Kris Peeters

🚀 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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When data agents fail, they often fail silently - giving confident-sounding answers that are wrong, and it can be hard to figure out what caused the failure. "Building and Evaluating Data Agents" is a new short course created with Snowflake and taught by Anupam Datta and Josh Reini. This course teaches you to build data agents with comprehensive evaluation built in. Skills you'll gain: - Build reliable LLM data agents using the Goal-Plan-Action framework and runtime evaluations that catch failures mid-execution - Use OpenTelemetry tracing and evaluation infrastructure to diagnose exactly where agents fail and systematically improve performance - Orchestrate multi-step workflows across web search, SQL, and document retrieval in LangGraph-based agents The result: visibility into every step of your agent's reasoning, so if something breaks, you have a systematic approach to fix it. Sign up to get started: https://lnkd.in/gVwKuCCQ

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Enterprise data pulls in two directions. On one side: governed BI workloads — structured, compliant, and built for business reporting. On the other: unstructured pipelines — streaming feeds, heavy transformations, and machine learning at scale. Most organizations now run Snowflake and Databricks in parallel to handle this dual reality. Yet stitching them together creates new challenges: deciding which workloads belong where, moving data without waste, and keeping governance consistent across environments. Each platform brings distinct strengths to the table — Snowflake excels at governed analytics and BI reporting, while Databricks powers heavy transformations, streaming, and machine learning. When combined thoughtfully, these strengths converge into a unified architecture that supports the full data lifecycle. > Watch the on-demand discussion on how Janus Henderson Investors designed a unified architecture that balances both worlds: https://lnkd.in/eG-8YBqm In a financial services setting where accuracy, performance, and cost discipline matter most, Janus Henderson’s approach connects Snowflake’s strengths in analytics and governance with Databricks’ capabilities in engineering and ML. #unifieddataarchitecture #snowflake #databricks #usecase

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After my post on the Data Downtime Paradox, many asked: 👉 “But what exactly is Data Downtime?” Here’s the definition 👇 Data Downtime = periods when data is partial, missing, erroneous, or inaccurate — when it no longer reflects the real state of the system. In simple terms: when your sensors stop telling the truth. So here’s Data Downtime 101 — a 5-minute explainer covering: 🔹 How Data Downtime shows up in sensors, streams, and historians 🔹 Why it silently kills trust in analytics, AI, and Industry 4.0 programs 🔹 What you can do about it 📌 Link to the video in the first comment 👇 At Timeseer.AI, this is exactly what we track and benchmark for industrial companies, turning Data Downtime into a measurable KPI. #DataDowntime #IndustrialAI #DataQuality #DigitalTransformation #AIoT Niels Verheijen Adriaan Van Horenbeek Thomas Dhollander Stijn Meganck

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Everyone in data is talking about the semantic layer again. Here’s why it matters and where the hype meets reality. Why the renewed interest? ✦ AI & LLMs need structured context to reason about data ✦ Headless BI & composability demand consistent metrics ✦ Governance & contracts live best at the semantic level ✦ Tooling (dbt, Cube, Transform, etc.) is maturing fast But let’s be honest... ✦ Most orgs still struggle with ownership and naming chaos ✦ It’s hard to get adoption across the stack ✦ LLMs might learn semantics from usage, not definitions The future? Semantic layers won’t just serve dashboards, they’ll: → Power AI agents with guardrails → Trigger smarter, impact-based observability → Explain “why” when decisions are made on your data The role of the semantic layer is evolving. In an AI-native stack, it's less about metrics governance and more about making data intelligent and trustworthy by default. Curious how others are thinking about it; agree, disagree? #semanticlayer #dataengineering #datagovernance #aireadiness

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🤯 How the hell did I miss this update to Databricks SQL last month? 💡 Databricks have been serious about their investments in SQL for some time now. In July they introduced stored procedures in their Databricks SQL (version 2025.20 in Preview)! As in other data platforms, stored procedures in Databricks SQL are pre-compiled collections of SQL statements allowing users to manage their SQL logic into a single, reusable unit. As expected, procedures in Databricks are stored in Unity Catalog. And yes, they can be nested and recursive ;-) ✨ Why I think this is important? Because: 1) it's another important step to make Databricks more friendly for SQL developers willing to use their skills (and habits) built on other platforms (often on-premises RDBMSs) to Databricks and 2) support for stored procedures can simplify the migration process when moving from SQL-based platforms to Databricks. ❓ Anyone has already given a serious test drive to stored procs in Databricks SQL? ➡️ Read more here: https://lnkd.in/dYUwzevW #Databricks #SQL #StoredProcedures #DataPlatform #DataEngineering

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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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