kafka-go VS kubernetes

There are two main libraries that people use to write clients Confluent Kafka and segment io kafka

Unlike the standard scripts, and many binary clients, it's a native, statically-compiled, binary. It uses segmentio/go-kafka internally, which means CGO can be disabled during compilation.

Scaling up our producers/consumers proved to be more cost-effective than adding more CPU/brokers to the Kafka cluster. To accomplish this, we used the segmentio/kafka-go client library which provides an excellent abstraction for interacting with the cluster and will handle data compression completely transparently.

PostgeSQL as event store database Kafka as messages broker gRPC Go implementation of gRPC Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus MongoDB MongoDB database Elasticsearch Elasticsearch client for Go. Echo web framework Kibana Kibana is data visualization dashboard software for Elasticsearch Migrate for migrations

I want to announce the first release of a kafkagosaur, a new Kafka client. It's built using WebAssembly and binds to the kafka-go library. The first release includes functionality to read and write Kafka messages and SASL support.

Use the Segment Kafka library, not Sarama, it’s much easier to use https://github.com/segmentio/kafka-go

Kafaka - kafka-go, Debezium Outbox

Segment learned quite some time ago that librdkafka-go has problems like these (and doesn’t support Contexts either), so they wrote a pure Go replacement instead. https://github.com/segmentio/kafka-go

In this article let's try to create closer to real world CQRS microservices with tracing and monitoring using: 🚀 Kafka as messages broker gRPC Go implementation of gRPC PostgreSQL as database Jaeger open source, end-to-end distributed tracing Prometheus monitoring and alerting Grafana for to compose observability dashboards with everything from Prometheus MongoDB Web and API based SMTP testing Redis Type-safe Redis client for Golang swag Swagger for Go Echo web framework

In the field of software development, efficiency and agility are always sought after. In the era of cloud-native apps, traditional deployment techniques—which are frequently laborious and prone to errors—are starting to become obstacles. This is when Kubernetes and GitOps come in handy.

Are you looking for a hassle-free way to deploy Moodle™ Learning Management Systems (LMS) on Kubernetes? Look no further! Krestomatio presents the LMS Moodle Operator, an open-source Kubernetes Operator designed to simplify the deployment and management of Moodle instances on Kubernetes clusters. Let's dive into what makes this tool a great choice for Moodle administrators and developers alike.

Securing access to your Kubernetes clusters is crucial as inadequate security measures can lead to unauthorized access and potential data breaches. However, navigating the complexities of Kubernetes access security, especially when setting up strong authentication, authorization, and network policies, can be challenging.

Deploying AI models into production requires tools that can package applications and manage them at scale. Docker simplifies the deployment of AI applications by containerizing them, ensuring that the application runs smoothly in any environment. Kubernetes, an orchestration system for Docker containers, allows for the automated deployment, scaling, and management of containerized applications, essential for AI applications that need to scale across multiple servers or cloud environments.

To learn more, you can start by exploring the official Kubernetes documentation.

With the containerized Node.js/Express API, I could run multiple containers, scaling to handle more traffic. Using a tool called minikube, we can easily spin up a local Kubernetes cluster to horizontally scale Docker containers. It was possible to keep one shared instance of the database, and many APIs were routed with an internal Kubernetes load balancer.

This package is widely used for powerful CLI builds, it is used for example for Kubernetes CLI and GitHub CLI, in addition to offering some cool features such as automatic completion of shell, automatic recognition of flags (the tags) , and you can use -h or -help for example, among other facilities.

We closely monitor Kubernetes and cloud providers' updates by following official changelogsand using RSS feeds, allowing us to anticipate potential issues and adapt our infrastructure proactively.

"You are holding it wrong", huh?

From the homepage https://kubernetes.io/:

"Kubernetes, also known as K8s, is an open-source system for automating deployment, scaling, and management of containerized applications."

Do you see "not recommended for smaller-scale applications" anywhere? Including on the entire home page? Looking for "small", "big" and "large" also yields nothing.