Reliability Enablers

What if the hardest part of reliability has nothing to do with tooling or automation? Jennifer Petoff explains why real reliability comes from the human workflows wrapped around the engineering work. Everyone seems to think AI will automate reliability away. I keep hearing the same story: “Our tooling will catch it.” “Copilots will reduce operational load.” “Automation will mitigate incidents before they happen.” But here’s a hard truth to swallow: AI only automates the mechanical parts of reliability — the machine in the machine. The hard parts haven’t changed at all. You still need teams with clarity on system boundaries.You still need consistent approaches to resolution.You still need postmortems that drive learning rather than blame. AI doesn’t fix any of that. If anything, it exposes every organizational gap we’ve been ignoring. And that’s exactly why I wanted today’s guest on. Jennifer Petoff is Director of Program Management for Google Cloud Platform and Technical Infrastructure education. Every day, she works with SREs at Google, as well as with SREs at other companies through her public speaking and Google Cloud Customer engagements. Even if you have never touched GCP, you have still been influenced by her work at some point in your SRE career. She is co-editor of Google’s original Site Reliability Engineering book from 2016. Yeah, that one! It was my immense pleasure to have her join me to discuss the internal dynamics behind successful reliability initiatives. Here are 5 highlights from our talk: 3 issues stifling individual SREs’ work To start, I wanted to know from Jennifer the kinds of challenges she has seen individual SREs face when attempting to introduce or reinforce reliability improvements within their teams or the broader organization. She categorized these challenges into 3 main categories * Cultural issues (with a look into Westrum’s typology of organizational culture) * Insufficient buy-in from stakeholders * Inability to communicate the value of reliability work Organizations with generative cultures have 30% better organizational performance. A key highlight from this topic came from her look at DORA research, an annual survey of thousands of tech professionals and the research upon which the book Accelerate is based. It showed that organizations with generative cultures have 30% better organizational performance. In other words, you can have the best technology, tools, and processes to get good results, but culture further raises the bar. A generative culture also makes it easier to implement the more technical aspects of DevOps or SRE that are associated with improved organizational performance. Hands-on is the best kind of training We then explored structured approaches that ensure consistency, build capability, and deliberately shape reliability culture. As they say – Culture eats strategy for breakfast! One key example Jennifer gave was the hands-on approach they take at Google. She believes that adults learn by doing. In other words, SREs gain confidence by doing hands-on work. Where possible, training programs should move away from passive listening to lectures toward hands-on exercises that mimic real SRE work, especially troubleshooting. One specific exercise that Google has built internally is Simulating Production Breakages. Engineers undergoing that training have a chance to troubleshoot a real system built for this purpose in a safe environment. The results have been profound, with a tremendous amount of confidence that Jennifer’s team saw in survey results. This confidence is focused on job-related behaviors, which when repeated over time reinforce that culture of reliability. Reliability is mandatory for everybody Another thing Jennifer told me Google did differently was making reliability a mandatory part of every engineer’s curriculum, not only SREs. When we first spun up the SRE Education team, our focus was squarely on our SREs. However, that’s...

A new or growing SRE team. A copy of the book. A company that says it cares about reliability. What happens next? Usually… not much. In this episode, I sit down with Dave O’Connor, a 16-year Google SRE veteran, to talk about what happens when organizations cargo-cult reliability practices without understanding the context they were born in. You might know him for his self-deprecating wit and legendary USENIX blurb about being “complicit in the development of the SRE function.” This one’s a treat — less “here’s a shiny new tool” and more “here’s what reliability actually looks like when you’ve seen it all.” ✨ No vendor plugs from Dave at all, just a good old-fashioned chat about what works and what doesn’t. Here’s what we dive into: * The adoption trap: Why SRE efforts often fail before they begin—especially when new hires care more about reliability than the org ever intended. * The SRE book dilemma: Dave’s take on why following the SRE book chapter-by-chapter is a trap for most companies (and what to do instead). * The cost of “caring too much”: How engineers burn out trying to force reliability into places it was never funded to live. * You build it, you run it (but should you?): Not everyone’s cut out for incident command—and why pretending otherwise sets teams up to fail. * Buying vs. building: The real reason even conservative enterprises are turning into software shops — and the reliability nightmare that follows. We also discuss the evolving role of reliability in organizations today, from being mistaken for “just ops” to becoming a strategic investment (when done right). Dave's seen the waves come and go in SRE — and he's still optimistic. That alone is worth a listen. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

