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Artikel von George Pirvu
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How do you recognize exceptional Data Scientists?
How do you recognize exceptional Data Scientists?
After working with couple of Data Scientists teams until now, I can conclude that all were extremely good prepared…
3
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Das Data Analytics Framework28. Nov. 2020
Das Data Analytics Framework
Warum brauchen die Unternehmen ein Data Analytics Framework? In Data Analytics Projekten besteht eine Tendenz zuerst…
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SAP on AZURE ?18. Nov. 2020
SAP on AZURE ?
Many Information Technology (IT) executives, be they Chief Information Officer (CIO), Chief Technology Officer (CTO)…
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What is Infrastructure as a Code?16. Nov. 2020
What is Infrastructure as a Code?
From the Iron Age to the Cloud Age Cloud Age technologies make it faster to provision and change infrastructure than…
5
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Data Lake Analytics Rationale5. Nov. 2020
Data Lake Analytics Rationale
Inception: With the advent of new applications and demand to build 360-degree analytics scenarios, there was a need to…
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Business Intelligence Developer (m/w)2. Sept. 2015
Business Intelligence Developer (m/w)
Sehr geehrte Damen und Herren, gerne stelle ich Ihnen ein Projekt vor und möchte mich bei Ihnen erkundigen, ob es…
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3596 Follower:innen
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George Pirvu hat dies geteilt💡 If you are using Databricks Genie and costs are spiking, probably you are thinking about how many tokens it burned and at which cost. If this is the case, just stop for a moment and read this. It will be helpful. Genie doesn't charge per token. You pay for the SQL warehouse it runs on, as a compound system. Probably you fell overpaying because of: 1️⃣ pointing to uncurate data or raw tables 2️⃣ oversized warehouses 3️⃣ lack of metadata information that Genie can`t feed in the LLM context 4️⃣ long idle timeouts burning DBUs between sessions 🤖 Costs spike when Genie is pointed at the wrong things. You can actually mitigate and control this. What if I tell you, you could actually use Genie for 100 users in production on a Small warehouse for roughly $6–7 per user per month at list price? We have done the calculation in the article. ❔ What does your BI-team do today when a business user needs an answer that isn’t already in a dashboard? Open ticket, assign to a BI engineer, create new report or modify existing report, publish. Just to answer couple of questions. This friction can be resolved. ❔ Will this billing model change in the future? This I don`t know, but that`s how it works today. 📰 Link to the article in the comment section. #Databricks #DataEngineering #CloudCosts #BI #AIEngineering #Genie Adastra
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George Pirvu hat dies geteilt💡 Insilico Medicine, a Hong Kong-based biotech using generative AI for drug discovery, finds a clinical candidate in 18 months. Traditional discovery takes 5-6 years. They test 60-200 compounds. Legacy methods screen up to 1 million. Their lead drug Rentosertib hit positive Phase 2a results for idiopathic pulmonary fibrosis, targeting a protein nobody had tried before, with AI. Eli Lilly, one of the world's largest pharmaceutical companies, noticed. $2.75B. Full commercial licensing deal across multiple therapeutic areas. For its part, Lilly is no stranger to AI partnerships, having teamed up with Nvidia in October to build what it called the “most powerful” supercomputer in pharma. AI is accelerating drug development, but it remains to be seen whether those accelerated compounds will pass clinical trials at a higher rate than those developed in traditional ways. 💊 German pharma still has the science base, but if we are still waiting for the next steering committee to approve a ticket change request, this is a losing battle. 👉 Link to article in the comment section. #Pharma #DrugDiscovery #AI #GermanPharma #Biotech
