Honeycomb is known for its incredibly fast performance: you can sift through billions of rows, comparing high-cardinality data across thousands of fields, and get fast answers to your queries. And none of that is possible without our purpose-built distributed column store. This post is an introduction to what a distributed column store is, how it functions, and why a distributed column store is a fundamental requirement for achieving observability.

If you are a candidate looking for your next role that involves an in-depth knowledge of Elasticsearch and the wider Elastic Stack then you will want to revise beforehand. In this resource guide on the top ELK interview questions, we've listed all of the leading questions that candidates are commonly asked about Elasticsearch, Logstash & Kibana (and their contemporary tools and plugins) alongside the answers. Want to improve your knowledge further?

Kubernetes is the de-facto platform for orchestrating containerized workloads and microservices, which are the building blocks of cloud-native applications. Kubernetes workloads are highly dynamic, ephemeral, and are deployed on a distributed and agile infrastructure. Although the benefits of cloud-native applications managed by Kubernetes are plenty, Kubernetes presents a new set of observability challenges in cloud-native applications. Let’s consider some observability challenges.

The content delivery network (CDN) has been an integral part of application infrastructure for more than two decades. A CDN is critical to the end-user experience, but it is no longer considered to be just a caching server. It has evolved to provide security from cyber threats, including DDOS attacks along with front end optimization. Although CDN services are now an indispensable part of any application infrastructure, visibility into CDN performance remains limited.