Annalise  Hyatt

Annalise Hyatt


An Introduction to Apache Kafka

Apache Kafka is a distributed event streaming platform. It provides a unified, high-throughput, highly scalable, fault-tolerant, low-latency platform for handling real-time data feeds. Kafka combines three key capabilities for end-to-end event streaming with a single battle-tested solution:

  1. To publish (write) and subscribe to (read) streams of events, including continuous import/export of your data from other systems.
  2. To store streams of events durably and reliably for as long as you want.
  3. To process streams of events as they occur or retrospectively.

Fun Fact: Kafka can be deployed on bare-metal hardware, virtual machines and containers, and on-premises as well as in the cloud. You can choose between self-managing your Kafka environments and using fully managed services offered by a variety of vendors.

This article is divided into 2 sections:-

  1. Introduction
  2. Working

1. Introduction

Kafka comprises of servers and clients that communicate via a high-performance TCP network protocol.


Kafka is run as a cluster of one or more servers that can span multiple data centers or cloud regions. Some of those servers form the storage layer, called the brokers. Other servers run Kafka Connect to continuously import and export data as event streams. Kafka maintains replicas of brokers which ensures continuous operations without any data loss.

Kafka Connect_ is a framework for connecting Kafka with external systems such as databases, key-value stores, search indexes and file systems. Its main motive is to stream data to and from Kafka._


Clients subscribe to the Kafka stream. You can configure your Kafka client to get parallel and batch reading. Clients send an acknowledgment to servers which in hand increase the offset.


Events are pushed to topics. Topics in Kafka are multi-producer and multi-subscriber in nature. Events in a topic can be read as often as needed as events are not deleted after consumption. Instead, you define for how long Kafka should retain your events through a per-topic configuration setting, after which old events will be discarded.

Topics can be partitioned over multiple brokers to provide a distributed platform. This means client applications can both read and write data from/to many brokers at the same time. When an event is published to a topic, it is appended to one of the topic’s partitions. **Note: **Event with the same event key is written to the same partition. Kafka guarantees that the events will be consumed in the same order as it was pushed to the topic.

Representation of publishing Kafka event to a single topic having multiple partitions present in different brokers.

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An Introduction to Apache Kafka
Carmen  Grimes

Carmen Grimes


Introduction To Apache Kafka

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.

#big-data #devops #kafka #apache-kafka #apache

Apache Kafka Tutorial - Kafka Tutorial for Beginners

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:

  1. Need of Messaging System
  2. What is Kafka?
  3. Kafka Features
  4. Kafka Components
  5. Kafka architecture
  6. Installing Kafka
  7. Working with Single Node Single Broker Cluster

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.

#apache #kafka #web-development #apache-kafka

Carmen  Grimes

Carmen Grimes


Using Apache Kafka with .NET

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.

How It Works

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.

Apache Kafka

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.

Kafka clusters

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.

#dotnet #kafka #apache #apache-kafka #developer

Learn Apache Kafka in 5 minutes

Learn the principles of Apache Kafka and how it works through easy examples and diagrams!

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Chaos Engineering with Apache Kafka and Gremlin | The most secure clusters aren’t built on the hopes that they’ll never break. They are the clusters that are broken on purpose and with a specific goal. When organizations want to avoid systematic weaknesses, chaos engineering with Apache Kafka® is the route to go.

Your system is only as reliable as its highest point of vulnerability. Patrick Brennan (Principal Architect) and Tammy Butow (Principal SRE) from Gremlin discuss how they do their own chaos engineering to manage and resolve high-severity incidents across the company. But why would an engineer break things when they would have to fix them? Brennan explains that finding weaknesses in the cloud environment helps Gremlin to:
► Avoid lengthy downtime when there is an issue (not if, but when)
► Halt lost revenue that results from service interruptions
► Maintain customer satisfaction with their stream processing services
► Steer clear of burnout for the SRE team

Chaos engineering is all about experimenting with injecting failure directly into the clusters on the cloud. The key is to start with a small blast radius and then scale as needed. It is critical that SREs have a plan for failure and then practice an intense communication methodology with the development team. This plan has to be detailed and includes precise diagramming so that nothing in the chaos engineering process is an anomaly. Once the process is confirmed, SREs can automate it, and nothing about it is random.

When something breaks or you find a vulnerability, it only helps the overall network become stronger. This becomes a way to problem-solve across engineering teams collaboratively. Chaos engineering makes it easier for SRE and development teams to do their job, and it helps the organization promote security and reliability to their customers. With Kafka, companies don’t have to wait for an issue to happen. They can make their disorder within microservices on the cloud and fix vulnerabilities before anything catastrophic happens.

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