Latest news with #microservices
Fast Company
02-07-2025
- Business
- Fast Company
Why test observability is critical for enterprise-scale testing
As organizations grow and embrace digital transformation, their software systems are becoming more complex than ever. With innovation moving at breakneck speed, enterprises are now rolling out updates multiple times a day—sometimes even within minutes. Large enterprises like Amazon and Netflix are pushing changes in real time, exceeding 100 changes daily and raising the bar for software development. This shift—especially with the rise of cloud-native and microservices-based architectures—has made traditional monitoring tools less effective. As enterprises migrate to more dynamic architectures—leveraging microservices, containers, and serverless computing—their need for comprehensive observability becomes even more critical. The observability market is projected to reach $2.75 billion by 2025 and $4.38 billion by 2030. Traditionally, teams relied heavily on test monitoring. However, test monitoring often falls short in pre-release environments, where failures demand deeper diagnostic insights. Test observability transforms the traditional testing process and addresses real-time performance across complex systems. This allows enterprises to quickly detect and resolve issues, enhance efficiency, and reduce costs. In fact, 79% of organizations with centralized observability are saving more time and money. It also supports faster time-to-market by accommodating major architectural shifts and ensuring organizations stay agile in the face of ever-evolving technological demands. NAVIGATING OBSTACLES IN ENTERPRISE TEST OBSERVABILITY ADOPTION Adopting test observability at an enterprise level comes with its own set of challenges: Cost Control: Implementing test observability tools might be considered an overhead expense. While the initial costs can be high, the potential long-term benefits justify the investment in improving testing efficiency and reducing maintenance costs. Fragmented Test Infrastructure: Test environments spread across on-premises, cloud, and hybrid systems, along with fragmented infrastructure using multiple tools for different testing types, make it challenging to correlate and monitor results, leading to incomplete or inaccurate insights. High Complexity Of Implementation: Integrating test observability tools into CI/CD pipelines and infrastructure can be complex and require extensive configuration to align with processes. Alert Fatigue: Information overload may lead to alert fatigue from the excessive notifications generated during testing, which may cause missed issues and burnout. AI -native testing capabilities can help reduce noise by limiting alerts to highlight critical issues only. Skills Gaps And Complexity: Many organizations struggle to effectively utilize test observability tools due to a lack of expertise. WHY ENTERPRISES MUST PRIORITIZE TEST OBSERVABILITY IN A DIGITAL-FIRST WORLD Any digital-first business, whether SMB or enterprise, must double down on test observability. The stakes are high. Take the global outage that Amazon experienced in 2021. A 59-minute disruption caused by website inaccessibility led to an estimated $34 million loss in sales, and highlighted the vulnerability of even the most robust digital platforms. Test observability tools help enterprises quickly identify root causes, saving time and resources. However, 28.7% of organizations still lack a dedicated test intelligence infrastructure, according to the Future of QA survey. Here's how test observability is reshaping enterprise software delivery: Faster Defect Identification And Resolution: With real-time feedback and integrated analytics, test observability accelerates development pipelines. Enterprises with strong observability are 2.8x faster at detecting issues (MTTD) and 2.3x quicker at resolving them (MTTR), thereby reducing costly delays. Flaky Test Identification: Automatically detecting and getting notified of flaky tests based on a predefined flaky test threshold or percentage enables teams to quickly address issues caused by environmental factors or dependencies, which enhances test reliability. Predicting Failures: AI-powered observability systems analyze historical data to predict potential test failures, and help teams focus on high-risk areas early. Intelligent Test Selection: By focusing on the most relevant tests based on recent code changes, these tools minimize unnecessary test cycles and optimize both time and resources. Automated Incident Summary: AI-powered test observability systems automatically generate incident summaries and allow teams to quickly identify and address the most critical issues. Maximized ROI: Test observability enables early issue detection and performance improvements, leading to a 4X return on investment in the long term. Improved Software Quality: Enterprises using test observability see a 73% improvement in software quality by reducing downtime and detecting issues in real time, all of which leads to better system reliability. PRACTICAL IMPLEMENTATION STRATEGIES FOR TEST OBSERVABILITY Here's how enterprises can implement test observability effectively: Define Objectives And Metrics: Set clear goals for observability, such as improving test coverage or reducing incident resolution time, and align them with business objectives to track success. Select The Right Tools: Choose test observability tools that integrate with existing testing infrastructure and CI/CD pipelines to provide the needed visibility and insights. Centralized Test Data: Consolidate test data into a unified dashboard for a comprehensive view of the testing process that can help teams make data-driven decisions. Enable Real-Time Monitoring: Implement real-time monitoring to quickly identify issues and leverage AI and machine learning for automated anomaly detection and prioritization. Alerting: Set up alerting mechanisms to notify teams of critical issues or test failures in real time. This ensures quick responses and minimizing downtime. Continuous Improvement: Ensure continuous optimization by regularly assessing and improving test strategies based on insights from observability tools. THE PATH FORWARD Enterprise testing has become less about checking boxes and more about risk mitigation, user trust, and innovation velocity. Test observability eliminates the guesswork. It turns data into decisions. As release velocity and reliability become critical to business success, it unites QA, Dev, and Ops with a shared quality mindset. The question is no longer, 'Should we invest in test observability?' but rather, 'Can we afford not to?'
