Christian Posta

Perconian Robert Bernier discusses Transparent Data Encryption for PostgreSQL and shows you how to automate the steps for enabling pg_tde for all new databases. Be sure to check this one out: https://hubs.ly/Q03wcc2v0

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Check out these System Instructions for Gemini 3 Pro that improved performance on agentic benchmarks by up to ~5%. For complex agents, enforcing behaviors like persistence, risk assessment, and proactive planning drives better reliability in multi-step workflows. Resources 🔗  SI Template: https://goo.gle/4rBTBMi Docs: https://goo.gle/4oAYN0d

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🎯 Running inference workloads on Kubernetes? I just wrote a deep dive on Gateway API Inference Extension and how it works. The Gateway API Inference Extension introduces two new Custom Resource Definitions (CRDs): InferenceModel and InferencePool. By using these two new resources, along with the endpoint selector, your L7 routing infrastructure becomes an “Inference Gateway” enabling you to self-host GenAI/LLMs with a “model-as-a-service” mindset. https://lnkd.in/g_eFqwUd #ai #llm #genai #inference

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🚀 Bare metal is making a serious comeback, especially for Kubernetes! But when is ditching the VMs for raw hardware really the right move for your K8s clusters? 🤔 It's a hot topic, and many are surprised by the clear advantages in specific scenarios. If you're nodding along, here’s a quick breakdown: 🎯 𝗕𝗮𝗿𝗲 𝗠𝗲𝘁𝗮𝗹 𝗦𝗵𝗶𝗻𝗲𝘀 𝗪𝗵𝗲𝗻: → 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀 𝗮 𝗠𝘂𝘀𝘁: Direct hardware access means minimal hypervisor overhead and no noisy neighbors hogging resources. Your critical tasks get full infrastructure capacity. → 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝗟𝗼𝘁𝘀 𝗼𝗳 𝗜𝗻𝗳𝗿𝗮 𝗙𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Got specialized, too new, or too old hardware? Bare metal handles it. Access different kernels and fully leverage unique hardware capabilities (like specific network cards) → 𝗬𝗼𝘂 𝗪𝗮𝗻𝘁 𝗙𝘂𝗹𝗹 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗼𝗳 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Single tenancy reduces the attack surface. No hypervisor vulnerabilities to worry about, plus you control kernel updates, encryption, and can use hardening frameworks like SELinux/AppArmor. → 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝗠𝗮𝘅𝗶𝗺𝘂𝗺 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲: Fewer abstractions mean better network speeds. Tools like CNI, flannel, and Calico make setup and routing smoother, and troubleshooting is often simpler. → 𝗬𝗼𝘂 𝗪𝗮𝗻𝘁 𝘁𝗼 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗖𝗼𝘀𝘁𝘀: Optimal resource utilization and predictable scaling costs can lead to significant savings-up to 30% TCO reduction in some cases! Plus, no hypervisor licensing fees. 🚧 𝗕𝘂𝘁, 𝗜𝘁'𝘀 𝗡𝗼𝘁 𝗔𝗹𝘄𝗮𝘆𝘀 𝗦𝗺𝗼𝗼𝘁𝗵 𝗦𝗮𝗶𝗹𝗶𝗻𝗴. 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀: → 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆: Forget "click-and-go." Provisioning, updates, networking, DNS, storage, and scaling are all on you. OS patching and certificate management? Your responsibility. → 𝗢𝗽𝘁𝗶𝗼𝗻𝗮𝗹𝗶𝘁𝘆 𝗢𝘃𝗲𝗿𝗹𝗼𝗮𝗱: The "blank slate" can be daunting. Choosing and ensuring compatibility for OS, interfaces, and hardening solutions requires careful planning and dedicated team resources. Choosing bare metal for K8s isn't a decision to take lightly, but for the right workloads, the benefits are undeniable! Learn more in my post here: https://lnkd.in/gqFduUsN

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New blog post: A Fork in the Road: Deciding Kafka's Diskless Future. Kafka is getting serious about S3 and finds itself at an architectural crossroads that will shape its next decade. Several new KIPs (1150, 1176, 1183) aim to reduce replication costs across cloud availability zones, but the implications go far beyond networking cost. It’s a mistake to think of S3 as simply a cheaper disk or a networking cheat. Building on object storage opens the door to operational benefits such as elastic, stateless compute — something many modern analytics systems already exploit. In my latest post, I outline two competing future paths: 🔹 Evolutionary path: Reuse large parts of existing Kafka components to reduce code changes and long-term maintenance. 🔹 Revolutionary path: Separate stateless and stateful layers to realize the full operational benefits of disaggregated storage. The post examines the trade-offs of each path, how the current KIPs map to these two paths and poses a broader question: what should Kafka become? And what will keep it relevant in the decade ahead? https://lnkd.in/d4E2BAe4

