Apache Kafka is an open-source, publish/subscribe (pub/sub) messaging system, also very often described as a distributed event log where all the new records are immutable and appended to the end of the log.
Kafka aims to provide a reliable and high-throughput platform for handling real-time data streams and building data pipelines. It also provides a single place for storing and distributing events that can be fed into multiple downstream systems which helps to fight the ever-growing problem of integration complexity. Besides all of that Kafka can also be easily used to build a modern and scalable ETL, CDC or big data ingest systems.
Kafka is used across multiple industries, from companies like Twitter and Netflix to Goldman Sachs and Paypal. It was originally developed by Linkedin and open sourced in 2011.
https://cnfl.io/apache-kafka-2-7 | Apache Kafka 2.7 is here and with it comes a new batch of Kafka Core, Kafka Connect, and Kafka Streams updates. In this video, Tim Berglund breaks down the seven Kafka Improvement Proposals (KIPs) that will add substantial updates to Apache Kafka®, including adding a new inter-broker API in relation to ZooKeeper removal, throttle create topics, support for PEM format, sliding windows, and end-to-end latency metrics. Make sure to check out the release notes and blog post for more information, and let’s get to building.
Release notes: https://dist.apache.org/repos/dist/release/kafka/2.7.0/RELEASE_NOTES.html
Audio-only version: https://developer.confluent.io/podcast/apache-kafka-27-overview-of-latest-features-updates-and-kips
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This Apache Kafka Tutorial - Kafka Tutorial for Beginners will help you understand what is Apache Kafka & its features. It covers different components of Apache Kafka & it’s architecture. So, the topics which we will be discussing in this Apache Kafka Tutorial are:
Why Learn Apache Kafka?
Kafka training helps you gain expertise in Kafka Architecture, Installation, Configuration, Performance Tuning, Kafka Client APIs like Producer, Consumer and Stream APIs, Kafka Administration, Kafka Connect API and Kafka Integration with Hadoop, Storm and Spark using Twitter Streaming use case.
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What is Apache Kafka?
Kafka is a Publish-Subscribe based messaging system that is exchanging data between processes, applications, and servers. Applications may connect to this system and transfer a message onto the Topic(we will see in a moment what topic is) and another application may connect to the system and process messages from the Topic.
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Diogo Souza explains using Apache Kafka with .NET including setting it up and creating apps to test sending messages asynchronously.
Have you ever used async processing for your applications? Whether for a web-based or a cloud-driven approach, asynchronous code seems inevitable when dealing with tasks that do not need to process immediately. Apache Kafka is one of the most used and robust open-source event streaming platforms out there. Many companies and developers take advantage of its power to create high-performance async processes along with streaming for analytics purposes, data integration for microservices, and great monitoring tools for app health metrics. This article explains the details of using Kafka with .NET applications. It also shows the installation and usage on a Windows OS and its configuration for an ASP.NET API.
The world produces data constantly and exponentially. To embrace such an ever-growing amount of data, tools like Kafka come into existence, providing robust and impressive architecture.
But how does Kafka work behind the scenes?
Kafka works as a middleman exchanging information from producers to consumers. They are the two main actors in each edge of this linear process.
Figure 1. Producers and consumers in Kafka
Kafka can also be configured to work in a cluster of one or more servers. Those servers are called Kafka brokers. You can benefit from multiple features such as data replication, fault tolerance, and high availability with brokers.
Figure 2. Kafka clusters
These brokers are managed by another tool called Zookeeper. In summary, it is a service that aims to keep configuration-like data synchronized and organized in distributed systems.
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