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George Pirvu hat dies geteilt💡 Prompts Are Not Security Controls I'm seeing a dangerous pattern in AI engineering projects: "Let's enforce the guidelines in the prompt." 💥 This is wrong and critical to understand. Prompts are probabilistic. The model CAN and WILL ignore your instructions. No matter how clearly you write your system prompt, it's not a security boundary. It's a suggestion the model will occasionally break. Never use prompts to enforce: 1️⃣ Access control 2️⃣ Data privacy rules 3️⃣ Compliance requirements 4️⃣ Safety constraints Use securtiy layers instead: 1️⃣ Input validation before the model 2️⃣ Output filtering after generation 3️⃣ Schema enforcement for structured outputs 4️⃣ Human approval for critical actions 5️⃣ Defined network perimeters 6️⃣ Monitor and evaluation Prompts are great for tone, style, and preferences. If breaking thre guardrails has consequences, don't trust natural language to prevent it. LLM can`t predict the outcome of its recommendations and actions. #AI #LLMs #Security #MLOps #Databricks #GenAI #AIEngineering #RAG
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George Pirvu hat dies geteiltWhy is streaming data becoming more and more popular? 💡 Most data is born as a stream (clicks, sensor spikes, transactions). Batch processing is just the "artificial suppression" of that flow, like holding back the flow until we’re ready to deal with it. 🎯 One reason is the increased popularity of real-time analytics. Businesses are demanding up-to-date data for faster, better business decisions, and fresh data is more actionable. 💫 Streaming data is becoming popular also for operational applications. Critical operational applications need real-time data for effective and instantaneous response. In high-volume manufacturing, the goal is to stop a defective part at Step 2 so you don't waste money and time on it through Step 50. In many systems, reacting late is expensive. Streaming removes the artificial waiting step between event and action. 📰 I wrote an article that explains streaming simple, how it works in Databricks, but alsow why it is challenging to implement. Link in the comments. 👇 #databricks #streaming #spark #ai #machinelearning #dataengineering Adastra
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George Pirvu hat dies geteilt💥 Something we have been working on behind the scenes is finally real. 🇸🇰 The first-ever Databricks User Group in Bratislava is happening on May 12. A big thank you to the Databricks team for trusting us to build this together. You don't just build technology, you invest in the people using it and building a community, step by step. That`s powerful. 💡 Slovakia, Czech Republic, Poland, Romania, Hungary. People here love data and technology. They get excited about it. They go deep, they experiment, they want to have a place to share and learn from each other. They actually care about building things well. That energy is awsome. And it deserves a place to meet. I am happy that Databricks is also about community. 🤝 Thank you, Darius Rahimi, Viktor Bartusek, Stepan Chop, Tomasz Kurzydym for the very good collaboration over the past months. 🤝 Thank you, Marius Maslo, Luboslav Gabal, Simona Laqua-Willigens & Tomas Synek for enabling this initaitve. 🤖 Pridaj sa k Databricks User Group Bratislava a pomôž formovať databricks komunitu na Slovensku. Ak si v okolí, toto je tvoja miestnosť. #databricks #usergroup #adastra #slovakia #bratislava #kosice #databrickscommunity #aiengineering #dataengineering #slovak AdastraGeorge Pirvu hat dies geteiltDear network, Together with Adastra, we're excited to invite you to the first-ever Databricks User Group Bratislava on May 12! 🇸🇰 Here’s what’s coming up: 🧐 Beyond the Diagram: The Concrete Decisions Behind Each Medallion Layer — Peter Karas, Multitude ⏳ Transitioning from legacy ecosystems to Databricks Lakehouse — Luboslav Gabal, Adastra 🍻 Networking, drinks & good food 🔜 and a 3rd presentation to be announced soon Don’t miss this great evening of learning, sharing, and connecting with the data community in Bratislava. 👉 Save your spot now: https://lnkd.in/dMHwVkD6 Viktor Bartusek Stepan Chop Tomasz Kurzydym George Pirvu
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George Pirvu hat dies geteiltHello everyone, tomorrow I'm going to present how to build the retrieval layer of an AI application on Databricks, hands-on. 🔴 Live tomorrow. Live Lab #61 - Building the Retrieval Layer for Modern AI Apps with Databricks Vector Search 💡 Here's what we're doing in 60 minutes: PDFs → embeddings → Delta Lake → Databricks AI Vector Search → Similarity Search → Reranking → Results Analysis That's the retrieval backbone of a RAG system. Built on Databricks. 📅 Tomorrow, 06:30 PM CEST 📍 SuperDataScience Platform P.S. I've used a lot of platforms in the past. SuperDataScience is cool. SuperDataScience is the one where you actually leave a session knowing how to do something you couldn't do before. They have great course on AI you might want to check it also. If you are interested,fee free to join the community. Link in comments. 👇 #databricks #genai #rag #aiengineering #llm #dataengineering #lakehouse #mosaicai Adastra
