The latest articles & publications, outlining our current thinking.
If we take a step back, we quickly realise that Data Mesh has many similarities to another hot topic: Team Topologies. Let's look at where this convergence of ideas could direct us.
Additional resources for those who want to take more of a deep-dive into the subject of Data Mesh.
In this post, we try to explain the concepts of Data Mesh and Data Fabric, and how they relate to each other.
What is Data Mesh? This is series of posts to help you understand the concept of Data Mesh and perhaps give you a few ideas how it could help your organisation.
Do you want to run a privacy-compliant LLM? What are your options? What are the real costs?
Are you using an LLM, but haven't checked if its license allows for commercial use? Is the model in compliance with upcoming EU law? Is your business at risk?
I took a deep-dive and read the EU AI Act, with amendments, so you don't have to. Here's a summary of the key points, without the histrionics.
Can GPT-4 replace no-code tools and even single-purpose SaaS products? Should the no-code movement be concerned? In this article, we try to get to the bottom of this question.
Software-as-a-Service ("SaaS") has mostly delivered on the promise of zero ops, zero maintenance software. But what happens if a core service your business relies on disappears overnight? What happens if it doesn't come back? And in the current Venture Capital downturn, how likely is this to occur, and what steps are you taking to mitigate this risk?
Knowing when to build vs when to buy can seem like a dark art at times. But what if we told you there is a tool/model that can make the process a little more objective? What if we showed you how to do it? Read on, and we will do exactly that!
"Will AI replace programming" is a lazy, unimaginative trope. It will not replace software engineers anytime soon. It will, however, give them another useful tool, increasing reliability and productivity. Here is how we shift our thinking from polar extremes, to practical solutions for Software Engineering with AI, read on!
Unless you’ve been living under a rock, you must have heard about ChatGPT by now. The hot-take of the moment is that AI will replace Software Engineers. But as with many hot-takes, it is wrong. AI will fundamentally change software companies, but not in the way most expect.
The Agile Manifesto was written in 2001. Extreme Programming was first introduced in 1996.They both set out a set of guiding principles that influence software until now. But in 2022, it is time to move beyond- and away from Agile. Let us set out why!
Most companies use software tools which haven’t been blessed by in-house engineers nor operations. There is what we call "shadow IT". We posit this is due to how organisations approach software management, and that No-Code tools are a solution to this issue. As a technology leader, why should you care about No-Code?
Measuring software engineers and teams by metrics is fraught with controversy, and rightly so. There are many horror stories of individual developer productivity being measured by Lines of Code, number of commits and other activity-based metrics. This is usually ill-advised, but it doesn’t mean that metrics don’t have a place in software engineering. Let’s look at a small set of metrics we’ve found useful!
What is the difference between high-performing engineering teams and unremarkable teams? How come some teams seem to effortlessly over-deliver, and do so while producing reliable software that rarely has production issues? Over the course of 20 years, I have observed some common patterns in how these teams operate. Allow me to share some conclusions!
Feature-flagging, A/B testing & Canary-releases sometimes get mixed-up, not least because there is a lot of overlap in use and features, while they target two different user-groups: software engineers and product manager respectively. But what do they do, and when & why should you consider using them, and how? Let's discuss it in this post.
In the 1970s & 80s, the expression "nobody ever got fired for buying IBM" was coined to illustrate IBMs utter dominance of the IT industry, and how executives playing it safe kept buying IBM, despite their products frequently being more expensive and inferior to alternatives. I believe we've reached the same cross-roads for todays dominant cloud provider, AWS (and to a lesser extent AWS major competitors). Why? Let me elaborate!
In 2022 Kubernetes reigns supreme in mindshare among workload schedulers/orchestrators. But does mindshare and popularity mean it is the best one out there? There is another kid on the block, Hashicorps Nomad, that might just give Kubernetes a run for it’s money. Let’s take a look and compare!
Many companies aspire to do Continuous Deployment, but few ever get there. How difficult is it really to achieve? Should it really be the white rhino of our industry? We would like to put forward that it doesn't need to be hard, but requires organizations to stop some bad habits and acquire a few good ones. This post will show how.
How do you increase the number of developers without losing much of the productivity you get when a team is just “a startup”? Is it even possible? If so, how do you do it? The answers are somewhat counter-intuitively: ”yes” and ”you don’t”. How do we reconcile these two answers? Let’s explore!
Google release GKE Autopilotat the end of February 2021. The main difference is that Autopilot takes all cluster and node management out of the hands of your team, thus further blurring the line between traditional Serverless and Kubernetes. Could this be a game changer? Let's have a look.
Microservices have been popular for many years now. Nevertheless, distributed systems are hard to get right. In this post, we will go beyond theory and discuss common mistakes to avoid and rules of thumb for getting it right. These principles are borne from practical experience and we have the battle-scars to prove it. Many will be familiar, but others may be new and yet others contentious. Let's get started!
It is a cliché to do predictions for the coming year around New Year, but we are not beyond clichés if it lets us look forward. Let's make some predictions!
Serverless or Kubernetes? The "TL;DR" answer to the question is, "it depends". The longer answer is, it depends on a number of factors, which we will try to address in this article.
To productively build serverless & distributed systems, we need to adopt new practices, some which may seem counterintuitive at first. This post will suggest five concrete practices to up your game.
To know where we are going, it is sometimes useful to reflect on where we have been and the progress we have made. Let's take a walk down memory-lane and see how IT operations- and software deployments have evolved and where this might lead us.
Timelines and budgets are often necessities for making informed decisions. But an overzealous focus on "being on-time" can be the cause large overruns. This article will take you through concrete examples of the estimation paradox and give recommendation on what can be done.
Chaordic as a company has been around since 2017, but we still get asked about the origins of the name. Many have speculated about the meaning of the name, some correctly, some have gotten it wrong. Chaordic is a term combining of the words "Chaos" and "Order", coined by former Visa CEO Dee Hock.
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