When the huge AWS outage in October introduced down world companies together with Sign, Snapchat, ChatGPT, Zoom, Lyft, Slack, Reddit, McDonald’s, United Airways, and even Duolingo, it uncovered the fragility of cloud-first operations that, in as we speak’s cloud-first world, something can fail. As corporations distribute their operations throughout world cloud platforms, the query is now not “whether or not techniques will fail”, however “how shortly they’ll recuperate and the way intelligently they’re constructed to take action.”
Elena Lazar is among the many engineers who’ve gained a strong understanding of this actuality, a senior software program engineer with over twenty years of expertise designing resilient architectures, automating CI/CD pipelines, and bettering observability throughout France, Poland, and america. As a member of the Institute of Electrical and Electronics Engineers and the American Affiliation for the Development of Science, Elena bridges the worlds of utilized engineering and scientific inquiry.
Elena informed HackRead what it means to engineer for failure within the period of distributed techniques: why resilience issues greater than perfection, how AI-assisted log evaluation is reshaping incident response, and why transparency usually beats hierarchy when groups face advanced system breakdowns. She additionally spoke in regards to the world cultural shift redefining reliability engineering, shifting from reactive firefighting to a mannequin the place restoration is inbuilt from the beginning.
Q. Based on the New Relic observability forecast, the median value of a high-impact IT outage has reached $2 million per hour, and that quantity retains rising. Out of your perspective, why is restoration turning into so pricey, and what can corporations realistically do to minimise these losses?
A. The principle cause outages are getting costlier is that digital infrastructure has turn into deeply interconnected and globally crucial. Each system now depends on dozens of others, so when one main supplier like AWS or Azure goes down, the affect cascades immediately throughout industries.
Restoration prices are rising not solely due to direct downtime, but additionally due to misplaced transactions and model harm that occur inside minutes of an outage. The extra world and automatic an organization turns into, the tougher it’s to take care of localised fallback mechanisms.
The one lifelike method to cut back these losses is to design for managed failure: construct redundant architectures, simulate outages commonly, and automate root-cause detection in order that restoration time is measured in seconds, not hours.
Q. Elena, you’ve labored for over 20 years in software program engineering, from a contract developer to your present function in large-scale initiatives within the broadcasting and content material distribution area. How has your understanding of reliability in distributed techniques developed by way of these totally different levels of your profession?
A. Twenty years in the past, actually large-scale distributed techniques had been comparatively uncommon and largely present in huge firms, just because constructing something dependable required sustaining your personal bodily infrastructure; even when it was hosted in knowledge centres, it nonetheless needed to be owned and operated by the corporate. Again then, a single enterprise server operating each a CRM and an internet site might be thought of “large-scale infrastructure,” and reliability largely meant conserving the {hardware} alive and manually checking functions.
The final 15 years modified all the pieces. Cloud computing and virtualisation launched elasticity and automation that made redundancy reasonably priced. Reliability grew to become not only a reactive aim however a design function: scaling on demand, automated failovers, and monitoring pipelines that self-correct. If we as soon as wrote monitoring scripts from scratch, now we’ve got dashboards, container orchestration, and time-series databases all obtainable out of the field. Right now, reliability will not be a toolset; it’s a part of system structure, woven into scalability, availability, and value effectivity.
Q. Are you able to share a selected case the place you deliberately designed a system to tolerate element failures? What trade-offs did you face, and the way did you resolve them?
A. In my present work, I design CI and CD pipelines that may stand up to failures of dependent companies. The pipeline analyses every error: generally it retries, generally it fails quick and alerts the developer.
In previous initiatives, I utilized the precept of swish degradation: letting a part of an online or cellular software go offline briefly with out breaking the entire consumer expertise. It improves stability however will increase code complexity and operational prices. Resilience all the time comes with that trade-off: extra logic, extra monitoring, extra infrastructure overhead, however it’s value it when the system stays up whereas others go down.
Q. In your work on CI/CD pipelines and infrastructure automation, you’ve made pipelines resilient to failures in dependent companies. Which instruments or practices have confirmed best?
A. For years, we used scripts to analyse logs programmatically. Extending them for brand spanking new situations took longer than handbook debugging. Not too long ago, we started experimenting with massive language fashions (LLMs) for this.
Now, when a pipeline fails, a part of its logs is fed to a mannequin skilled to recommend possible root causes. The LLM’s output goes straight to a developer through Slack or electronic mail. It usually catches easy points, flawed dependency variations, failed checks, outdated APIs and saves hours of assist time.