I know it’s already six months into 2025, but we recorded this almost three months ago. I’ve been busy with my foray into the world of tech consulting and training —and, well, editing these podcast episodes takes time and care. This episode was prompted by the 2025 Catchpoint SRE Report, which dropped some damning but all-too-familiar findings: * 53% of orgs still define reliability as uptime only, ignoring degraded experience and hidden toil * Manual effort is creeping back in, reversing five years of automation gains * 41% of engineers feel pressure to ship fast, even when it undermines long-term stability To unpack what this actually means inside organizations, I sat down with Sebastian Vietz, Director of Reliability Engineering at Compass Digital and co-host of the Reliability Enablers podcast. Sebastian doesn’t just talk about technical fixes — he focuses on the organizational frictions that stall change, burn out engineers, and leave “reliability” as a slide deck instead of a lived practice. We dig into: * How SREs get stuck as messengers of inconvenient truths * What it really takes to move from advocacy to adoption — without turning your whole org into a cost center * Why tech is more like milk than wine (Sebastian explains) * And how SREs can strengthen—not compete with—security, risk, and compliance teams This one’s for anyone tired of reliability theatrics. No kumbaya around K8s here. Just an exploration of the messy, human work behind making systems and teams more resilient. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Most teams talk about reliability with a margin for error. “What’s our SLO? What’s our budget for failure?” But in the energy sector? There is no acceptable downtime. Not even a little. In this episode, I talk with Wade Harris, Director of FAST Engineering in Australia, who’s spent 15+ years designing and rolling out monitoring and control systems for critical energy infrastructure like power stations, solar farms, SCADA networks, you name it. What makes this episode different is that Wade isn’t a reliability engineer by title, but it’s baked into everything his team touches. And that matters more than ever as software creeps deeper into operational technology (OT), and the cloud tries to stake its claim in critical systems. We cover: * Why 100% uptime is the minimum bar, not a stretch goal * How the rise of renewables has increased system complexity — and what that means for monitoring * Why bespoke integration and SCADA spaghetti are still normal (and here to stay) * The reality of cloud risk in critical infrastructure (“the cloud is just someone else’s computer”) * What software engineers need to understand if they want their products used in serious environments This isn’t about observability dashboards or DevOps rituals. This is reliability when the lights go out and people risk getting hurt if you get it wrong. And it’s a reminder: not every system lives in a feature-driven world. Some systems just have to work. Always. No matter what. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Exploring how to manage observability tool sprawl, reduce costs, and leverage AI to make smarter, data-driven decisions. It's been a hot minute since the last episode of the Reliability Enablers podcast. Sebastian and I have been working on a few things in our realms. On a personal and work front, I’ve been to over 25 cities in the last 3 months and need a breather. Meanwhile, listen to this interesting vendor, Ruchir Jha from Cardinal, working on the cutting edge of o11y to help reduce costs from spiraling out of control. (To the skeptics, he did not pay me for this episode) Here’s an AI-generated summary of what you can expect in our conversation: In this conversation, we explore cutting-edge approaches to FinOps i.e. cost optimization for observability. You'll hear about three pressing topics: * Managing Tool Sprawl: Insights into the common challenge of juggling 5-15 tools and how to identify which ones deliver real value. * Reducing Observability Costs: Techniques to track and trim waste, including how to uncover cost hotspots like overused or redundant metrics. * AI for Observability Decisions: Practical ways AI can simplify complex data, empowering non-technical stakeholders to make informed decisions. We also touch on the balance between open-source solutions like OpenTelemetry and commercial observability tools. Learn how these strategies, informed by Ruchir's experience at Netflix, can help streamline observability operations and cut costs without sacrificing reliability. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Andrew Tunall is a product engineering leader focused on pushing the boundaries of reliability with a current focus on mobile observability. Using his experience from AWS and New Relic, he’s vocal about the need for a more user-focused observability, especially in mobile, where traditional practices fall short. * Career Journey and Current Role: Andrew Tunall, now at Embrace, a mobile observability startup in Portland, Oregon, started his journey at AWS before moving to New Relic. He shifted to a smaller, Series B company to learn beyond what corporate America offered. * Specialization in Mobile Observability: At Embrace, Andrew and his colleagues build tools for consumer mobile apps, helping engineers, SREs, and DevOps teams integrate observability directly into their workflows. * Gap in Mobile Observability: Observability for mobile apps is still developing, with early tools like Crashlytics only covering basic crash reporting. Andrew highlights that more nuanced data on app performance, crucial to user experience, is often missed. * Motivation for User-Centric Tools: Leaving “big observability” to focus on mobile, Andrew prioritizes tools that directly enhance user experience rather than backend metrics, aiming to be closer to end-users. * Mobile's Role as a Brand Touchpoint: He emphasizes that for many brands, the primary consumer interaction happens on mobile. Observability needs to account for this by focusing on user experience in the app, not just backend performance. * Challenges in Measuring Mobile Reliability: Traditional observability emphasizes backend uptime, but Andrew sees a gap in capturing issues that affect user experience on mobile, underscoring the need for end-to-end observability. * Observability Over-Focused on Backend Systems: Andrew points out that “big observability” has largely catered to backend