Introducing LangGraph RAG with Elasticsearch

Struggling with slow updates in your Apache Iceberg 🧊 tables? When dimensions change too fast, Iceberg writers can face chilly performance bottlenecks. What if there was another way you could write Iceberg tables with blistering throughput, no compromises? Read our latest engineering blog to learn how your Iceberg tables can leverage techniques that standard OSS might not get even by the v6 spec 😉. 👉 https://lnkd.in/gS5nPPai #ApacheIceberg #ApacheXTable #ApacheHudi #DataLakehouse

Feeling like a JSON novice? Ever wondered how many valid JSON strings are out there in the wild? Well, wonder no more! Our latest blog post dives into the fascinating world of JSON and unravels the mystery behind its limitless potential. Join us as we crunch the numbers and explore how vast the landscape of valid JSON strings really is. Spoiler alert: the answer might just blow your mind! Don't miss out—click the link to learn more! https://ift.tt/PvVXtUM

Composability makes you fast, but does it also make you fragile? As the builders of the most composable data system™ on the planet (bauplan), keeping customer workloads safe, even in the face of failures, is a top priority. Our mental model extends the usual “reliability line” into the “dependency plane” with a new dimension: *“un-f%!^%g things myself.” Unit tests, end-to-end tests, simulators, and formal proofs are then all part of our deliberate testing strategy. This is also a good moment to recognize the projects and communities we rely on (and often contribute back to): the CDMS event (Satya Valluri, Mohamed Zait, Wes McKinney, Pedro Pedreira, etc.), Apache DataFusion, Apache Iceberg, DuckDB, Kuzu, etc. Huge thanks to the SRDS Conference for having us for the keynote today: next year’s edition is in Rome, just sayin’ 😎 Slides are available upon request. See you, #distributedsystem cowboys! #faas #serverless #lakehouse #verification #testing #iceberg

It's valuable to grasp data streaming fundamentals, even when building streaming apps with a low-code/no-code tool and not being a software developer.

🍦 @fizzwiz/vanilla v0.0.0-dev.1 is out! Lightweight semantics for plain JSON objects — perfect for distributed objects & async workflows. ✨ Includes: OptionStore (type-based options) ObjNavigator (safe nested editing) 🧠 Learn more → https://lnkd.in/enqK3rJv 📦 npm → https://lnkd.in/eqg6EvCy “Keep it plain, keep it structured.”  — `@fizzwiz` ✨`

🔥 Master the power behind Apache DolphinScheduler’s Worker Load Balancing! Discover 3 key algorithms — Weighted Random, Smooth Round-Robin, and Linear Load (default) — plus weight tuning, warm-up logic, and full technical insights. #ApacheDolphinScheduler #LoadBalancing #BigData #OpenSource 🚀 https://lnkd.in/eG4ah-Fi

New JSON attachments let you log arbitrarily large or nested JSON, beyond any trace limits. Use them for long conversation transcripts, doc collections, embeddings, and more. Learn more: https://lnkd.in/gV6-E_gW

HelixDB is an open source graph-vector DB with native support for reranking, MCP and graph RAG. Written in Rust, it has its own type-safe query language and explicit schema support. https://www.helix-db.com/ #GraphDatabase #Graphs

LazyDocker A fast, Go-powered TUI for Docker and Compose with logs, stats, exec, and lifecycle controls at your fingertips—the lazier way to manage containers ⭐ Stars: ~46.6k 🍴 Forks: ~1.5k 🧾 License: MIT 🧩 Languages: Go 💻 https://lnkd.in/eaPqVm4 ✨ Features: - Overview containers/compose - Tail service logs - ASCII metrics graphs - Custom metrics - Attach to containers - Restart/remove/rebuild - Inspect image layers - Prune disk usage 🔔 Discover Open Source. Every Day

Implementing Astro Pagination for Large Data When I was building the MCP directory for Free DevTools, I ran into a problem: loading 13,000+ repositories from JSON at once was painfully slow. I initially tried to build a custom pagination component, but it didn’t work efficiently — everything had to load at once before the UI even appeared. That’s when I started digging into Astro’s built-in pagination system — and it turned out to be the perfect solution for large collections. If you’ve ever fetched a big dataset (thousands of items) and tried to render it in one go, you’ve seen what happens: The page freezes or crashes during hydration. Memory spikes. Scrolling becomes laggy. Static build times increase drastically. In my case, the MCP directory had 13k+ items coming from JSON. I needed pagination that could: Split data into multiple pages. Load only what’s needed. Still be SEO-friendly (pre-rendered static pages). Astro has first-class support for pagination, directly built into its file-based routing system. You don’t need a https://lnkd.in/gGhd5Rwm