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Our most popular RAG solution now has support for ACLs in the cloud ingestion pipeline! Bring your existing ADLS2 blob storage account or create a new one, and the indexer will extract the oids/groups based off blob ACLs, and store those on the oids/groups fields in the AI Search index. Then, when the user is logged in, they can only chat with documents which they're allowed to access! Release notes here: https://lnkd.in/ewQfJ6Sg

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Excited to share that we’ve just shipped Obot 0.13, one of our most powerful updates yet. This release introduces composite MCP servers, giving admins the ability to combine multiple servers, expose only the tools they want, and deliver curated experiences to different teams or user groups. We’ve also added audit log export directly from the UI, making compliance, reviews, and long-term record keeping dramatically easier. For teams running Obot on Kubernetes, 0.13 brings centralized scheduling and resource controls, so you can manage affinity, limits, and deployment policies with far more consistency. Admins will now also receive update notifications in the UI, ensuring deployments stay current with the latest improvements. We’ve streamlined the initial setup flow for authentication and model providers, updated documentation across all major clouds, improved resiliency across the platform, and fixed a long list of bugs. A big release with a lot under the hood—excited to see what teams build with it. #AIEngineering #MCPGateway #MCPsecurity #AIops #EnterpriseAI #AIInfrastructure

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I spoke about just using Postgres instead of Kafka the other day. Here's an Apache-licensed stateless Kafka proxy with a pluggable Postgres (or SQLite, or S3) backend. What stops one from bundling this into Postrges via an extension? It could expose SQL-native functions to use as a pub-sub, and it could expose a true Kafka TCP server (with a background worker). That way it'd remain Kafka wire compatible from within the db machine. And both would share the same tables. I'm not an expert on C nor Postgres extensions, but ChatGPT seems to claim it's doable without much work. https://lnkd.in/dY3Hvwex

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"Moving from a single‑node RDS instance to YugabyteDB Aeon has been one of the most impactful infrastructure upgrades in Tofu’s journey. We gained horizontal scale, fault tolerance, and operational simplicity—without sacrificing PostgreSQL familiarity or developer velocity."💬 Don't miss this insightful blog from Ken Kanai, VP of Engineering at Tofu, which details how Tofu evolved its database architecture from a single‑node AWS RDS (PostgreSQL) instance to YugabyteDB Aeon, a fully managed, PostgreSQL‑compatible, distributed SQL database that now powers production.💡 https://lnkd.in/eGBsjHrk #distributedsql #Postgrescompatible #ai #GenAI #database #cloudnative #yugabytedb #opensource #accounting #financialservices

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Super interesting piece by our partners at TeraSky, introducing their Kubernetes Adoption Framework. KAF is a practical, modular guide to help organizations move from first steps to full production in K8s with confidence. At Spectro, we’re also passionate about simplifying Kubernetes. We know getting the foundation right is critical, and frameworks like this go a long way in clearing the path for others on their K8s journey. Check it out here 👉 https://okt.to/Bt6Xq0 #Kubernetes #KubernetesManagement #CloudComputing #DevOps

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This article speculates what Kubernetes 2.0 could look like by proposing a move from YAML to HCL, building in package management and revisiting core abstractions, aiming for fewer landmines and more developer ergonomics. More: https://ku.bz/p7-TvJncP

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Today marks the launch of Apache Iceberg Digest Vol 1, a new resource built by practitioners for practitioners. https://lnkd.in/gEZhkuAU This first volume dives deep into Iceberg concepts, tools & practical guidance to help engineers and architects stay ahead in their data journey. At MinIO, we believe the debate is settled: Apache Iceberg is the standard for enterprise AI data. With AIStor Tables, we’ve taken the next step by making Iceberg native to the object store. The Iceberg catalog is now part of AIStor itself, not a separate database or service. That means structured and unstructured data finally come together in a single, object-native platform. This ebook showcases the energy and expertise of the Iceberg community, with contributions from leaders across the ecosystem: - Alex Merced, Head of DevRel at Dremio - Karthic Rao, Software Engineer at MinIO - Casey Karst, Principal Product Manager at Fivetran - Kasun Indrasiri Gamage, Product at Confluent - Sa Wang, Software Engineer at PuppyGraph Read it here: https://lnkd.in/gEZhkuAU Apache Iceberg | #ApacheIceberg #AI #DataEngineering

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