The prometheus monitoring stack includes an alert dispatching component called alertmanager. Many integrations are available to dispatch the alerts to pager, slack, etc… i.e. notification channels. But how to bring easily and efficiently automated responses is the question we’ll try to answer here.

More features, active users, and data are collected daily. Your database is slowing your application. Many people don't understand database sharding, which could solve their difficulties. This article explains database sharding, its benefits, including how to use it and when not to.

Have you ever wondered what it takes to build a serverless software as a service (SaaS) offering in under a year? In this blog post, we will describe how we built ClickHouse Cloud – a managed service on top of one of the most popular online analytical processing (OLAP) databases in the world – from the ground up. We delve into our planning process, design and architecture decisions, security and compliance considerations, how we achieved global scalability and reliability in the cloud, and some of the lessons we learned along the way.

The Twelve-Factor App methodology is a methodology for building software-as-a-service applications by Adam Wiggins. We cover how they have since evolved, and what we can learn from them today and how they changed the status quo of yesteryear.

Streamlining Your Team’s Incident Response

How Relational Databases Work. This post talks about how indexes and transactions work on the inside of relational databases.

GoReplay is an open-source tool for capturing and replaying live HTTP traffic into a test environment in order to continuously test your system with real data. It can be used to increase confidence in code deployments, configuration changes and infrastructure changes.

TerraCurl is a utility Terraform provider that allows engineers to make managed and unmanaged API calls in their Terraform code.

There are multiple ways to configure environment settings in Terraform. This repo evaluates initial four but it has branched out to multiple possible methods with an aim of writing DRY easy to maintain code.

For a data format, yaml is extremely complicated. It aims to be a human-friendly format, but in striving for that it introduces so much complexity, that I would argue it achieves the opposite result. Yaml is full of footguns and its friendliness is deceptive. In this post I want to demonstrate this through an example.

This post is a rant, and more opinionated than my usual writing.

Observability has become a bit of a buzzword in the industry for the last few years. Exactly what "observability" means depends on who you ask, but most people would agree its about both:

- Being able to observe customer experience and behavior

- Being able to observe and understand what's happening within our technology solutions

There's plenty of content out there telling you how to implement observability, or what good looks like. But what about bad observability? What are some anti-patterns to watch out for?

Logging information in a structured format for better analysis and processing of log data