Tag: Apache Kafka

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Top 10 Benefits of Apache Kafka

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What is Apache Kafka? Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system used to manage big volumes of data. Due to high efficiency, reliability and replication characteristics, Kafka is applicable for systems like tracking service calls (tracks every call), instant messaging or tracking IoT sensor data where a traditional technology might not be considered. Kafka can work with different frameworks for real-time ingesting, analysis, and processing of
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Kafka Streams

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Kafka Stream is a stream processing library that transforms data in real-time. It utilizes a Kafka cluster to its full capabilities by leveraging horizontal scalability, fault tolerance, and exactly-once semantics. It is a true stream processing engine that analyzes and transforms the data stored in Kafka topics record by record. Kafka streams are written as standard Java application that takes input from Kafka topics & processes the output in Kafka
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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 to allocate a production budget measured in billions of dollars. We’ve learned how Deutsche Bahn manages passenger data for over 5 million
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Real-Time Data Streaming, Kafka, and Analytics Part 1: Data Streaming 101

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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
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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 medium priority apps, we can keep it simple and less frequent. Before we get into details of Kafka-enabled event streaming,
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