Offerings | Buckingham Cloud

What you'll find in every Buckingham Cloud entry

We don't publish textbook summaries. Every post comes from something we tested in a real environment: a cluster that went down, a Terraform plan that two people overwrote, an invoice that went up with no explanation. If we couldn't reproduce it, we don't write about it.

Configuration snippets ready to read

Kubernetes manifests, Terraform blocks, and broker files appear in full, with the context around them: version, provider, and why they ended up that way. We don't trim the uncomfortable parts or show only the YAML that looks good on screen.

Dashboard screenshots, not mockups

The charts that accompany each topic come from dashboards we use to diagnose incidents: CPU usage with visible throttling, queues that pile up, latencies that spike at a specific hour. They help you recognize the same pattern when it shows up in your own environment.

Tool comparisons with no sponsorship

When we put two options side by side, we say what we measured, under what conditions, and what was left out of scope. Nobody pays us to tip the scales. If a tool was painful to work with, we write that up with the specific case that justifies it.

Topics that keep coming up in production

Kubernetes, Terraform, observability, message queues, instance costs, and legacy service migration. These aren't topics chosen because they're trendy: they're the ones that come back every time a platform team has to sustain something serious.

An approach for platform, SRE, and backend teams

We write for those who have to make a decision on Monday, not for those looking for an introduction. The articles assume you already work with containers and deployment automation, and they go straight to the detail that changes a decision.

Weekly newsletter and search by tags

If you'd rather have the notes come to you, there's a weekly newsletter with the latest published content. And if you're looking for something specific, the networking, storage, or CI/CD tags group the posts by the topic you want to solve right now.

If you want to see how this applies to a concrete case, you can review the work formats we offer or stop by who writes the log.

What comes next after reading an article

Almost no technical note ends where the text ends. After adjusting a memory limit or moving Terraform state, questions remain that do not fit in the original article: what to review the next day, which metric to watch for a week, which decision is better postponed. This selection gathers the posts that are usually read in sequence, because they share the same type of problem and the same type of answer: concrete configuration, screenshots of real dashboards, and comparisons without sponsorship. They are intended for platform teams, SREs, and backend developers who already have a cluster running and need judgment, not an introduction from scratch.

Kubernetes in production: resource limits that actually prevent outages

It is worth reading right after a post about deployments, because it explains why a pod restarts without anyone having touched the manifest. It reviews poorly calibrated requests and limits, CPU throttling that does not show up in application logs, and the difference between an OOMKilled and an overly aggressive liveness probe. It includes manifest snippets and a way to read kubelet events before changing any numbers.

Useful if you copied values from a test environment to production and something started failing.

Terraform with remote state: when the local backend stops working

The natural link from the previous note when the problem is not in the cluster but in how changes are applied. It describes the symptoms of accidentally shared local state: overlapping plans, orphaned resources, and locks that no one declared. It also reviews how locking works in remote backends and what checks to perform before moving the state file without breaking the plan.

Intended for teams where more than one person applies changes in the same week.

Observability without noise: metrics, logs, and traces that are actually used

After fixing an incident, the next question is what signal would have detected it earlier. This entry proposes classifying metrics, logs, and traces by their real operational value, shows dashboards with a few well-chosen series, and discusses when it makes sense to sample traces instead of storing them all. It closes with retention decisions and the cost of storing data that no one queries.

It reads well alongside the limits note, because many alerts originate there.

Message queues: retries, dead letters, and duplicates

When a consumer fails, the queue can amplify the error instead of containing it. Here, backoff policies, dead-letter queues, and deduplication by idempotency key are compared, with configuration examples in well-known brokers. It explains why infinite retries hide design flaws and how to measure the age of pending messages to detect accumulation before it becomes visible.

Applies if your distributed architecture already has more than one service talking.

Cloud instance costs: where the budget escapes

The bill rarely goes up because of a single resource. It is usually the sum of oversized instances, orphaned disks, and test environments that nobody shuts down. This review proposes a periodic audit of the highest-spending services, shows how to tag resources to attribute costs to teams, and warns about usage commitments signed without measuring actual demand.

No promises of magic savings: review habits and nothing more.

Migrating a legacy service to containers: mistakes that keep repeating

The logical conclusion of the series. Moving a legacy service into a container clashes with assumptions the code took for granted: absolute paths, state on disk, network dependencies, and startups that take longer than the liveness probe tolerates. It gathers failure patterns observed in migrations and proposes a verification sequence before exposing the service to traffic.

It complements the readiness and liveness adjustments from the first entry.

How a topic becomes a published post

At Buckingham Cloud we don't publish on a schedule or out of obligation to anyone. Every article starts from a real problem that came up in a production environment, is researched carefully, and is written with the material left on the table: manifests, dashboard screenshots, tool comparisons. Here is the full journey, from the moment a topic enters the list until the post is available to read.

Step 1

Choose the topic by tag

Everything starts with a specific tag: networking, storage, CI/CD, Kubernetes, Terraform, observability, or message queues. If a topic doesn't fit any of them, it's discarded. The idea is that anyone looking for a category finds coherent material and not a loose mix of articles.

Step 2

Reproduce the problem

Before writing, the team reproduces the scenario in a controlled environment. Resource limits are adjusted, a network failure is forced, a message retry is simulated. Without that reproduction there is no post: we would rather not publish than describe something we couldn't see working.

Step 3

Gather the raw material

The configuration snippets that were actually used are saved, along with screenshots of the dashboards with the relevant metrics and comparisons between tools when one had to be chosen. No made-up examples: what appears in the post is what was recorded during the test.

Step 4

Write and review

The text is put together with a direct tone, designed for platform teams, SREs, and backend developers who need to decide, not read theory. Another engineer on the team reviews that the claims are supported by the collected material and that there are no stretched conclusions.

Step 5

Publish and notify

The post becomes available on the blog and is announced in the weekly newsletter, which summarizes what was published and links to each article. Anyone who prefers to follow a specific topic can search by tag and find all related posts without depending on the mailing.

If you want to see the full set of formats and resources we offer, check out the services and content formats. To understand where this approach comes from, you can read how the team works.