT devices is exploding, and they are generating more telemetry than ever. The Microsoft Azure IoT ecosystem offers a rich set of capabilities for processing IoT telemetry, from its arrival in the cloud through its storage in databases and data lakes.
Digital twins are software abstractions that track the behavior of individual devices in IoT applications. Because real-world IoT applications can track thousands of devices or other entities (e.g., The digital twin model is worth a close look when designing the next generation of IoT applications.
Digital twins are software abstractions that track the behavior of individual devices in IoT applications. Because real-world IoT applications can track thousands of devices or other entities (e.g., The digital twin model is worth a close look when designing the next generation of IoT applications.
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Going back to the mid-1990s, online systems have seen relentless, explosive growth in usage, driven by ecommerce, mobile applications, and more recently, IoT. For ecommerce applications, this evolution has created new capabilities that dramatically improve the experience for online shoppers.
Going back to the mid-1990s, online systems have seen relentless, explosive growth in usage, driven by ecommerce, mobile applications, and more recently, IoT. For ecommerce applications, this evolution has created new capabilities that dramatically improve the experience for online shoppers.
Examples include tracking a fleet of trucks, analyzing large numbers of banking transactions for potential fraud, managing logistics in the delivery of supplies after a disaster or during a pandemic, recommending products to ecommerce shoppers, and much more.
Examples include tracking a fleet of trucks, analyzing large numbers of banking transactions for potential fraud, managing logistics in the delivery of supplies after a disaster or during a pandemic, recommending products to ecommerce shoppers, and much more.
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Whether it’s ecommerce shopping carts, financial trading data, IoT telemetry, or airline reservations, these data sets need fast, reliable access for large, mission-critical workloads. For more than a decade, in-memory data grids (IMDGs) have proven their usefulness for storing fast-changing data in enterprise applications.
The reality is that many traditional BI solutions are built on top of legacy desktop and on-premises architectures that are decades old. While BI solutions have existed for decades, customers have told us that it takes an enormous amount of time, IT effort, and money to bridge this gap.
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