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Case Study: How Groupon scales personalised offers to 48 Million customers on time

Use Case Retain market leadership with personalised, time-sensitive offerings to a user base that includes more than 48 million active customers. Challenge With evidence showing that delays in outreach impact conversions, Groupon must ensure campaign delivery of millions of daily emails and push notifications to users in 500+ markets across 15 countries. Maintain a resilient and responsive user experience, especially when scaling out to handle large peaks in demand–2-3 hours
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Case Study: Flipkart reduces infrastructure by 75% and “Connekts” with 70 million shoppers using reactive microservices

Introduction Flipkart first opened for business selling books in 2007, and has grown rapidly since then to now employing over 33,000 people serving a marketplace of over 100 million consumers. Now with a market valuation of over $11 billion and over $2 billion in revenues, Flipkart has become a significant market power in India’s massive e-commerce industry–offering everything from books and electronics to furniture and washing machines. One of the untold
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Real-time processing and aggregation of Call Detail Records (CDRs) using Kafka and Flink

With the exponential growth in data, the biggest problem plaguing the telecom industry is how to process and aggregate huge volumes of call detail records (CDRs). Telcos are taking  fresh approaches to get the real-time value of streaming data. They are shifting from heavy batch processing systems to faster, scalable and resilient IT architectures. However, integrating devices and machines to enable real-time data processing is complex and time-consuming. Kafka ecosystem,
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Stateful Cloud Native Applications – Why Reactive Matters

What is a Cloud Native Application? A Cloud Native application is an application designed to leverage the cloud operating model. They are predictable, decoupled from the infrastructure, right-sized for capacity, enable a tight collaboration between development and operations, decomposed into loosely coupled independently operating microservices, resilient from failures, driven by data, and intelligently operate across geographic nodes. It’s not uncommon to see an application demand increasingly tougher requirements: High transactions per second
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It’s a Great Time to Transition to Microservices Architecture

Microservices architecture has graduated from a new concept to one that’s well understood, and microservices-specific tools are now available to support development. Microservices architecture adoption has moved beyond use in the largest of cloud enterprises to applications of all sizes. Whether you are an early adopter – or you’re just now weighing your options — Kyle Davis, Head of Developer Advocacy, and Loris Cro, Developer Advocacy Manager at Redis Labs, authors
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Failure detection and alert from real-time data flow using Kafka and KSQL

Apache Kafka ecosystem is relevant to detect anomalies (failure detection) in the huge volume of data sets. These data sets can come from various industries including IoT, banking or insurance. In this article, we present a use case on how to use the cloud-native ecosystem with Kafka, MQTT, Kafka Connect, Stream, KSQL to build scalable and resilient solutions for real-time IoT anomalies detection. Processing huge volumes of data for anomaly