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This article expands on the most commonly used RabbitMQ use cases, from microservices to real-time notifications and IoT. Key Takeaways RabbitMQ is a versatile message broker that improves communication across various applications, including microservices, background jobs, and IoT devices.
ENEL is using AWS to transform its entire business, closing all of their data centers by 2018, migrating workloads from over 6,000 on-premises servers onto AWS in nine months, and using AWS IoT services to better manage and understand energy consumption. In 2013, we launched a dedicated program called AWS Activate.
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.
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.
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.
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.
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