Kafka

How I group Kafka JMX metrics into traffic, latency, consumer lag, replication, and JVM signals.

My first practical pass at Kafka JMX: enabling remote access safely and finding the measurements that explain cluster behavior.

Broker-level controls for a safer Kafka deployment, from service accounts and filesystems to monitoring and recovery.

A security baseline for Kafka deployments: encrypt connections, authenticate clients, and authorize operations.

A practical introduction to how Kafka distributes records across brokers and why partition planning matters.

A simple mental model for ZooKeeper, znodes, sessions, and why Kafka historically used it for cluster coordination.

In today’s data-driven world, the ability to process information in real-time is crucial.

In Apache Kafka, trust store and keystore are essential components for ensuring secure communication between brokers, clients, and other services.

I was struggling with the mTLS configuration for zookeeper configuration and somehow no matter what I do I kept getting an error as below

Creating a Topic in a kafka-cluster is easy and well documented for the kafka-topics.sh or even the official API documentation.

To enable and configure monitoring of Java VM for any java-application using the ready-to-use JMX agent, you must set certain system properties when you start the Java VM.

While I continue to learn more about architecture and design of kafka, a quickstart to deploy & test kafka is a must. Step 1: Add Chart Repo # In my local system it is already present which was confirmed by the listing

While reading Apache Kafka it was interesting analogy that they described Event streaming is the digital equivalent of the human body’s central nervous system. — Apache Kafka It draws a great parallel between how the central nervous system allows for real time event generations and consumption in terms of reflex actions. The examples are well explained and captured here.