Frédéric Schmaljohann

💛 working with TMDL on Direct Lake I tested my personal new highlight feature of this month today with Marcus Wegener and Andrea Hupp - it works on all explicitly created Semantic Models - the "edit mode" has to no report - like in the Web-Service. But the DAX query view feels "more close" - all changes are synched to the published model. No saving needed. 🤯 but the real game changer is to export the Power BI file a project, then I can: - edit everything in TMDL code and sync the changes fast to the published model (but also beeing able to refuse the changes). Deployment never have been so easy 🤩 - as an empty report appears when I open the .pbip file, I am able to test my changes in real visuals. Even when I love the DAX query view... sometimes visuals show me more insights when I investigate some data issues 😉 👾 Doing the Video was also a chance to use the AI Dubbing one more time - it still feels a little strange, but I still like it and its faster doing the recording multiple times. I still sound different in real english tutorials I do from time to time 😅 Looking forward for more news from the European Microsoft Fabric Community Conference ..! 😃 #powerbi #fabric #update

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🤖 AI Agents are everywhere these days! Just yesterday, I had another customer workshop, where those Agents were able to solve complex tasks on private data, which involved writing and executing custom SQL queries! 🤯 In our latest todo:cast | Developer Podcast 🎙️ episode (🇩🇪 only in German ¯\_(ツ)_/¯), Malte and I discussed, what AI Agents are, what they can do, and how to build them. #Agents #AI #AutoGen #LLM #ChatGPT #OpenAI

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My new article on medium: how to simply enable temporal / history table in PostgreSQL. No need for: - Special extensions. - Specialized databases. - Special techniques like event storming or soft deletes. None of that is needed. Don't overcomplicate things. https://lnkd.in/dQDNrCq8

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“After one year of reading and working daily with LLMs, here are the 𝘁𝗼𝗽 𝟭𝟮 𝗯𝗹𝗼𝗴𝘀 on 𝗟𝗟𝗠𝘀 & 𝗥𝗔𝗚 I 𝗿𝗲𝗮𝗱 𝘄𝗲𝗲𝗸𝗹𝘆 and 𝗿𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱 ↓”

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Building a Single-node Lakehouse using 'Hudi-rs' - no JVM, Spark [New Blog] The Apache Hudi community recently released "hudi-rs" - a native Rust library for Hudi with Python bindings. This makes it possible to work with #lakehouse platform like Hudi across a range of use cases that typically don’t require distributed processing. For eg, data science & machine learning applications need frequent access to diverse datasets for exploratory data analysis/model training. Direct access to Hudi datasets for these use cases reduces the ‘wait time’ on data stakeholders, and speeds up the time-to-insight process. Traditionally, accessing these tables involves configuring Java, Spark, Hadoop, and other related dependencies. This is what "Hudi-rs" addresses & makes it easy for data consumers to get started. In this introductory blog, I go over - ✅ the internals of "Hudi-rs" with #ApacheArrow under the hood ✅ how to use it with popular single node computes such as DuckDB, Polars, Daft & Apache Datafusion Check out the link in the comments. And reach out if you want to get involved with the project! #dataengineering #softwareengineering

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We must consider the premise that moving on completely from the GenAI craze is not only a legitimate position, but maybe the smartest one. The theoretical minor benefits this technology provides dwarfs in comparison to the risks, both theoretical and concrete.

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There's a common myth in the cloud consulting business landscape. In the quest to offer end-to-end solutions, many companies attempt to juggle more balls than they can. They often forget they are not circus performers - if you think you are, consider this a friendly reminder that you’re (probably) not. Cloud Consulting boutiques love to brag about their in-house capabilities to manage the entire customer journey from 0-100. On paper, this sounds amazing - and many customers are falling for these flattery terms: “We are the only cloud partner you need” “Comprehensive control and seamless integration” “All your needs handled under one roof” I totally disagree with that approach. I know my data stuff well but e.g. have no idea how to set up a proper landing zone like Manuel, and no idea how to make CI/CD a joy like Johannes. And now, I also know the cost guru of the cloud - if a shiver runs down your spine every time you look at your AWS bill, you know what to do ➡ Cristian Cristian Cristian So, instead of trying to be a jack of all trades: Focus on what you do best and partner for the rest. Did we forget how niche specialisation made the “Mittelstand” the backbone of our economy?

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As your fleet of databases grows, you want to optimize price and performance across that set of Hyperscale databases. Elastic pools for Azure SQL Database Hyperscale offers the convenience of pooling resources like CPU, memory, IO, while ensuring strong security isolation between those databases.

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Join Databricks on November 7th for Data + AI World Tour Munich! Discover how leading companies like Fraport AG, Deichmann SE, Bayer AG, and CARIAD SE (A Volkswagen Group Company) are taking control of their data and building custom AI on the Databricks Data Intelligence Platform. Register now!

