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Real-time processing and aggregation of Call Detail Records (CDRs) using Kafka and Flink

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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

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Real-Time Small Business Intelligence with ksqlDB

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If you’re like me, you may be accustomed to reading articles about event streaming that are framed by large organizations and mountains of data. We’ve read about how the event streaming juggernaut Netflix uses Apache Kafka® to make sense of the viewing habits of 167 million subscribers, and understand how best

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Hybrid Cloud: The gateway to growth for businesses

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Hybrid cloud market has been growing significantly over the last five years because of flexible offering across private and public clouds. With businesses (from small to large scale, across all industries) moving more towards cloud deployments, security, protection of intellectual property, compliance and reliance on legacy infrastructure have become impedance

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The rise of event stream processing

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“The perfect kind of architecture decision is the one which never has to be made”  Robert C. Martin From being a phenomenal trend to becoming one of the most essential IT designs considerations, event streaming has evolved to be pivotal for businesses due to an exponential increase in data and

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Failure detection and alert from real-time data flow using Kafka and KSQL

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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,

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Event streaming using Apache Kafka

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There are levels of event recording in accordance with the use cases for which we are tracking like application logging (Log4j levels). Now a mission-critical application (like transactions) will generate a huge number of events, this will enable to debug/analyse in case of any failure or audits. For low to