engineers due to the immense complexity of backend systems with microservices and Kubernetes. Despite mobile being a primary interface for apps like Facebook and Instagram, observability tools for mobile lag behind backend-focused solutions. * Lack of Mobile Engineering Leadership in Observability: Reflecting on a former Meta product manager’s observations, Andrew highlights the lack of VPs from mobile backgrounds, which has left a gap in observability practices for mobile-specific challenges. This gap stems partly from frontend engineers often seeing themselves as creators rather than operators, unlike backend teams. * OpenTelemetry’s Limitations in Mobile: While OpenTelemetry provides basic instrumentation, it falls short in mobile due to limited SDK support for languages like Kotlin and frameworks like Unity, React Native, and Flutter. Andrew emphasizes the challenges of adapting OpenTelemetry to mobile, where app-specific factors like memory consumption don’t align with traditional time-based observability. * SREs as Connective Tissue: Andrew views Site Reliability Engineers (SREs) as essential in bridging backend observability practices with frontend user experience needs. Whether through service level objectives (SLOs) or similar metrics, SREs help ensure that backend metrics translate into positive end-user experiences—a critical factor in retaining app users. * Amazon’s Operational Readiness Review: Drawing from his experience at AWS, Andrew values Amazon’s practice of operational readiness reviews before launching new services. These reviews encourage teams to anticipate possible failures or user experience issues, weighing risks carefully to maintain reliability while allowing innovation. * Shifting Focus to “Answerability” in Observability: For Andrew, the goal of observability should evolve toward “answerability,” where systems provide engineers with actionable answers rather than mere data. He envisions a future where automation or AI could handle repetitive tasks, allowing engineers to focus on enhancing user experiences instead of troubleshooting. This is a public episode. If...

Andrew Fong’s take on engineering cuts through the usual role labels, urging teams to start with the problem they’re solving instead of locking into rigid job titles. He sees reliability, inclusivity, and efficiency as the real drivers of good engineering. In his view, SRE is all about keeping systems reliable and healthy, while platform engineering is geared toward speed, developer enablement, and keeping costs in check. It’s a values-first, practical approach to tackling tough challenges that engineers face every day. Here’s a slightly deeper dive into the concepts we discussed: * Career and Evolution in Tech: Andrew shares his journey through various roles, from early SRE at Youtube to VP of Infrastructure at Dropbox to Director of Engineering at Databricks, with extensive experience in infrastructure through three distinct eras of the internet. He emphasized the transition from early infrastructure roles into specialized SRE functions, noting the rise of SRE as a formalized role and the evolution of responsibilities within it. * Building Prodvana and the Future of SRE: As CEO of startup, Prodvana, they're focused on an "intelligent delivery system" designed to simplify production management for engineers, addressing cognitive overload. They highlight SRE as a field facing new demands due to AI, discussing insights shared with Niall Murphy and Corey Bertram around AI's potential in the space, distinguishing it from "web three" hype, and affirming that while AI will transform SRE, it will not eliminate it. * Challenges of Migration and Integration: Reflecting on experiences at YouTube post-acquisition by Google, the speaker discusses the challenges of migrating YouTube’s infrastructure onto Google’s proprietary, non-thread-safe systems. This required extensive adaptation and “glue code,” offering insights into the intricacies and sometimes rigid culture of Google’s engineering approach at that time. * SRE’s Shift Toward Reliability as a Core Feature: The speaker describes how SRE has shifted from system-level automation to application reliability, with growing recognition that reliability is a user-facing feature. They emphasize that leadership buy-in and cultural support are essential for organizations to evolve beyond reactive incident response to proactive, reliability-focused SRE practices. * Organizational Culture and Leadership Influence: Leadership’s role in SRE success is highlighted as crucial, with examples from Dropbox and Google emphasizing that strong, supportive leadership can shape positive, reliability-centered cultures. The speaker advises engineers to gauge leadership attitudes towards SRE during job interviews to find environments where reliability is valued over mere incident response. * Outcome-Focused Work Over Titles: Emphasis on assembling the right team based on skills, not titles, to solve technical problems effectively. Titles often distract from focusing on outcomes, and fostering a problem-solving culture over role-based thinking accelerates teamwork and results. * Engineers as Problem Solvers: Engineers, especially natural ones, generally resist job boundaries and focus on solving problems rather than sticking rigidly to job descriptions. This echoes how iconic engineers like Steve Jobs valued versatility over predefined roles. * Culture as Core Values: Organizational culture should be driven by core values like reliability, efficiency, and inclusivity rather than rigid processes or roles. For instance, Dropbox's infrastructure culture emphasized being a “force multiplier” to sustain product velocity, an approach that ensured values were integrated into every decision. * Balancing SRE and Platform Priorities: The fundamental difference between SRE (Site Reliability Engineering) and platform engineering is their focus: SRE prioritizes reliability, while platform engineering is geared toward increasing velocity or reducing costs. Leaders must be cautious when assigning both roles...

