Rob Gilliom

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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If your team is building with LLMs, you know the value of clean, retrievable data. That's why we're teaming up with Unstructured to streamline one of the hardest parts of AI development. This integration removes friction across the AI stack—from unstructured ingestion to semantic retrieval—so your teams can focus on building. Read more from our Product Marketing Managers, Rini Vasan and Jim Allen Wallace, and Unstructured’s Head of Developer Relations, Maria Khalusova here: https://lnkd.in/e98QqXAB

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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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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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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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“1 million rows per second.” In his opening keynote, our CEO Max Liu walked through what that really means in production: - TiDB processing 1M rows/sec - 10x higher throughput for cross-region replication - A data layer built to make multi-tenant architectures actually practical at scale If you’re thinking about SaaS, multi-region, or AI-heavy workloads, this is the kind of infrastructure ceiling you want. 🎥 Watch the full keynote from Max Liu at #TiDBSCaiLE to see what’s behind the numbers: https://ow.ly/BgKl50YetBq #TiDB #DistributedSQL #MultiTenancy #CloudNative #Scalability

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LLMs can now drive Playwright directly, generate tests, refactor them, and even monitor production apps... or can't they? Here’s how far we got with Playwright MCP and AI-powered workflows 👇 https://hubs.ly/Q03PgXyD0

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The best way to test your Solidity knowledge isn't reading more docs. It's quizzing yourself until you can't get it wrong. 🧵 Most developers read a concept, nod along, and move on. Then they sit down to build something and realize they can't recall any of it. Sound familiar? Active recall is how you actually learn: → Close the tutorial → Try to explain the concept from memory → Write the code without looking → Take a quiz and find your gaps Reading feels productive. Testing yourself IS productive. This is why Updraft courses include quizzes between sections. They're not filler. They force you to confront what you think you know vs what you actually know. The gap between those two things is where real learning happens. If you're going through the Foundry course, don't skip the quizzes. They're there to make the rest of the course stick. Start here, completely free: https://lnkd.in/eA6WQszU

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Learn how Akuity boosted Argo CD performance and simplified infrastructure by moving from Redis to Dragonfly. A great example of how API-compatible, modern data infrastructure can drive efficiency without code changes. Check out the full case study to see the results. https://hubs.la/Q041Lw7L0 #ArgoCD Argo Project #GitOps #Kubernetes

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Anyscale 🤝GKE Through our continued collaboration with the Google Kubernetes Engine (GKE) team, we’re advancing the experience to scale Ray and Kubernetes in the cloud. This work includes: Label Selectors for improved scheduling: https://lnkd.in/gUZ8iVUW New Features for AI Scaling: https://lnkd.in/gAGSMiir Enhanced TPU Support: https://lnkd.in/gsKDvwpv

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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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Cassandra and ScyllaDB rely on fundamentally different scaling models. But even in tests that favor Cassandra's vNodes model, ScyllaDB sustained 4X the throughput while maintaining consistently lower P99 latencies under similar infrastructure. See the results for yourself here > https://ow.ly/3SHB50Y1fL9 #ScyllaDB

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Getting telemetry flowing isn’t always straightforward, especially when every stack looks a little different. As part of our ongoing OpenTelemetry series, we put together a clear, simple guide to help you set up instrumentation and get signals in cleanly: https://lnkd.in/g7gib4ds How do you usually approach instrumentation in your services?

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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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🎉 Exciting read from our very own Florian Hussonnois, Lead Software Engineer at Kestra! In this first episode of Orchestrating Streams, Florian shows how Kestra and Apache Kafka can work hand-in-hand to power event-driven workflows with no boilerplate code required. From generating synthetic data streams to producing real events from APIs, webhooks, or SQL tables, this post walks through practical, hands-on examples of streaming orchestration made simple. 📘 Read the full article on Medium:  https://lnkd.in/g8hU3S_k

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