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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
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3 reasons your company needs real-time anomaly detection

In the business world, time is money. When an anomaly, or unexpected data pattern, occurs, each second can amount to additional loss or missed opportunities. This is especially true for a fast-moving, data-driven business. When manual thresholds and dashboards prove no match at managing millions of metrics, companies need to adopt an analytics solution that works autonomously and in real-time. Here’s why. 1. Opportunity As author William Arthur Ward famously
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Real-Time Data Streaming, Kafka and Analytics, Part 2: Going Beyond Pure Streaming

Data transaction streaming is managed through many platforms, with one of the most common being Apache Kafka. In our first article in this data streaming series, we delved into the definition of data transaction and streaming and why it is critical to manage information in real-time for the most accurate analytics. As more individuals increase their data literacy and use data to make business decisions, real-time data is becoming a
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Real-Time Data Streaming, Kafka, and Analytics Part 1: Data Streaming 101

The terms “real-time data” and “streaming data” are the latest catch phrases being bandied about by almost every data vendor and company. Everyone wants the world to know that they have access to and are using the latest, greatest data for making business decisions. But using the term is not enough. Do your business executives and users know what data streaming is, where the information comes from and why it