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Here’s what we covered: Defining Platform Engineering * Platform engineering: Building compelling internal products to help teams reuse capabilities with less coordination. * Cloud computing connection: Enterprises can now compose platforms from cloud services, creating mature, internal products for all engineering personas. Ankit’s career journey * Didn't choose platform engineering; it found him. * Early start in programming (since age 11). * Transitioned from a product engineer mindset to building internal tools and platforms. * Key experience across startups, the public sector, unicorn companies, and private cloud projects. Singapore Public Sector Experience * Public sector: Highly advanced digital services (e.g., identity services for tax, housing). * Exciting environment: Software development in Singapore’s public sector is fast-paced and digitally progressive. Platform Engineering Turf Wars * Turf wars: Debate among DevOps, SRE, and platform engineering. * DevOps: Collaboration between dev and ops to think systemically. * SRE: Operations done the software engineering way. * Platform engineering: Delivering operational services as internal, self-service products. Dysfunctional Team Interactions * Issue: Requiring tickets to get work done creates bottlenecks. * Ideal state: Teams should be able to work autonomously without raising tickets. * Spectrum of dysfunction: From one ticket for one service to multiple tickets across teams leading to delays and misconfigurations. Quadrant Model (Autonomy vs. Cognitive Load) * Challenge: Balancing user autonomy with managing cognitive load. * Goal: Enable product teams with autonomy while managing cognitive load. * Solution: Platforms should abstract unnecessary complexity while still giving teams the autonomy to operate independently. How it pans out * Low autonomy, low cognitive load: Dependent on platform teams but a simple process. * Low autonomy, high cognitive load: Requires interacting with multiple teams and understanding technical details (worst case). * High autonomy, high cognitive load: Teams have full access (e.g., AWS accounts) but face infrastructure burden and fragmentation. * High autonomy, low cognitive load: Ideal situation—teams get what they need quickly without detailed knowledge. Shift from Product Thinking to Cognitive Load * Cognitive load focus: More important than just product thinking—consider the human experience when using the system. * Team Topologies: Mentioned as a key reference on this concept of cognitive load management. Platform as a Product Mindset * Collaboration: Building the platform in close collaboration with initial users (pilot teams) is crucial for success. * Product Management: Essential to have a product manager or team dedicated to communication, user journeys, and internal marketing. Self-Service as a Platform Requirement * Definition: Users should easily discover, understand, and use platform capabilities without human intervention. * User Testing: Watch how users interact with the platform to understand stumbling points and improve the self-service experience. Platform Team Cognitive Load * Burnout Prevention: Platform engineers need low cognitive load as well. Moving from a reactive (ticket-based) model to a proactive, self-service approach can reduce the strain. * Proactive Approach: Self-service models allow platform teams to prioritize development and avoid being overwhelmed by constant requests. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Why many copy Google’s monitoring team setup * Google’s Influence. Google played a key role in defining the concept of software reliability. * Success in Reliability. Few can dispute Google’s ability to ensure high levels of reliability and its ability to share useful ways to improve it in other settings BUT there’s a problem: * It’s not always replicable. While Google's practices are admired, they may not be a perfect fit for every team. What is Google’s monitoring approach within teams? Here’s the thing that Google does: * Google assigns one or two people per team to manage monitoring. * Even with centralized infrastructure, a dedicated person handles monitoring. * Many organizations use a separate observability team, unlike Google's integrated approach If your org is large enough and prioritizes reliability highly enough, you might find it feasible to follow Google’s model to the tee. Otherwise, a centralized team with occasional “embedded x engineer” secondments might be more effective. Can your team mimic Google’s model? Here are a few things you should factor in: Size matters Google's model works because of its scale and technical complexity. Many organizations don’t have the size, resources, or technology to replicate this. What are the options for your team? Dedicated monitoring team (very popular but $$$) If you have the resources, you might create a dedicated observability team. This might call for a ~$500k+ personnel budget so it’s not something that a startup or SME can easily justify. Dedicate SREs to monitoring work (effective but difficult to manage) You might do this on rotation or make an SRE permanently “responsible for all monitoring matters”. Putting SREs on permanent tasks might lead to burnout as it might not suit their goals, and rotation work requires effective planning. Internal monitoring experts (useful but hard capability) One or more engineers within teams could take on monitoring/observability responsibilities as needed and support the team’s needs. This should be how we get monitoring work done, but it’s hard to get volunteers across a majority of teams. Transitioning monitoring from project work to maintenance 2 distinct phases Initial Setup (the “project”) SREs may help set up the monitoring/observability infrastructure. Since they have breadth of knowledge across systems, they can help connect disparate services and instrument applications effectively. Post-project phase (“keep the lights on”) Once the system is up, the focus shifts from project mode to ongoing operational tasks. But who will do that? Who will maintain the monitoring system? Answer: usually not the same team After the project phase, a new set of people—often different from the original team—typically handles maintenance. Options to consider (once again) * Spin up a monitoring/observability team. Create a dedicated team for observability infrastructure. * Take a decentralized approach. Engineers across various teams take on observability roles as part of their regular duties. * Internal monitoring/observability experts. They can take responsibility for monitoring and ensure best practices are followed. The key thing to remember here is… Adapt to Your Organizational Context One size doesn’t fit all Google's model may not work for everyone. Tailor your approach based on your organization’s specific needs. The core principle to keep in mind As long as people understand why monitoring/observability matters and pay attention to it, you're on the right track. Work according to engineer awareness If engineers within product and other non-operations teams are aware of monitoring: You can attempt to **decentralize the effort** and involve more team members. If awareness or interest is low: consider **dedicated observability roles** or an SRE team to ensure monitoring gets the attention it needs. In conclusion There’s no universal solution. Whether you centralize or decentralize monitoring...

