As autumn graced the vibrant city of Chicago, I had the distinct opportunity to immerse myself in the heart of innovation and camaraderie at the CNCF’s Kubecon North America conference. Over the span of four remarkable days, from Nov 6-9, I was fortunate enough to walk alongside the many enthusiasts, contributors and organizers of open source and cloud native communities.
As a developer, when I first stepped into the wonderful world Kubernetes, I was amazed at the endless configuration possibilities it offered for containerized apps. It felt like I was handed the most multifaceted tool for all my deployment needs.
As a developer I love automation, Whether it’s orchestrating a smart home or optimizing developer toolchains. Automation injects efficiency into my daily routine, simplifying intricate processes and eliminating repetitive tasks. Especially when it comes to toolchains, I’m constantly on the lookout for ways to boost coding workflows. Cloud Development Kits (CDKs) brought with them a new era of streamlined developer toolchains.
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.
