Systems | Development | Analytics | API | Testing

Building Smart O11y for Kuma With Elastic Observability

This blog was co-created by Ricardo Ferreira (Elastic) and Viktor Gamov (Kong). We love our microservices, but without a proper observability (O11y) strategy, they can quickly become cold, dark places cluttered with broken or unknown features. O11y is one of those technologies deemed created by causation: the only reason it exists is that other technologies pushed for it. There wouldn’t be need for O11y if, for example, our technologies haven’t gotten so complex across the years.

Using Elastic ML to Observe Your Kuma API Observability Metrics

Observability is catching on these days as the de-facto way to provide visibility into essential aspects of systems. It would be unwise for you not to leverage it with Kuma service mesh — the place that allows your services to communicate with the rest of the world. However, many observability solutions restrict themselves to the works: simple metric collection that provides them with dashboards. Expecting users to simply sit on their chairs and look at those metrics all day long is an invitation to failure, as we know that one can only do so much when they get tired and bored.

Application Observability With Kuma Service Mesh

The more services you have running across different clouds and Kubernetes clusters, the harder it is to ensure that you have a central place to collect service mesh observability metrics. That’s one of the reasons we created Kuma, an open-source control plane for service mesh. This tutorial will show you how to set up and leverage the Traffic Metrics and Traffic Trace policies that Kuma provides out of the box.

Services Don't Have to Be Eight-9s Reliable with Liz Fong Jones from Honeycomb | Kongcast Episode 1

In this Kongcast episode, Liz Fong-Jones, principal developer advocate at Honeycomb, introduces us to the concept of error budgets for service-level objectives (SLOs) and demonstrates how to accelerate software delivery with observability.

Kong Konnect: Maximize Service Reuse, Observability and Manageability

Developer teams need to move faster than ever today and reusing services is a great driver for agility. In this tutorial, you'll learn how to use ServiceHub to enable development teams to search, discover and consume existing services. You'll also learn about Runtime Manager and Vitals for operational metrics of deployed services.

Service Mesh and Microservices: Improving Network Management and Observability

Whether you're transitioning away from a monolith or building a green-field app, opting for a microservice architecture brings many benefits as well as certain challenges. These challenges include namely managing the network and maintaining observability in the microservice architecture. Enter the service mesh, a valuable component of modern cloud-native applications that handles inter-service communication and offers a solution to network management and microservice architecture visibility.

How to Automate Service Mesh Observability With Kuma

The more services you have running across different clouds and Kubernetes clusters, the harder it is to ensure that you have a central place to collect service mesh observability metrics. That’s one of the reasons we created Kuma, an open source control plane for service mesh. In this tutorial, I’ll show you how to set up and leverage the Traffic Metrics and Traffic Trace policies that Kuma provides out of the box. If you haven’t already, install Kuma and connect a service.

How to get Observability for Apache Airflow

Observability of Apache Airflow presented by Ry Walker, Founder & CTO at Astronomer. Apache Airflow has become an important tool in the modern data stack. We will explore the current state of observability of Airflow, common pitfalls if you haven't planned for observability, and chart a course for where we can take it going forward.

Going Beyond Observability for Spark Applications & Databricks Environments

Join Chris Santiago, Solutions Engineer Director at Unravel Data, as he takes you through Unravel’s approach to getting better and finer grain visibility with Spark applications and how to tune and optimize them for resource efficiency. An overview of out of the box tools like Ganglia and their overall lack of visibility on Databricks jobs How Unravel helps you gain finer grain visibility, observability, monitoring into Spark data pipelines How Unravel can recommend better configurations and tuning of Spark applications.