I spent years treating Spring Boot startup time as a fact of life, the way you treat weather. Then I found out the JVM has had a fix for a big chunk of it since Java 12; it works beautifully inside Docker, and almost nobody bakes it into their images. It is called Class Data Sharing, CDS for short, and this article shows you how to make your Docker build do the work

A safer operating model treats each agent as an isolated contributor with a dedicated Git worktree, an explicit file-level contract, deterministic validation commands, and no authority to integrate directly into the protected branch. Git worktrees provide multiple linked working trees for one repository, while modern coding-agent platforms independently reinforce the same principle through isolated sandboxes, scoped write access, and controlled network permissions. 

The repository's demo reports 30x+ actor-to-worker oversubscription for that sample workload, with sub-second activation. It is a demonstration, not a production capacity guarantee. 

"The daily digest the pipeline sends, with new postings badged."

If you deploy AWS Lambdas through Terraform, you almost certainly use archive_file. With enough lambdas, a single terraform apply can kill the CI runner with OOM. The trickiest part is that you will not see any errors in Terraform output and have no clue what just happened.

I noticed this when my lambdas started failing — every first terraform apply after a routine change. SIGKILL from the kernel OOM killer and nothing in Terraform logs. The strange part is that reapply sometimes worked — not always on the first try, but eventually it went through. I've named the ticket "Flaky CI," and two weeks of investigation was focused on the CI itself: runner memory, parallel jobs, Docker leaks. terraform apply was the last suspect — from my perspective, there was no way or reason for it to consume so much memory.

• In regulated industries, cloud migration success is determined less by technology selection and more by how deliberately you decouple risk vectors — compliance risk, organizational hesitation, user adoption gaps, and integration changes — so no single failure can derail the whole program.
• You can successfully migrate an application to AWS while keeping data on-premises by routing through a REST API abstraction (e.g., IBM’s DB2 REST API layer) paired with dedicated AWS security groups controlling cloud-to-on-prem traffic, allowing the data migration to proceed on its own compliance and trust-building timeline.
• The most dangerous compliance gap in regulated applications isn’t declared sensitive fields — it’s free-form text fields where users may inadvertently type SSNs, credit cards, or other regulated identifiers; proactive tokenization in the application’s write path closes this gap before any audit finds it.
• Long-tenured business users carry a decade of UX muscle memory that QA testing cannot replicate; allocating real production validation time (such as a 15-day dark deployment cohort) is essential when migrating systems users have relied on daily for 10+ years.
• Before starting a regulated cloud migration, ask which risk vector each architectural decision is decoupling and whether your team is aligned on why — this single question reframes "cloud migration" from a technology project into a coordinated risk-management exercise.

Introduction

Most published writing on legacy-to-cloud migration treats it as a technical exercise: pick the stack, plan the cutover, flip the switch. In regulated industries, that framing fails — and the failure mode isn’t a missed deployment window. It’s a stalled program, a failed compliance audit, or a client who pulls back from the cloud strategy entirely.

A cloud migration in healthcare insurance is as much about regulatory risk management, organizational trust-building, and user adoption as it is about microservices and Fargate. Get the technology right and miss the risk choreography, and the project doesn’t ship.

But feature flags are not limited to simple on/off switches. They can support gradual rollouts, experimentation, access control, operational safeguards, and runtime configuration. Each of these use cases has a different purpose and requires a different way of designing and managing flags.

What first got you interested in digging into complex infrastructure and systems problems?

In this blog, you will take a closer look at the different exchange types that can be used in RabbitMQ. All are demonstrated by means of examples in a Spring Boot application. Enjoy!

Introduction

In the previous blog, you learned the basic concepts of RabbitMQ and how to use it in a Spring Boot application. However, you only scratched the surface of it, so now it is time to dig a bit deeper into the different exchange types.

A Familiar Routine

If you build data pipelines for a living, you've lived this story. Your PySpark job runs perfectly in a cloud notebook. You productionize it, push it through CI, deploy it to the cluster, and it fails. A dependency mismatch. A different Spark minor version. A Delta Lake protocol feature your local wheel doesn't know about. A timezone default nobody set.

You look at the sprint board, and tickets are moving to "In Review" faster than ever. Your developers are happy. They are writing boilerplate in seconds and generating entire component structures before their morning coffee gets cold. If you measure productivity by the sheer volume of code generated, your team has successfully turned into a factory.

The answer determines whether the architecture that follows will be auditable or not. Teams that answer it with process — "we have policies about that" — spend the next 18 months patching access-control gaps that reopen every time a new engineer joins or a new service gets wired in. Teams that answer it architecturally spend a week setting up AWS Organizations correctly and then largely stop thinking about it.

Session capture, per-line attribution, and selection-bias-free agent benchmarks, on top of the Git host you already use.

A technical overview for engineers and engineering leaders evaluating how much of their codebase is now written by AI agents - and who is accountable for it.

]]> Mon, 24 Aug 2026 17:00:01 GMT https://dzone.com/articles/3669854 Troian Serhii https://feeds.dzone.com/link/23567/17424573/bedrock-vs-vertex-vs-foundry Every few weeks, someone on my team, or in a client meeting, asks me the same question: "Which cloud should we use for our AI workloads?" I have been building enterprise integrations for over fourteen years now, and lately most of my time goes into RAG pipelines, vector databases, and agentic orchestration on top of these platforms. So I get this question a lot, and honestly, there is no single right answer. The right cloud depends on where your data already lives, what your compliance team will accept, and which models your architecture actually needs.