Monitoring in the software engineering world continues to grapple with poor signal-to-noise ratios. It’s a challenge that’s been around since the beginning of software development and will persist for years to come. The core issue is the overwhelming noise from non-essential data, which floods systems with useless alerts. This interrupts workflows, affects personal time, and even disrupts sleep. Sebastian dove into this problem, highlighting that the issue isn't just about having meaningless pages but also the struggle to find valuable information amidst the noise. When legitimate alerts get lost in a sea of irrelevant data, pinpointing the root cause becomes exceptionally hard. Sebastian proposes a fundamental fix for this data overload: be deliberate with the data you emit. When instrumenting your systems, be intentional about what data you collect and transport. Overloading with irrelevant information makes it tough to isolate critical alerts and find the one piece of data that indicates a problem. To combat this, focus on: * Being Deliberate with Data. Make sure that every piece of telemetry data serves a clear purpose and aligns with your observability goals. * Filtering Data Effectively. Improve how you filter incoming data to eliminate less relevant information and retain what's crucial. * Refining Alerts. Optimize alert rules such as creating tiered alerts to distinguish between critical issues and minor warnings. Dan Ravenstone, who leads platform at Top Hat, discussed “triaging alerts” recently. He shared that managing millions of alerts, often filled with noise, is a significant issue. His advice: scrutinize alerts for value, ensuring they meet the criteria of a good alert, and discard those that don’t impact the user journey. According to Dan, the anatomy of a good alert includes: * A run book * A defined priority level * A corresponding dashboard * Consistent labels and tags * Clear escalation paths and ownership To elevate your approach, consider using aggregation and correlation techniques to link otherwise disconnected data, making it easier to uncover patterns and root causes. The learning point is simple: aim for quality over quantity. By refining your data practices and focusing on what's truly valuable, you can enhance the signal-to-noise ratio, ultimately allowing more time for deep work rather than constantly managing incidents. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

The question then condenses down to: Can technical leads support reliability work? Yes, they can! Anemari has been a technical lead for years — even spending a few years doing that at the coveted consultancy, Thoughtworks — and now coaches others. She and I discussed the link between this role and software reliability. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

We're already well into 2024 and it’s sad that people still have enough fuel to complain about various aspects of their engineering life. DORA seems to be turning into one of those problem areas. Not at every organization, but some places are turning it into a case of “hitting metrics” without caring for the underlying capabilities and conversations. Nathen Harvey is no stranger to this problem. He used to talk a lot about SRE at Google as a developer advocate. Then, he became the lead advocate for DORA when Google acquired it in 2018. His focus has been on questions like: How do we help teams get better at delivering and operating software? You and I can agree that this is an important question to ask. I’d listen to what he has to say about DORA because he’s got a wealth of experience behind him, having also run community engineering at Chef Software. Before we continue, let’s explore What is DORA? in Nathen’s (paraphrased) words: DORA is a software research program that's been running since 2015. This research program looks to figure out: How do teams get good at delivering, operating, building, and running software? The researchers were able to draw out the concept of the metrics based on correlating teams that have good technology practices with highly robust software delivery outcomes. They found that this positively impacted organizational outcomes like profitability, revenue, and customer satisfaction. Essentially, all those things that matter to the business. One of the challenges the researchers found over the last decade was working out: how do you measure something like software delivery? It's not the same as a factory system where you can go and count the widgets that we're delivering necessarily. The unfortunate problem is that the factory mindset I think still leaks in. I’ve personally noted some silly metrics over the years like lines of code. Imagine being asked constantly: “How many lines of code did you write this week?” You might not have to imagine. It might be a reality for you. DORA’s researchers agreed that the factory mode of metrics cannot determine whether or not you are a productive engineer. They settled on and validated 4 key measures for software delivery performance. Nathen elaborated that 2 of these measures look at throughput: [Those] two [that] look at throughput really ask two questions: * How long does it take for a change of any kind, whether it's a code change, configuration change, whatever, a change to go from the developer's workstation. right through to production? And then the second question on throughput is: * How frequently are you updating production? In plain English, these 2 metrics are: * Deployment Frequency. How often code is deployed to production? This metric reflects the team's ability to deliver new features or updates quickly. * Lead Time for Changes: Measures the time it takes from code being committed to being deployed to production. Nathen recounted his experience of working at organizations that differed in how often they update production from once every six months to multiple times a day. They're both very different types of organizations, so their perspective on throughput metrics will be wildly different. This has some implications for the speed of software delivery. Of course, everyone wants to move faster, but there’s this other thing that comes in and that's stability. And so, the other two stability-oriented metrics look at: What happens when you do update production and... something's gone horribly wrong. “Yeah, we need to roll that back quickly or push a hot fix.” In plain English, they are: * Change Failure Rate: Measures the percentage of deployments that cause a failure in production (e.g., outages, bugs). * Failed Deployment Recovery Time: Measures how long it takes to recover from a failure in production. You might be thinking the same thing as me. These stability metrics might be a lot more interesting to...

