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When it comes to IoT data, MQTT is a superstar. With so many IoT devices generating data out in the world, developers need ways to access it. After all, data lies at the heart of every application. But data doesn’t just magically manifest itself into your datastore, and building the right data pipeline can make or break an application. Data collection is not a one-size-fits-all problem to solve.
You are not the only one asking this seemingly popular question! Several companies are torn between the rise in appeal of open-source databases and the undeniable challenges inherent to their adoption. Let’s explore the trends, the drivers and the challenges related to open-source database adoption.
Curious about Microsoft Azure and the best ways to connect? Azure is a hybrid Cloud Service Provider (CSP) with customized, scalable, cloud-based packages. These encompass Software as a Service (SaaS), based on subscription-based software licensing and delivery, Platform as a Service (PaaS), allowing companies to develop, deploy, manage, and update applications, and Infrastructure as a Service (IaaS), providing high-level application programming interfaces (APIs).
Kubeflow 1.6 was released on September 7, and Charmed Kubeflow 1.6 (Canonical’s distribution) came shortly after, as it follows the same roadmap. Charmed Kubeflow introduces a new version of Kubeflow pipelines as well as model training enhancements.
When talking about managing web applications and the services they provide, the term “API gateway” is often thrown around, but what does it mean? In order to unpack how a gateway functions, we first need to understand what an API is.
MQTT is a machine-to-machine communication protocol. Devices publish messages to a broker under specific topics, and other devices subscribe to those topics to receive information. It’s popular because it doesn’t take up a lot of bandwidth, so IoT devices with limited network connectivity can use it. MQTT works because of brokers. Each device sending and receiving data can communicate with potentially millions of other devices while only connecting to one broker.
When building cloud-based applications, managing the infrastructure becomes a bigger challenge as you scale. Kubernetes brings order to the chaos, letting you control and automate the containers used to deploy your application. Debugging in the cloud presents further challenges, and the complexities of distributed applications make it hard for many debugging setups to keep pace. Tools designed to run locally aren't effective. However, there are Kubernetes debugging tools that can handle the shift in paradigm. In this article, you'll read about several options that make debugging Kubernetes applications much easier.