This allows for lower-latency processing and easier support for multiple data sources and distributed data consumption. Kafka abstracts away the details of files and gives a cleaner abstraction of log or event data as a stream of messages.
Log aggregation typically collects physical log files off servers and puts them in a central place (a file server or HDFS perhaps) for processing. Many people use Kafka as a replacement for a log aggregation solution. This involves aggregating statistics from distributed applications to produce centralized feeds of operational data. Kafka is often used for operational monitoring data.
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Offline data warehousing systems for offline processing and reporting.Īctivity tracking is often very high volume as many activity messages are generated for each user page view. These feeds are available for subscription for a range of use cases including real-time processing, real-time monitoring, and loading into Hadoop or This means site activity (page views, searches, or other actions users may take) is published to central topics with one topic per activity type. The original use case for Kafka was to be able to rebuild a user activity tracking pipeline as a set of real-time publish-subscribe feeds. In this domain Kafka is comparable to traditional messaging systems such as ActiveMQ or In our experience messaging uses are often comparatively low-throughput, but may require low end-to-end latency and often depend on the strong Solution for large scale message processing applications.
In comparison to most messaging systems Kafka has better throughput, built-in partitioning, replication, and fault-tolerance which makes it a good Message brokers are used for a variety of reasons (to decouple processing from data producers, to buffer unprocessed messages, etc). Kafka works well as a replacement for a more traditional message broker. Here is a description of a few of the popular use cases for Apache Kafka®.įor an overview of a number of these areas in action, see this blog post.
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