We’ll explore 3 use cases for monitoring data. They are: * Analyzing long-term trends * Comparing over time or experiment groups * Conducting ad hoc retrospective analysis Analyzing long-term trends You can ask yourself a couple of simple questions as a starting point: * How big is my database? * How fast is the database growing? * How quickly is my user count growing? As you get comfortable with analyzing data for the simpler questions, you can start to analyze trends for less straightforward questions like: * How is the database performance evolving? Are there signs of degradation? * Is there consistent growth in data volume that may require future infrastructure adjustments? * How is overall resource utilization trending over time across different services? * How is the cost of cloud resources evolving, and what does that mean for budget forecasting? * Are there recurring patterns in downtime or service degradation, and what can be done to mitigate them? Sebastian mentioned that it's a part of observability he enjoys doing. I can understand why. It’s exciting to see how components are changing over a period and working out solutions before you end up in an incident response nightmare. Getting to effectively analyze the trends requires the right level of data retention settings. Because if you're throwing out your logs, traces, and metrics too early, you will not have enough historical data to do this kind of work. Doing this right means having the right amount of data in place to be able to analyze those trends over time, and that will of course depend on your desired period. Comparing over time or experiment groups Google’s definition You're comparing the data results for different groups that you want to compare and contrast. Using a few examples from the SRE (2016) book: * Are your queries faster in this version of this database or this version of that database? * How much better is my memcache hit rate with an extra node and is my site slower than it was last week? You're comparing it to different buckets of time and different types of products. A proper use case for comparing groups Sebastian did this particular use case recently because he had to compare two different technologies for deploying code: AWS Lambda vs AWS Fargate ECS. He took those two services and played around with different memories and different virtual CPUs. Then he ran different amounts of requests against those settings and tried to figure out which one was the better technology option most cost-effectively. His need for this went beyond engineering work but enabling product teams with the right decision-making data. He wrote out a knowledge base article to give them guidance for a more educated decision on the right AWS service. Having the data to compare the two services allowed him to answer questions like: * When should you be using either of these technologies? * What use cases would either technology be more suitable for? This data-based decision support is based mainly on monitoring or observability data. The idea of using the monitoring data to compare tools and technologies for guiding product teams is something I think reliability folk can gain a lot of value from doing. Conducting ad hoc retrospective analysis (debugging) Debugging is a bread-and-butter responsibility for anyone who is a software engineer of any level. It’s something that everybody should know a little bit more about than other tasks because there are very effective and also very ineffective ways of going about debugging. Monitoring data can help make the debugging process fall into the effective side. There are organizations where you have 10 different systems. In one system, you might get one fragmented piece of information. In another, you’ll get another fragment. And so on for all the different systems. And then you have to correlate these pieces of information in your head and hopefully, you get some clarity out of the fragments...

