Julian Hirt

🚀 New in Docling: Structured Data Extraction from Documents! 🚀 We’ve just added a brand-new functionality: extraction of structured data from complex documents using free-form schemas 🤩 What does that mean? 🔹 You can now skip the conversion step — instead of turning documents into text or JSON first, Docling directly extracts the structured fields you care about. 🔹 The requested fields are defined in a free-form schema, so you can instantly align the extraction with the schemas of your own databases. 🔹 This makes it ideal for data-pipelines that don’t need full document conversion, but do need to populate structured databases from messy, unstructured documents. ✨ It’s: 1️⃣ Super simple to use (check out the code snippet 👉 https://lnkd.in/eK6vaMEe ) 2️⃣ 100% open-source and runs fully local – no API calls needed 🙌 3️⃣ Powered by cutting-edge models from NuMind (YC S22) 4️⃣ Perfect for data-pipelines where you need to populate structured databases from documents (think invoices, Curriculum Vitae, contracts, product datasheets, etc) 5️⃣ Currently focused on PDFs and images (PNG) — support for pure text coming soon! The example below shows how easy it is to define a schema and extract structured fields directly from an invoice. 👉 Try it out, break it, send us feedback, and if you like what we’re building, don’t forget to ⭐ the repo (https://lnkd.in/d4UT-6_2)! After a long and fruitful summer, the Docling team has been cooking up many new features — and this is just the beginning! 🌟 #opensource #AI #documentAI #docling #IBM #IBMResearch

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What I love about #ApacheIceberg is how many different workloads folks are running on the same copy of data. Graph queries are a really cool example. Instead of copying data into specialized graph databases, PuppyGraph lets you run graph traversals directly on your existing Iceberg tables. Pretty cool stuff. But here's the thing - to get truly great performance, you need to ensure your Iceberg tables are well maintained and optimized. That's where Ryft comes in. We make sure your Iceberg tables are automatically maintained and optimized, no matter what engine you are using. It was terrific to work with Zhenni Wu and Weimo Liu, to create this blog post on how you can use Ryft with PuppyGraph to run graph queries on a fully optimized Iceberg lake! https://lnkd.in/giwnwMuc #ApacheIceberg #GraphQueries

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Databricks announces Zerobus - a Kafka-like message bus for writing data directly to your Lakehouse in the Unity Table Format. Zero copy. High throughput. Near real-time latency. Unlike Kafka, this is useful when your only destination is the lake house. I posted a while ago about the idea of open table formats reducing the need for Kafka - if S3 and table formats become the default place to store data, you omit a lot of read fanout jobs that simply copied the data to another system for use. Just store it in one place. Interesting development. It seems like every company is releasing their Kafka-like niched down alternative. (see Cloudflare Pipelines)

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🔊 Audio just became AI-native in Snowflake. AI_TRANSCRIBE is now available in Cortex — meaning you can transcribe audio files, detect speakers, and get word-level timestamps without managing any ML infra. Combine it with AI_SENTIMENT or AI_CLASSIFY and suddenly every call, podcast, or voice note becomes searchable, labelable, and insight-ready. I made a new Quickstart that walks through exactly how to set this up — including combining transcription with text and image analysis: 👉 https://lnkd.in/gyxeDdPi Can't wait to see how folks use this in production 🔥 #Snowflake #CortexAI #AITranscribe #MultimodalAI #GenAI #VoiceAI

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