Entrepreneur
13-06-2025
- Business
- Entrepreneur
Streaming Without Compromise: Head of Reliability Engineering on SRE, Microservices, and Scalable Architecture
Alexandr Hacicheant, Head of Reliability Engineering, ensures system stability and fault tolerance. He implements practices that minimize risks, let developers sleep peacefully at night, and simultaneously optimize business resources. Opinions expressed by Entrepreneur contributors are their own. You're reading Entrepreneur India, an international franchise of Entrepreneur Media. Mayflower is a global FunTech company taking the entertainment industry to the next level. Its flagship product is a live-streaming platform. Mayflower's CDN processes over 10,000 parallel input streams and distributes approximately 100,000 output streams. Downtime is unacceptable—every delay means losing users. Alexandr Hacicheant, Head of Reliability Engineering, ensures system stability and fault tolerance. He implements practices that minimize risks, let developers sleep peacefully at night, and simultaneously optimize business resources. "My job is to ensure the system doesn't just work—it must withstand peak loads and recover quickly from failures," he explains. Alexandr shares his career journey, key projects, and best practices—SRE, microservices, and minimizing latency in live streaming. From Developer to CTO Before joining Mayflower, Alexandr worked remotely for several years with Russian and international companies as a backend developer. He specialized in solving critical issues—whether implementing urgent features or fixing system failures. "For example, when promo campaigns caused a surge in users, I had to ensure services could handle the traffic spike," he recalls. In 2016, he moved to Cyprus and joined Mayflower. Starting as an engineer on a 15-person team developing and testing new features, he shifted focus to architecture optimization and bottleneck elimination as scaling challenges emerged. "I looked for ways to scale not by buying more servers but by improving our tech stack's efficiency," he says. One of his initiatives was dedicating 30% of team time to technical debt. This improved system stability, reduced incidents, and enhanced engineers' work-life balance. "Before, employees often woke up at night to fix issues. We started addressing root causes—not just symptoms." After several years, Hacicheant became CTO, overseeing technical growth: leading tech leads, coordinating backend/client development, ML teams, and DevOps. Under his guidance, security improved—including forming a dedicated infosec team (previously handled by infrastructure teams). Alexandr and his team implemented automated vulnerability detection pipelines (SAST and SCA solutions) to scan project source code before production deployment, while establishing streamlined remediation processes. Additionally, they deployed a centralized access management system for company resources. Furthermore, Hacicheant spearheaded a company-wide security awareness initiative through interactive training sessions and meetups. Under Alexandr's leadership, the development and operations teams also dedicated significant efforts to building a cloud platform and migrating applications to cloud infrastructure. This transition delivered substantial advantages in computational resource management and allocation, automated scaling and failure recovery, along with accelerated application and service deployment speeds compared to traditional physical server or virtual machine environments. Resilience at the Architectural Level After approximately three years as CTO, the expert took over leadership of Reliability Engineering. Currently, Alexandr's primary goal is to ensure service fault tolerance while establishing robust failure recovery and analysis processes. "Ideally, outages shouldn't occur. But when they do, we need to identify the issue and recover quickly," he explains. System failures can stem from various causes, often due to suboptimal code or hastily chosen architectures. Alexandr's team identifies failure root causes during profiling and analysis, documents best practices, shares them company-wide, and automates detection of similar future issues. For example, they've implemented load-testing pipelines to evaluate code performance under multi-user loads and assess service readiness for peak traffic. Under Alexandr's guidance, the team established a three-tier technical support system: - First line: 24/7 monitoring team - Second line: SRE team comprising developers and DevOps engineers for specific services - Third line: Team leads and technical leads with broad expertise "Initially, incidents frequently escalated to the third line. But as the first and second lines gained experience—writing postmortems (documents detailing timelines, conclusions, and preventive measures) and action items—escalations dropped dramatically," Hacicheant emphasizes. Collectively, these innovations reduced major incidents from weekly occurrences to no more than monthly. A current priority for Alexandr is decomposing the monolith into microservices. Monolithic architecture—a single, tightly integrated system—simplifies development but severely hinders scaling and partial updates. Microservices, conversely, break applications into independent modules, each handling specific functions and deployable separately, communicating via APIs. "Monoliths work initially when validating hypotheses quickly. But they eventually impede scaling, and a single failure can disrupt half your business processes," Alexandr explains. For this transition, he oversees technology selection and architectural decisions. The expert emphasizes that such decisions must always strike a balance between business requirements, technical considerations, and available resources - both human and temporal. Chasing after new technologies and ideas isn't always the optimal approach. "When testing a business hypothesis, it's better to leverage