Shlomo Bielak is the Head of Engineering (Operational Excellence and Cloud) at Penn Interactive, an interactive gaming company. He’s dedicated much of his talk time at DevOps events to talk about a topic less covered at such technical events. A lot of what he said alluded to ways to become a more valuable engineer. I’ve broken them down into the following areas: * Avoid the heroic efforts * Mind + heart > Mind alone * Curiosity > Credentials * Experience > Certifications * Thinking for complexity When I saw him in Toronto, I thought he would talk about pre-production observability. It would only make sense after watching the previous presenter do a deep dive into Kubernetes tooling. But surprisingly, he started about culture and the need to prevent burnout among engineers — a topic that is as important today as it was 2 years ago when he did the talk. Here’s a look into Shlomo’s philosophy and the practices he champions. Avoid the heroic efforts Shlomo's perspective on heroics in engineering and operations challenges a traditional mindset that often glorifies excessive individual efforts at the cost of long-term sustainability. He emphasizes that relying on heroics — where individuals consistently go above and beyond to save the day — creates an unhealthy work environment. "We shouldn't be rewarding people for pulling all-nighters to save a project; we should be asking why those all-nighters were necessary in the first place." This approach not only burns out engineers but also masks underlying systemic issues that need to be addressed. So, instead of celebrating these heroic efforts, Shlomo advocates for creating processes and metrics that ensure smooth operations without the need for constant intervention. Mind + Heart > Mind alone One of the challenges Shlomo has faced recently is scaling his engineering organization amidst rapid growth. His approach to hiring is unique; he doesn’t just look for technical skills but prioritizes self-awareness and kindness. "Hiring with heart means looking for individuals who bring empathy and integrity to the team, not just expertise." When he joined The Score, a subsidiary of Penn Interactive, Shlomo immediately revamped the hiring practices by integrating the values above into the process. He favors role-playing scenarios over solely using behavioral interviews to evaluate candidates, as this method reveals how individuals might react in real production situations. I tend to agree with this approach as seeing how people are doing the work is more enlightening than asking them how they behaved in a past situation alone. Curiosity > credentials How it plays into career progression When it comes to career progression, Shlomo places little value on traditional markers like education or years of experience. Instead, he values adaptability, resilience, and curiosity. This last trait is the one he doubles down on. According to Shlomo, curiosity is the cornerstone of continuous growth and innovation. It’s not just about asking questions. It’s about fostering a mindset that constantly seeks to understand the 'why' behind everything. Shlomo advocates for a deep, insatiable curiosity that drives engineers to explore beyond the surface of problems, looking for underlying causes and potential improvements. He believes that this kind of curiosity is what separates good engineers from great ones, as it leads to discovering solutions that aren’t immediately obvious and pushes the boundaries of what’s possible. How it plays into teamwork For Shlomo, curiosity also plays a crucial role in building a cohesive and forward-thinking team. He encourages leaders to cultivate an environment where questions are welcomed, and no stone is left unturned. This approach not only sparks creativity but also ensures that everyone is engaged in a continuous learning process, which is vital in a field that evolves as rapidly as DevOps and SRE. By nurturing curiosity, teams can stay ahead of...

Incident response is an increasingly difficult area for organizations. Many teams end up paying a lot of money for incident management solutions. However, issues remain because processes supporting the incident response are not robust. Incident response software alone isn't going to fix bad incident processes. It's gonna help for sure. You need these incident management tools to manage the data and communications within the incident. But you also need to have effective processes and human-technology integration. Dr Ukis wrote in his Establishing SRE Foundations book about complex incident coordination and priority setting. According to Vladislav, at the beginning of your SRE journey, it’s not going to be focused on incident response in terms of setting up an incident response process, but more on core SRE artifacts like SLIs, availability measurement, SLOs, etc. And now we are safely investing more into the customer-facing features and things like this. So this is going to be the core SRE concepts. But then at some point, once you've got these things, more or less established in the organization. Understanding and Leveraging SLOs Once your Service Level Objectives (SLOs) are well-defined and refined over time, they should accurately reflect user and customer experiences. Your SLOs are no longer just initial metrics; they’ve been validated through production. Product managers should now be able to use this data to make informed decisions about feature prioritization. This foundational work is crucial because it sets the stage for integrating a formal incident response process effectively. Implementing a Formal Incident Response Before you overlay a formal incident response process, ensure that you have the cultural and technical groundwork in place. Without this, the process might not be as effective. When the foundational SLOs and organizational culture are strong, a well-structured incident response process can significantly enhance its effectiveness. Coordinating During Major Incidents When a significant incident occurs, detecting it through SLO breaches is just the beginning. You need a system in place to coordinate responses across multiple teams. Consider appointing incident commanders and coordinators, as recommended in PagerDuty’s documentation, to manage this coordination. Develop a lightweight process to guide how incidents are handled. Classifying Incidents Establish an incident classification scheme to differentiate between types of incidents. This scheme should include priorities such as Priority One, Priority Two, and Priority Three. Due to the inherently fuzzy nature of incidents, your classification system should also include guidelines for handling ambiguous cases. For instance, if uncertain whether an incident is Priority One or Two, default to Priority One. Deriving Actions from Incident Classification Based on the incident classification, outline specific actions. For example, Priority One incidents might require immediate involvement from an incident commander. They might take the following actions: * Create a communication channel, assemble relevant teams, and start coordination. * Simultaneously inform stakeholders according to their priority group. * Define stakeholder groups and establish protocols for notifying them as the situation evolves. Keep Incident Response Processes Simple and Accessible Ensure that your incident response process is concise and easily understandable. Ideally, it should fit on a single sheet of paper. Complexity can lead to confusion and inefficiencies, so aim for simplicity and clarity in your process diagram. This approach ensures that the process is practical and can be followed effectively during an incident. Preparing Your Organization An effective incident response process relies on an organization’s readiness for such rigor. Attempting to implement this process in an organization not yet mature enough may result in poor adherence during...

