I'm no stranger to ranting about deploys. But there's one thing I haven't sufficiently ranted about yet, which is this: Deploying software is a terrible, horrible, no good, very bad way to go about the process of changing user-facing code. It sucks even if you have excellent, fast, fully automated deploys (which most of you do not). Relying on deploys to change user experience is a problem because it fundamentally confuses and scrambles up two very different actions: Deploys and releases.

When you’re just getting started with observability, a proof of concept (POC) can be exactly what you need to see the positive impact of this shift right away. Coveo, an intelligent search platform that uses AI to personalize customer interactions, used a successful POC to jumpstart its Honeycomb observability journey—which has grown to include 10,000+ machine learning models in production at any one time. Wondering how Coveo got there? So were we.

Observability is coming into its own, as SREs and DevOps practitioners increasingly seek to centralize the sprawl of tools and data sources to better manage their workloads and respond to incidents faster — and to save time and money in the process. That was the overarching message from more than 250 observability practitioners who took part in the Grafana Labs’ first ever Observability Survey.

So far in this series, I’ve outlined how a scaling enterprise’s accumulation of data (data gravity) struggles against three consistent forces: cost, performance, and reliability. This struggle changes an enterprise; this is “digital transformation,” affecting everything from how business domains are represented in IT to software architectures, development and deployment models, and even personnel structures.

If you’re in the cloud engineering and DevOps space, you’ve probably seen the name OpenSearch a lot over the last couple of years. But, what is your current understanding of OpenSearch, and the components around it? Let’s take a closer look.

When we work at it, professionals are pretty good at analysis. We can break down a simple system, look at its parts and their relations, and master it. Given enough time and teammates, we can analyze a very complicated system and fix it when it breaks. But complex systems don’t yield to analysis. We have to add another skill: sense-making. Complex systems have parts that learn and change, with relations that vary with state and history. They respond to and influence their environment.