![]() In comparison to log-centric systems like Scribe or Flume, Kafka offers equally good performance, stronger durability guarantees due to replication, 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. 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. Since channel strip performances can be changed with your MIDI controller keyboard, this also lets you switch performances during playback by simply pressing a key.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. Once you save your channel strip performances, you’ll see them in that same Setting popup menu. A dialogue box will pop up where you can name the program and give it a program change number. If you save your channel strip settings as a performance, you can give it a program change number, which can then be triggered by your MIDI controller keyboard as a program change, which is useful for live performance applications or having more creative control over your instrument tracks.įor example, if you wanted to trigger a snare track with a particular channel setting as part of a live backing track, click on Setting in the Channel Strip view and then choose Save As Performance. It will not change any of your routing or panning. If you click on one of those settings to load it, all the previous effects will be applied to the current channel. Below that, you’ll see any previously saved settings. ![]() In the Channel Strip view on the left, if you click on Setting, you’ll see an option to save your channel strip settings, among others. ![]()
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