I’m nonetheless pushing for deeper LLM integration. Mockingly, I generally run a light-weight AI mannequin in Docker on my laptop computer simply to hurry up log evaluation. That’s the place we’re nonetheless bridging automation gaps with creativity.
Q. Having labored on initiatives in banking, broadcasting, and e-commerce, which architectural patterns have confirmed best in bettering system reliability?
A. Replication mixed with load balancing is the unsung hero. Enabling well being checks in AWS ELB, as an example, virtually implements a circuit breaker: it stops routing visitors to unhealthy nodes till they recuperate. We additionally depend on database replication; fashionable DBMSs assist asynchronous replication by default.
In a single banking mission, integrating an exterior system overloaded a monolithic service. We broke that performance right into a scalable microservice behind a load balancer, which solved the issue but additionally uncovered hidden dependencies. Some inside instruments failed just because they weren’t documented. That have taught me a common rule: undocumented infrastructure is a silent reliability killer.
Q. You’ve labored extensively on infrastructure automation and repair reliability. How do you resolve which alerts to observe with out overwhelming groups or inflating prices?
A. Right now, including metrics is simple as a result of most frameworks assist them out of the field. There’s a transparent shift from log parsing to metrics monitoring as a result of metrics are secure and structured, whereas logs are continuously altering. Nonetheless, detailed logs stay indispensable for understanding «why» behind an outage.
It’s about steadiness: metrics hold techniques wholesome; logs clarify their psychology.
Q. Many organisations now run tons of of microservices. What pitfalls do you see when scaling techniques this manner, particularly round failure affect?
A. Useful resource overhead is the largest hidden value that load balancers and cache layers can eat, as a lot compute energy because the core companies themselves. The one actual mitigation is sweet structure.
Failure propagation is a traditional instance. When companies talk with out safeguards like heartbeat calls, circuit breakers, or latency monitoring, one failure can shortly cascade by way of all the system. But over-engineering the safety provides latency and value.
Typically the best options work greatest: return a fallback «knowledge unavailable» response as a substitute of an error, or use good retry logic. Not each drawback requires fairly common however pricey event-based, asynchronous architectural options equivalent to a Kafka cluster.
The important thing to managing development is transparency. Limiting builders to remoted «scopes» with out seeing the larger image is the worst anti-pattern I’ve seen. Fashionable Infrastructure-as-Code instruments make even huge techniques readable, reproducible, and, most significantly, comprehensible.
Q. Outages can value corporations hundreds of thousands per hour, based on New Relic and Uptime Institute stories. How do you justify long-term investments in reliability when enterprise priorities are sometimes targeted on short-term supply?
A. We reside in an period the place everybody is aware of the price of failure. You don’t should argue a lot anymore. Rising failure charges robotically set off investigations, and the information speaks for itself.
For instance, if the error price in an AOSP platform replace service spikes due to previous Android purchasers, we analyse each the service and the distributed OS picture. The enterprise case all the time boils all the way down to: repair reliability or lose customers.
Even for inside instruments like code repositories, documentation, CI and CD pipelines, the logic is comparable. Unreliable infrastructure delays customer-facing options. The problem isn’t convincing stakeholders, it’s discovering the time and folks to repair it.
Q. Based on your expertise, what classes would you share with engineering leaders constructing resilient pipelines as we speak?
A. Failures are inevitable, however chaos isn’t. What causes chaos is unclear possession and poor communication. One easy rule helps immensely: give everybody entry to the complete codebase. When mixed with a transparent accountability map, even when it’s only a well-structured Slack workspace, it empowers groups to collaborate as a substitute of ready for tickets to escalate. Transparency is step one towards resilience.
Q. You’ve labored with machine studying–pushed observability and talked about your curiosity in agentic AI for automated remediation. What’s your imaginative and prescient for the way AI will rework reliability engineering over the following 5 years?
A. Machine-learning-driven observability is already right here, feeding logs into AI fashions to foretell failures earlier than they occur. However the true frontier is automated remediation: techniques that self-heal and produce significant post-incident stories.
Sure, there’s inertia as enterprises worry autonomous modifications in manufacturing, however economics will win. Startups and dynamic organisations are already experimenting with agentic AI for reliability. Finally, it would turn into the usual.
Resilience isn’t nearly uptime. It’s a mindset that prioritises transparency, possession, and techniques that anticipate and recuperate from failure by design.
(Picture by Umberto on Unsplash)