According to Vlad Ukis, there are a lot of enterprises around whose IT functions are organized around ITIL. What you use SRE for is something completely different. SRE is not for setting up the IT function. It is for enabling the product organization to operate online services reliably at scale. However, the problem is that many in the industry are NOT using SRE principles but instead handing over complex services to a more traditional IT function. Dr. Vladislav Ukis is well qualified to talk about reliability, being at Siemens Healthineers and leading 250 people globally to offer their cloud platform running off Microsoft Azure. We discussed key concepts from his book, Establishing SRE Foundations: A Step-by-Step Guide to Introducing Site Reliability Engineering in Software Delivery Organizations. Unlike other technical books in this field, Dr Ukis’ book is aimed at technology professionals who are beginners to the reliability journey. This is different from the Site Reliability Engineering (2016) book by Google, which covers all the bells and whistles that SRE encompasses. That book requires a degree of prior knowledge and also prior experience in the field. Vlad wanted to make it more accessible: What I did with my book is to say, ‘Okay, so now you've never done operations, but you now are thrown in the world of online services where you have to operate them. How do you get started?’ So this is what the book is for. So for people who want to learn how to get started in the world of operating online services. ITIL was originally developed by the UK government in the 80s to improve IT governance. It is best related to SRE through its service management and incident management components. But it’s for managing systems that are more predictable and can be handled through strict process control. Modern product delivery doesn’t have the luxury of bureaucratic levels of predictability that older IT services have. It requires a more engineer-oriented approach to solving problems/incidents and providing services. So how was Vlad’s experience bringing SRE into an organization that previously had run solely on the ITIL model? Siemens Healthineers for many years operated like a traditional software development organization. In other words, they were developing on-prem software, not cloud software. The company would ship the physical software product to its hospital customers and then those hospitals would have the software operated and supported by their IT departments. The change came about when Siemens Healthineers began to work on a new digital health platform, which would be cloud-based from the beginning. So they would no longer ship physical software in discs to customers, but provide online services in the cloud centrally for the customers to use. The early days were haphazardly done with the software deployed to the cloud with no major issues. Not many customers were on the cloud platform so the team could get away with “handcrafted operating procedures”. But as traffic and service count started to rise rapidly, the Healthineers team learned that they needed a more professional approach. They began to understand that their initial approach to operations could not continue as-is. This is when Vladislav began to drive SRE practices in the organization. This was a sub-30-minute conversation that covered a lot of ground that would be relevant to the needs of organizations looking to transition to product delivery of online services at scale. Have a listen. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Sonja Blignaut is a complexity expert. That might not sound relevant to incident response in reliability engineering. But it is! Our systems are becoming more complex and so are the resulting incidents. Learning about complexity can help reliability folk go into an incident with less anxiety, which we’ll explore in this episode. We'll explore the causes of complexity in incidents and how the Cynefin framework classifies incidents. We'll also deep dive into the concept of complexity itself and dispel a common issue where it gets mixed up with complicatedness. About Sonja Sonja is a co-founder of Complexity Fit and founder of More Beyond focusing on helping teams build capacity for sensemaking, collaboration, and wayfinding. She has a background in programming from her early career as a meteorologist, having worked in C and Fortran, and then progressing to working as a web developer. You can connect with Sonja to learn more about complexity via LinkedIn. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Have you got complete monitoring of your software in effect? Are you sure? Google's SREs break monitoring down to white box versus black box monitoring. It's not the same as internal versus external monitoring, which we'll explore further. We'll cover topics like: - (quickly) What is monitoring? - What is whitebox monitoring? - What is black box monitoring? - The rising importance of blackbox monitoring This is a concept from Chapter 6 (Monitoring Distributed Systems) of the Google SRE (2016) book. Chapter written by Rob Ewaschuk and edited by Betsy Beyer. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com

Jack Neely is a DevOps observability architect at Palo Alto Networks and has a few interesting ways of extracting value from o11y data. We crammed into just under 25 minutes ideas like these 7 takeaways: * Reasserting the Need to Monitor Four Golden Signals: Focus on latency, traffic, errors, and saturation for effective system monitoring and management. * Prioritize Customer Health: in Jack’s words, the 5th golden signal. Go beyond traditional metrics to monitor the health of your customers for a more comprehensive view of your system's impact. * Apply Mathematical Techniques: Incorporate advanced mathematical concepts, like the Nyquist Shannon law and T Digest algorithm, to enhance data accuracy and observability metrics. * Build Accurate Percentiles: Implement techniques to accurately reproduce percentiles from raw data to ensure reliable performance metrics. * Manage High Cardinality Data: Develop strategies to handle high cardinality data without overwhelming your resources, ensuring you extract valuable insights. * Standardize Log Records: Use readily available frameworks to emit standardized log records makes data easier to process and visualize. * Handle High-Velocity Data Efficiently: Develop methods for collecting and processing high-velocity data without incurring prohibitive costs. Watch Jack’s Monitorama talk via this link: https://vimeo.com/843996971 This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit read.srepath.com