So we did the work. We evaluated the resources data engineers actually use, judged on five things that matter: relevance to the DE interview loop, depth of practice, realism of the questions, feedback quality, and price. Below is the ranked list.

The short answer is: "exactly once delivery" is a great concept, but in practice it's mostly fiction given the nature of distributed systems. What you really can make is at-least-once delivery + idempotent processing, yielding exactly once effects. This article demonstrates how to accomplish this in Node.js with a tangible, functioning implementation.

I paid a price to learn this, too: I spent half my career learning these lessons the hard way, as many others probably did.

Two weeks later, during a casual conversation with that hiring manager, the reason came out. It wasn't her architectural skills or her communication. It was a question someone had slipped in near the end: "Walk us through how you'd set up an AI-assisted code review pipeline for a team that ships twelve microservices." She described doing it manually. The other finalist described standing up an orchestration layer with context-aware models, configuring fallback thresholds, and building observable feedback loops that trained the team's prompt library over time.

This article walks through what Quick is, how its components fit together technically, how the MCP integration model works with real code, and where it sits relative to the rest of AWS's agent stack.

People often say that QA is just there to check developers’ work. That is a superficial view. The role of QA is not simply to confirm that the code works, but to try to uncover every scenario in which it can fail. QA engineers have to think differently from the people who built the system. They need to think like real users, including those who will inevitably follow unexpected paths or use the product in ways no one originally planned.

And still, the interviews go nowhere.

There's a story economists love to tell about ATMs and bank tellers. You've probably heard it. When ATMs were introduced in the 1970s, everyone predicted they would eliminate teller jobs. They didn't. By the 2000s, there were actually more tellers than before the ATM existed. The story became a load-bearing parable for anyone who wanted to argue that technology doesn't kill jobs, cited by economists like Daron Acemoglu and David Autor, by tech executives like Eric Schmidt, and more recently by politicians reaching for reassuring historical analogies when asked about AI.

David Oks recently published a sharp piece that complicates this parable. His conclusion: ATMs didn't kill bank tellers, but the iPhone did. Teller employment entered prolonged decline in the 2010s because mobile banking made the branch itself irrelevant. Once customers stopped coming in, the institutional context that gave the teller role its value simply ceased to exist.

This prediction aged quite interestingly.

No SLO breaches, no latency spikes, no 3 AM Slack messages from an on-call engineer clutching cold coffee. The system is working — by every conventional measure it's healthy — and yet something is deeply wrong. Money is hemorrhaging out of the infrastructure at a rate that won't become visible until the CFO opens a billing dashboard, squints at a number that seems obviously misformatted, and then realizes with a specific, cold dread that it isn't.

The Ageism Crisis in Tech: A Stark Reality

Tech's youth obsession is no secret. A 2023 AARP report found that 1 in 5 workers over 50 face age discrimination, with tech hit hardest — median employee age at major firms hovers around 30-32, per Levels.fyi data. High-profile cases abound: Intel's 2024 layoffs disproportionately axed veterans, while startups shun "overqualified" applicants fearing cultural misfits. The rationale? Assumptions that older workers lag in adapting to rapid tech shifts, from cloud-native architectures to GenAI workflows.

This is the default state for most shops. Not negligence, exactly. More like a structural information deficit: the feedback loop between code change and cost impact is measured in billing cycles, not seconds.

In 2025, I read 34 books spanning philosophy, history, economics, and software engineering. While these subjects may seem unrelated to coding, they all aim to deepen our understanding of systems, whether in societies, economies, or software architectures.

Today, engineers are expected to do more than build systems — they are expected to influence decisions, communicate across teams, and demonstrate impact beyond their immediate environment. Yet most career advice still focuses solely on improving technical skills.

You increase workers. The job runs faster.

Once models are embedded into core product flows such as transaction routing, risk evaluation, or decision automation, behavior stops being fully predictable. Outcomes depend not just on code, but on data distributions, external dependencies, retry paths, latency budgets, and second-order effects that only appear at scale. As a result, product managers and engineers can no longer operate in parallel lanes. They must rethink how they work together.

Last year, we asked you for your thoughts on emerging and evolving software development trends, your day-to-day as devs, and workflows that work best — all to shape our 2026 Community Research Report. The goal is simple: to better understand our community and provide the right content and resources developers need to support their career journeys.

When an incident occurs, the mean time to recovery (MTTR) often depends less on the technology stack and more on who is on call. If the expert is unavailable, the system stays down. This is the knowledge bottleneck.

Do you work in software development and have something interesting to share with your industry peers? If so, we invite you to submit your content for consideration to be published to DZone's global audience of software development professionals. 

Organization: NETINT, Supermicro, and Ampere® Computing

Problem: The demand for high-quality live video streaming has surged, putting pressure on operational costs and user expectations. Legacy x86 processors struggle to handle the intensive video processing tasks required for modern streaming.