existing solutions and quickly assemble a makeshift system that meets requirements using the tech stack your team already knows. There will be time to refine architectural solutions once the service proves its business value. Otherwise, all resources that could have been invested in validating other hypotheses will be wasted," Hacicheant stresses. According to the expert, one of the key recommendations before implementing microservice architecture is to thoroughly understand the system's business processes. This enables more precise definition of service boundaries and their decomposition. Additionally, services should maintain loose coupling; issues with one service shouldn't cause the entire system to degrade. This can be achieved through approaches like asynchronous communication and Event-Driven Architectures. Simultaneously, Hacicheant is enhancing monitoring and improving service observability so the system can automatically identify where failures occur and alert the appropriate personnel. "The ultimate goal isn't manual log collection, but building an intelligent alert system that can independently diagnose what went wrong and where, then precisely notify the responsible team." SRE: A Systemic Approach to Failures Under Alexandr's leadership, Mayflower adopted Site Reliability Engineering (SRE) practices—an engineering methodology balancing feature velocity with service stability. Core principles acknowledge that failures are inevitable; teams must minimize impact, automate responses, and prevent recurrences. At the core of SRE are three key concepts: SLO (Service Level Objective) — the target levels of service availability or performance; SLI (Service Level Indicator) — the metrics used to measure these targets; and the error budget — the acceptable threshold of failure. If this threshold is exceeded, the system automatically triggers an alert, and the CI/CD pipeline may suspend further deployments. SRE practice includes the preparation of runbooks — step-by-step guides for resolving common issues. After an incident, a postmortem is created. In addition, SRE promotes gradual rollouts: updates are initially delivered to 5–10% of users, and only if the system remains stable are they rolled out more broadly. If issues are detected, tools like Spinnaker can automatically roll back the changes. This approach helps companies reduce the number of outages, accelerate recovery times, and improve user satisfaction. Instead of chaotic late-night firefighting and stress, SRE brings structure, transparency, and predictability. As Alexandr emphasizes, implementing SRE provides not only technical but also cultural benefits: it enhances collaboration across teams and reduces burnout.
Forbes
12-05-2025
- Business
- Forbes
How The Microservices Vs. Monoliths Debate Is Damaging Your Business
Beyond The Architecture Cage Match: How The Microservices Vs. Monoliths Debate Is Damaging Your ... More Business In the red corner, weighing in with independent scalability and distributed complexity: microservices! In the blue corner, the reigning legacy champion, with its infamous deployment challenges: the monolith! For years, architects and technology executives have watched this architectural cage match with bated breath. Technology forums buzzed with trash talk from both sides. Conference speakers built careers championing one approach while demonizing the other. Vendors sold middleware solutions promising to crown you champion — if only you'd pick their preferred fighter. But what if we told you that this entire spectacle was all just a waste of time? The truth? Your organization shouldn't pick a single winner in this so-called battle. You need different solutions tailored to specific contexts. The industry landscape is littered with both cautionary tales and success stories that illustrate architectural tension. Consider how Segment, the customer data platform, famously documented its journey from monolith to microservices and then partially back again. The engineering team initially split Segment's platform into over 100 microservices in pursuit of scalability, only to face what they called 'death by a thousand microservices.' The team eventually consolidated back to a more balanced approach after experiencing mounting operational complexity and debugging challenges that outweighed the benefits. On the flip side, many established enterprises cling to aging monoliths long past their expiration dates. When retail giant Target began its digital transformation, it realized that its monolithic architecture couldn't deliver the agility needed to compete with Amazon. Its pragmatic phased approach to modernization — selectively decomposing components while maintaining core systems — helped Target achieve an impressive digital turnaround without falling into either extreme of the architectural spectrum. The lesson from both scenarios? Architectural decisions driven by trends rather than business context frequently lead organizations astray. Architecture is about weighing trade-offs, not adhering to dogma. As we enter a new era of digital acceleration, the organizations pulling ahead aren't arguing about monoliths versus microservices. They're pragmatically applying architectural patterns where they make sense, modernizing incrementally where they see concrete benefits, and staying focused on delivering business value. So go beyond the battle royale, put down the architectural dogma, and start asking better questions about what your specific context, organization, and business needs demand. The true champion of modern software architecture isn't a particular pattern — it's the pragmatic, business-focused approach that delivers real results in your unique context. Because in the real world, the only architectural approach fighter that truly wins is the one that helps your business succeed. This post was written by Principal Analyst Devin Dickerson and Principal Analyst David Mooter and it originally appeared here.