In this article, I want to walk through the three big players, AWS Bedrock, Google Vertex AI, and Microsoft Azure AI Foundry, and share what I have learned working with these platforms in real enterprise settings, not just from reading marketing pages.

]]> Thu, 20 Aug 2026 15:00:01 GMT https://dzone.com/articles/3671140 Balaji Venkatasubramaniyar https://feeds.dzone.com/link/23567/17423231/docker-ai-development-platform It stopped being just a packaging tool the day our onboarding doc got shorter instead of longer.

Three weeks into a new ML platform job, I asked a coworker why the 'getting started' doc had a section called 'If conda breaks, try the alternative.' He laughed in a way that told me it wasn't a joke. Every new hire spent their first two days fighting Python versions, CUDA driver mismatches, and a vector database that someone had installed locally in 2022 and nobody dared touch. We had four individuals on the team, each with distinct working setups, and "it works on my machine" was no longer a mere punchline; it had become a regular agenda item during our daily standup meetings. 

]]> Wed, 19 Aug 2026 17:00:00 GMT https://dzone.com/articles/3663882 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23567/17423057/docker-llm-containerization The first time I containerized a fine-tuned Llama model for a client's internal search tool, the build finished at 38 gigabytes. I remember staring at the terminal thinking there was no way that was right. It was right. The image included a CUDA base, PyTorch with every backend compiled in, model weights baked directly into the layer, and a pip cache that had not been cleaned. 

Pushing that to our registry took eleven minutes on a good connection. Pulling it onto a fresh node during an autoscale event took even longer, and by the time the pod was ready, the traffic spike it was supposed to handle had already passed. That's the moment I stopped treating LLM containers like regular application containers, because they are not the same animal at all.

]]> Wed, 19 Aug 2026 14:00:01 GMT https://dzone.com/articles/3663880 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23567/17422952/docker-engine-swarm-traffic If different Docker Engine versions are running simultaneously in a Docker Swarm cluster, this may lead not to an obvious service outage but to a more subtle scenario: partial traffic degradation on individual nodes. In this case, the issue appeared on one of the manager nodes, Traefik started reporting an unavailable status (health=0) for the router-app service, and the cause, according to the working hypothesis, was related to differences in iptables rules and overlay networking between Docker 28.1.1 and 28.2.2.

On June 22, 2025, this exact scenario occurred in the production cluster of the backend infrastructure for a socially significant public transportation mobile application. The system serves about 2 million users, several tens of thousands of daily active users, and a total load of around 1000–1600 RPS, so even partial degradation at a single entry point affected a high-load segment of traffic and could have had a noticeable impact on SLA metrics if it had not been localized in time.

]]> Wed, 19 Aug 2026 13:00:00 GMT https://dzone.com/articles/3665843 Denis Tiumentsev https://feeds.dzone.com/link/23567/17422393/building-internal-developer-platforms-on-kubernetes Introduction

The meeting that changed the platform team's direction was not a technical one. It was a conversation with a product engineer who had been at the company for eight months and had never successfully deployed to production without help from someone on the platform team. Not because she lacked skill. 

She was smart, experienced, and had successfully launched production systems at two previous jobs, but getting a working service into production meant dealing with fifteen different configuration files across four repositories, figuring out how Helm values files and Kustomize overlays worked together, and knowing which of the three CI pipeline templates to use based on whether the service needed a sidecar, a job scheduler, or neither.

]]> Tue, 18 Aug 2026 16:00:00 GMT https://dzone.com/articles/3659661 Pruthvi Raj Seknametla https://feeds.dzone.com/link/23567/17422336/palantir-foundry-data-pipelines Senior data engineers are trained to be skeptical of proprietary platforms. When I entered a Palantir Foundry training bootcamp, I expected to find a slow, expensive alternative to the mature tools I know on AWS and Azure. What I found instead was a platform built for a radically different user, one who cannot write SQL but needs answers now.

I want to write about what I actually observed honestly, including where I think the hype is justified and where I think it is not, because most Foundry content I have seen is either from Palantir's own marketing or from practitioners so embedded in the platform they have forgotten what it was like to come to it fresh. I am writing this while that perspective is still clear.

]]> Tue, 18 Aug 2026 15:00:04 GMT https://dzone.com/articles/3665482 Sashank siwakoti https://feeds.dzone.com/link/23567/17418297/from-raw-manifests-to-self-service-kubernetes-apps

Sponsored By: Nutanix The following is sponsored content. It may not reflect the views of our editorial staff.

The Kubernetes scaling problem nobody talks about

Enterprise platform teams encounter the same pattern repeatedly: a Kubernetes platform works well enough that nobody wants to change it.

This happens gradually as teams make reasonable technology choices: selecting different ingress controllers, secrets management tools, CD platforms, or observability software. Individually, none of these decisions is a problem. Months later, however, they’ve created a Kubernetes environment that only a handful of people understand. As soon as that one person gets sick or leaves the company, maintaining or improving the platform becomes much more difficult.

]]> Fri, 14 Aug 2026 16:27:20 GMT https://dzone.com/articles/3673195 DZone Staff