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Tomas Vondra: The state of the Postgres community 🚀 Back from Swiss PGDay 2024! About a month ago, @TomasVondra delivered a keynote on the state of the Postgres community with some eye-opening charts. 🎤 If you missed it, don't worry. I’m here to share the highlights. Spoiler alert: we’re doing great, but we’re not perfect. Tomas pointed out what's working, what's not, and what will most likely keep us up at night. Think of it as a Postgres report card. 📝 Some of the trends were intriguing – even surprising! For instance, some parts of the community are thriving like never before. Kudos to all the contributors and tireless developers. You rock! 💪 But, it’s not all rainbows and butterflies. We’ve got challenges ahead that need more TLC. So, gear up. This is a marathon, not a sprint. Ready for a deep dive? Here’s the full talk: [Swiss PGDay 2024](https://www.pgday.ch/2024/). Big shoutout to Tomas Vondra for his insights and stellar presentation. 👏 #PostgreSQL #DatabaseGeek #SwissPGDay2024 #CommunityInsights #TechTalk You can find the full article here: https://postgr.es/p/6Bf

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It looks like that the new, upcoming PostgreSQL 17 is mostly performance oriented. Many improvements to the optimizer. Here are the release docs, see for yourself, maybe I'm wrong. https://lnkd.in/dyUw4d9w

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"[...] I have been fascinated with databases and query languages." [1] Andy Grove ...I can definitely share that fascination, so I decided to dive deep and build a query engine from scratch in Rust. After 8 weeks, roughly 12k lines of code, and more than a few moments of frustration, it's "done"! Inspired by "How Query Engines Work" and heavily influenced by Apache DataFusion [2], I implemented the most common operations you'd expect from a query engine. While it's not feature-complete, it's been a fantastic way to explore the inner workings of a query engine while keeping things relatively simple. ✨ Some Features: - Basic SQL and CSV support - DataFrame expression API - Aggregations, GroupBy, Joins, and more - Simple rule-based query optimizer If you're curious about query engines or just love Rust, take a look at the project on GitHub: https://lnkd.in/ehC6_sk6 [1] https://lnkd.in/exm9S6ZC [2] https://lnkd.in/eNa5368a #Rust #Databases #QueryEngines #OpenSource #LearningByBuilding

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👇🏻 Check out how Erste Group is leveraging synthetic data to democratize #Data & #AI for everyone using MOSTLY AI integrated with the Databricks #DataIntelligence Platform 👇🏻

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Very insightful post. Few questions every seasoned AI practitioner ask themselves: 1. TS: Why to use Gen AI model when ARIMA, TES etc can solve ? 2. Image: Why to use Gen AI model when CNN is suffice enough ? 3. Sequential data: Why to use Gen AI model when RNN can easily handle? ....and so on Model Architecture always should be depended on the complexity of the requirement + complexity of the data. Not on fancy trends. That 's why the forceful implementation always ends up with rediclous consequences. Salesman won't understand, but product owner should. wrong implementation backfires Post-Prod. #Generativeai #genai #ai #industry

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Low-Code No-Code is a mega trend in IT industry. Not only in Applications Development, but also in others like #MachineLearning. Ludwig, a popular open-source low-code framework for building custom AI models like LLMs and other deep neural networks, it simplifies the machine learning process with a low-code approach, enabling non-ML roles(application developers, business users etc) and empowering the ML professionals(ML Engineers, Data Scientists etc) to quickly experiment, build, train, and deploy models When paired with SAP AI Core, these machine learning operations can be streamlined and managed at scale, fostering faster AI-driven innovation across the enterprise with enterprise-grade of security, scalability and compliance. The benefits of combing two together. ✅ Accelerated innovation of enterprise AI ✅ Improved Accessibility and Productivity ✅ Enterprise-grade scalability, security, and compliance ✅ Seamless Integration of Custom Intelligence into Business Process Here is my exploration with end-to-end Low-Code Machine Learning with Ludwig and SAP AI Core. I am able to replicate some ML models like Book Genre Classification, Defect Detection with image segmentation, and predictive maintenance based on machinery sound anomaly detection in hours. The only code is just a declarative configuration file in yaml. Check it out. Gianluigi Bagnoli Jacob T. Mostafa Sharaf Alice Magnani Cesare Calabria Trinidad Martinez Thiago Mendes Dr. Merza Klaghstan Alessandro Biagi Pavel Penaz Peter von Linstow

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Unlock the future of data warehousing with Snowflake and AWS: Drive into our comprehensive guide to scalability, performance and integration. Thankyou PCG Germany and Carsten Riggelsen for giving me the opportunity to publish my thoughts! #DataWarehousing #AI #ML #Snowflake #AWS #PCG Hannes Novak Raphael Bögel Julius Otto Mathias Rehberg David Lewenko

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Composition of models will bring the next wave of innovation.

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