Kaya Winn

Who needs self-service? In this article, Conduktor’s VP of Engineering, Matt Searle, talks about his own experiences attempting to balance developer autonomy alongside platform stability. His key insight? Govern Kafka, or risk havoc. Read his latest blog—and learn more about how Conduktor can help you implement self-service with guardrails. https://lnkd.in/ejWTRctu

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What does it take to build a successful engineering organization? 🚀 It’s not just strategy decks or scaling headcount. It’s the messy, behind-the-scenes work—building clarity when there’s chaos, balancing autonomy with alignment, and constantly evolving your architecture and culture as you grow. I’ve spent the last few years learning (and re-learning) this. So, I wrote a 3-part blog series to share some nuggets. If you're leading (or hoping to lead) at scale, I hope there's something useful in here for you. Would love your thoughts, and even more—your own lessons 🙂 #engineering #leadership #lessonslearned

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💡 AI doesn’t just change how code is written – it changes expectations and outcomes. In the final section of Jellyfish’s AI Adoption Guide, we explore how expectations for engineering leaders are evolving as AI becomes embedded across the SDLC. Because the organizations pulling ahead are treating AI as a transformation, not a tooling upgrade. Inside, you’ll explore: - Why AI adoption is quickly becoming a leadership mandate, not an option - How to balance ambitious goals with realistic expectations - What cultural investments are required to sustain long-term gains - How data can be used to align executives and engineering teams The teams that win in the AI era won’t just have better tools – they’ll have leaders who know how to guide change. Download today: https://lnkd.in/dut_6WpQ

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Spotify's engineering team built a background coding agent with Claude Agent SDK that handles complex migrations across their massive codebase. Engineers describe what they need in plain text—via Slack, even—and the agent takes it from there, running tests and opening pull requests. They're now merging 650+ agent-generated PRs into production each month. Read more about how they built it, and what else Claude is enabling at Spotify: https://lnkd.in/gVhivXu6

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Two of AWS’ Sr. Principal Engineers, Nicholas Matsakis and Marc Bowes, take us inside the development of Aurora DSQL in a fascinating new blog. The post shows how the team scaled write operations without two-phase commit, overcame garbage collection hurdles, and ultimately embraced Rust for both data and control planes. What I find particularly interesting is how the team's journey with Rust evolved. They started cautiously, using it for a single component, and ended up rewriting almost everything in Rust. It's a reminder that sometimes the hard choices - like adopting a new language - can unlock tremendous value. The full post is well worth your time. It's not just about database internals, but also about team dynamics, a culture of learning (and questioning established approaches), and making difficult architectural decisions. Read it here: https://lnkd.in/eMhxBu9e Now, go build!

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One thing the industry isn’t talking about enough: building trust with AI-powered observability. AI is getting more powerful, but in high pressure moments, engineers still need to stay in the loop. 👇 Hear more from Devin Cheevers, Director of Product.

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Crashes cost revenue. Poor #UX costs trust. #MobileTeams can't afford reactive workflows anymore, and your app is only as good as what your users experience. 😬 CIOReview spotlights how leading teams can detect, prioritize, and resolve issues with our AI-powered #MobileObservability before users ever notice.🛠️ 👉 Get the full insights: https://hubs.ly/Q03p4jYf0

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When it comes to incident response, we all talk about MTTR - but is it the right metric to use? 🤔 On this week’s #PageItToTheLimit, our Principal Site Reliability Engineer, Rich Lafferty, joins Senior Developer Advocate and host Mandi Walls to unpack how PagerDuty engineers think about measuring reliability. Tune in ➡️ bit.ly/3M1Hzfe

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🧢 Platform engineers aren’t just building tools. They’re building products. At PlatformCon 2025, our CTO and co-founder Ganesh Datta made the case for why platform teams need to think like product managers. His talk covers: ✔️ How to design for adoption, not just availability ✔️ Why golden paths are only valuable if they’re opinionated ✔️ What product management failure modes look like in platform teams ✔️ The metrics that actually show you’re making an impact This is how great platforms get built. Don’t miss it. 🎥 Watch the full session → https://lnkd.in/guE__kYB

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In a new blog post, Marc Bowes looks at how updates work in Aurora DSQL, and what it means for scalable schema design. Read it here: https://lnkd.in/gmGEK8Dv A couple take-aways for application builders: - You mostly don't have to worry about hot read keys in DSQL, even in read-write transactions. DSQL can scale out per-key read throughput, and the design requires no co-ordination between transactions reading the same key. (Thank you, MVCC and Time Sync!) - Scalable applications do need to avoid high-contention write keys when doing updates. Writes need to be coordinated and ordered (to implement the I in ACID), which limits per-key throughput. - Most applications can avoid hot write keys with the right schema design. Most applications have already been written this way, because high-contention keys perform poorly on relational databases of all kinds.

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There's been some talk around storage unification and zero-copy lakehouse integrations recently, and I wanted to better define these terms as well as look at ways we should evaluate different design decisions in this space. So I’ve published a new post: A Conceptual Model for Storage Unification Storage unification isn't about “one storage format to rule them all,” but virtualizing them into a unified logical model. In this post, I lay out a conceptual framework for thinking about storage unification (aka data virtualization). I explore: 🔹 Internal vs. shared tiering (and why shared tiering is so tricky). 🔹 When materialization makes more sense. 🔹 Client vs. server-side stitching. 🔹 Lifecycle and schema management across evolving formats. 🔹 Seven key considerations for evaluating different approaches. This is the groundwork for a set of posts where I’ll dig into how some real-world systems deliver storage unification, primarily about unifying real-time and historical data. https://lnkd.in/dBiS2kxJ

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PagerDuty is now a Cursor customer. Over 30,000 customers rely on PagerDuty to respond to incidents faster, reduce outage risks, and build operational resilience. We’re excited that PagerDuty engineers will now use Cursor’s agents to build these mission-critical products.

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