Webhooks, those wonderful little lifelines connecting one application to another, have become an essential part of our app notification world. They help keep your systems in the loop, notifying them immediately when events of interest occur. This real-time communication ensures that your applications remain responsive, adaptive, and always up-to-date with the latest information.

In our journeys as developers, we frequently encounter the need for speed and efficiency. But often, integrating development tools can feel like a time-consuming venture, more so than our usual build processes. If you’ve ever found yourself delving into java logs looking for needles in logstacks, you’ll appreciate the beauty of this 1-click OpenTelemetry.

This is the third blog in our series on Kafka, where we continue to explore the nuances of deploying Kafka for scale. In our previous blogs, Essential Metrics for Kafka Performance Monitoring and Auto-Instrumenting OpenTelemetry for Kafka, we laid the foundation for understanding Kafka’s performance and monitoring aspects. Now, as we explore further into the Kafka ecosystem, we’re here to tackle the common challenges that can arise during deployment and scaling.

Apache Kafka is an open-source distributed streaming system that has grown in popularity and usage across the technology industry. Originating from LinkedIn and now part of the Apache Software Foundation, Kafka provides a robust and scalable platform. It’s uniquely designed with an architecture that includes both a storage layer and a compute layer.

Apache Kafka, born at LinkedIn in 2010, has revolutionized real-time data streaming and has become a staple in many enterprise architectures. As it facilitates seamless processing of vast data volumes in distributed ecosystems, the importance of visibility into its operations has risen substantially. In this blog, we’re setting our sights on the step-by-step deployment of a containerized Kafka cluster, accompanied by a Python application to validate its functionality. The cherry on top?

It’s been only a few days since the Bun 1.0 announcement and it’s taken social media by storm! And rightly so. Bun promises better performance, and Node.js compatibility and comes with batteries included. It comes with a transpiler, bundler, package manager and testing library. You no longer have to install 15 packages before writing a single code line. It creates a standardised set of tools and addresses the fractured nature of the Node.js ecosystem.

As Kubernetes adoption surges across the industry, AWS EKS stands out as a robust solution that eases the journey from initial setup to efficient scaling. This fully managed Kubernetes service is revolutionizing how businesses handle containerized applications, offering agility, scalability, and resilience.

RabbitMQ is a popular open-source message broker that facilitates communication between different components of a distributed system. Monitoring a RabbitMQ instance is crucial to ensure its health, performance, and reliability. Monitoring allows you to identify and address potential issues before they escalate, ensuring smooth communication between various parts of your application.

Kubernetes v1.28 comes with multiple new enhancements this year and we’ve already covered an overview of those in our previous blog, Do check this out before diving into sidecar containers. We’re going to completely focus on the new sidecar feature for this post, which enables restartable init containers and is available in alpha in Kubernetes 1.28.

RabbitMQ is a household name in the world of application development and system architecture. Acting as a middleman for communication, it seamlessly bridges the gap between various application components. If you’ve been contemplating the integration of RabbitMQ into your infrastructure or simply want to better understand its functionalities, this blog post is for you. Here are the top 8 things to know.