Tyler Schade

Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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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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In-memory databases are the architectural backbone of real-time AI. They’re a necessary advantage for memory-intensive workloads that rely on instant access to context. Modern systems like Redis blend durability, multi-model flexibility (JSON, vectors, time-series), and tiered storage to deliver the speed of memory with the reliability of disk. The result? Real-time analytics, instant personalization, and AI systems that actually feel intelligent. Here’s how we deliver the speed of memory with the reliability of disk: https://lnkd.in/gSm65kdB

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The most effective architectural changes are often subtractive - removing entire layers of cost and complexity. Behind the scenes, OpsHelm, Inc.'s streaming backbone was migrated from a costly MSK and unreliable NATS setup to a single diskless Kafka architecture. The results were immediate: - Annual spend dropped from >$50,000 to <$10,000. - Data loss incidents went from recurring to zero. - Manual scaling and cross-cloud networking fees were eliminated. Read the full story of their migration and 78% cost reduction: https://lnkd.in/dS6UK9-7 #disklesskafka #datastreaming #Aiven

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Nate asked what makes Editor V4 so much faster. The simple answer is a new, CSS-first framework that produces incredibly clean code. This means faster load times and higher performance scores for everything you build. Want the full story? Watch Roi explain how this gives you a real performance edge.

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Unify your metrics without ripping out legacy tooling. Use OTel Collector as a central hub to ingest OTLP, Prometheus exporters, and StatsD, add consistent labels, then ship to Prometheus via Remote Write or a single scrape endpoint. Fewer configs, cleaner dashboards, faster troubleshooting. #OpenTelemetry #Prometheus #Observability #DevOps #Kubernetes https://lnkd.in/gFC_2vzX

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Why the Traditional IaC Learning Path Fails Engineers The old way is broken: ❌ Learn Terraform syntax ❌ Write basic configs ❌ Break things in production ❌ Spend months untangling technical debt This wastes time, stalls teams, and slows down careers. Modern IaC flips the script: ⚡ Versioned, tested, and consistent across every environment ⚡ Disaster recovery that rebuilds infra in hours ⚡ New engineers provisioning full environments without senior help And here’s the key insight for 2025: 👉 You don’t have to choose between learning quickly and learning correctly. We outlined 8 essential practices that separate pros from beginners and help you build production-grade infrastructure from day one. 📖 Read the full blog here: https://lnkd.in/gMA92S6J

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Running APIs in Kubernetes? It’s not as simple as dropping your gateway into a cluster. This post breaks down the real challenges of API management in K8s, from autoscaling and observability to security and GitOps, and shares how Kubernetes-native approaches can help you solve them. A must-read if you’re navigating API complexity in modern environments: 🔗 https://lnkd.in/g8eTaavv #APIManagement #Kubernetes #CloudNative #DevOps #WSO2 #APIGateway

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From Terraform to Crossplane Kubernetes-native IaC is evolving—are you keeping up? Crossplane is redefining how we manage infrastructure: declarative, secure, and fully integrated with the K8s ecosystem. Think Terraform, but closer to your cloud-native apps. -YAML over DSL -Native RBAC & multi-tenancy -Seamless integration with Helm, Operators & more Explore how Crossplane offers the next-gen control plane—bridging DevOps and Platform Engineering like never before. #InfrastructureAsCode #Crossplane #Kubernetes #CloudNative #DevOps #PlatformEngineering #MSysTechByte #Infraunlocked #MSysTechnologies

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Myth: Kafka is too complex for small teams. Truth: With Condense, managing Kafka becomes simple, visual, and ops-free. Small teams often avoid Kafka because of setup headaches, CLI configs, and infra management. But Condense changes the game, giving you a fully managed, Kafka-native platform with drag-and-drop pipelines, Git-backed deployments, and BYOC flexibility. Now, even lean teams can build real-time, production-ready pipelines without worrying about infrastructure. 🔗 Learn more: https://lnkd.in/gEWNJHcf 🔗 Explore more: https://lnkd.in/gnXu_bYP #KafkaMadeSimple #MythBusting #ApacheKafka #RealTimeData #DataStreaming #Condense #OpsFreeStreaming #KafkaForTeams #Zeliot

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🐛 What's worse than a 10.0 CVSS score? Nothing, it doesn't get any worse. 🤯 Researchers have revealed a critical 13-year-old bug in Redis (CVE-2025-49844, aka “RediShell”), rated at CVSS 10.0, that allows authenticated users to run malicious Lua scripts which can manipulate the garbage collector and trigger use-after-free vulnerabilities. 🩹 In effect, this flaw breaks out of the Lua sandbox and enables remote code execution on the Redis host. The flaw affects all Redis versions supporting Lua scripts. Redis has released patches in versions 6.2.20, 7.2.11, 7.4.6, 8.0.4, and 8.2.2 on October 3, 2025. ⚔️ Actionable threat intelligence not only involves knowing what bugs exist in the wild, but also how threat actors actively exploit them. SIEM alerts certainly do not provide such critical context, but LMNTRIX XDR does. To find out more, visit us here 👉 https://lmntrix.com/ #LMNTRIX #XDR #MDR #MXDR https://lnkd.in/gch5EQx3

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Traditional Kafka consumers for analytics = expensive, complex, and fragile. Replay, deduplication, schema drift… it’s a lot. On Oct 9, we’ll show how Estuary + MotherDuck simplify all of this with a fresh approach to streaming ingestion. 🔗 https://lnkd.in/dncWFDGm

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Spark on Kubernetes looks great on paper. But in production? That's where things break. Spark assumes static executor sizing. Kubernetes expects dynamic workloads. Add the JVM blind spot, where Kubernetes can’t see heap usage, off-heap allocations, or GC pressure, and you get the classic failure mode: executors look healthy to Spark but get killed by cgroup memory limits. That’s not optimization. That’s firefighting. In our latest blog post, Konstantin Zelmanovich breaks down why this happens and how the ScaleOps platform fixes it. If you’re running Spark in K8s and tired of memory guesswork, this one’s for you. Read the full breakdown 🔗 https://lnkd.in/dkQTvi_W

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GraphQL offers flexibility, but it also expands your attack surface. This datasheet explains how we help protect GraphQL APIs from evolving threats, providing runtime protection and deep visibility into malicious activity without slowing your teams down. Download the Wallarm GraphQL Security Datasheet to explore key protections and learn how to keep your APIs resilient: https://lnkd.in/d79jdedJ #CyberSecurity #APISecurity #GraphQL #AppSec #ThreatDetection

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