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George Pirvu hat dies geteilt“AI psychosis” or “delusional spiraling” is an emerging phenomenon where AI chatbot users find themselves dangerously confident in outlandish beliefs after extended chatbot conversations. This phenomenon is typically attributed to AI chatbots’ well-documented bias towards validating users claims, a property often called “sycophancy.” 🤖 The Core Problem AI chatbots trained with RL from Human Feedback develop a bias toward agreeing with users called sycophancy. Because users rate agreeable responses are overwhelming, this paper below formally investigates whether sycophancy causes "delusional spiraling": users becoming dangerously confident in false beliefs after extended chatbot use. 💡 Key Findings Even a perfectly rational Bayesian user spirals into false beliefs when the bot is sycophantic. The paper argues that Sycophancy is the driver, not just hallucination. A bot that hallucinates randomly is less dangerous than one that hallucinates strategically to validate the user. 🎯 Right now there is no obvious solution to completly mitigate this Sycophancy. According to the paper, it must be addressed directly at the training level. Awareness campaigns and RAG guardrails alone are also not enough. Paper was signed by MIT CSAIL, University of Washington Seattle, MIT Department of Brain & Cognitive Sciences Conclusions belong to you. 📰 Link to the paper in the comments. #ArtificialIntelligence #MachineLearning #Databricks #AI #GenAI #RLHF
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George Pirvu hat dies geteilt💥 Something just went live in Slovakia. The Databricks User Group Chapter Bratislava is officially launched. 💡 Slovakia has a technically strong and professional data community. Bratislava, Košice, the talent is there. What's been missing is a dedicated space where Databricks practitioners and customers can sit in the same room and realize we are all in the same boat when it comes to data challenges. This is the first official Databricks User Group chapter in Slovakia. One community, led by practitioners, for practitioners. Every strong community starts small. What to expect from our sessions: 1️⃣ Lakehouse architecture & Delta Lake patterns 2️⃣ AI & ML on the platform 3️⃣ Unity Catalog governance 4️⃣ Scaling data platforms in enterprise environments 5️⃣ Lakebase & Apps usecases 6️⃣ Great conversations about what's hard and how to solve 📌 Join the Databricks User Group Bratislava and help shape the Databricks community if you are around. 🇸🇰 Pridaj sa k Databricks User Group Bratislava a pomôž formovať databricks komunitu na Slovensku. Ak si v okolí, toto je tvoja miestnosť. 🙏 This wouldn't have been possible without the great collaboration of Darius Rahimi from Databricks and the SK Adastra team led by Marius Maslo and Luboslav Gabal. Proud to be building this together. More to come. #Databricks #DataEngineering #Slovakia #Bratislava #Lakehouse #DataCommunity #AI #LLM #GenAI #Kosice #Slovensko #DatabricksKomunita #SlovenskaKomunita Adastra
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George Pirvu hat dies geteiltYou need to install a critical library for your Databricks workloads on the shared cluster. For example Oracle jdbc connector. You found the right one in the repository but when trying to install it you get: ❌ "JARs and Maven libraries on shared clusters must be on the allowed list." What does this even means? 💡 Here is why the allow list is necessary. Shared clusters are used by entire teams. One bad library can take everyone down. So Databricks says: nothing gets in unless someone with the right privileges explicitly allows it. That's the allow list. Think of it like a building with a guest list at the door. Doesn't matter how legitimate you are. If you're not on the list, you're not getting in. Same with the library. 📽️ I recorded a short video showing exactly how to do it. It might save you some time. Link in the comments. 👇 #Databricks #DataEngineering #DataGovernance #Unitycatalog #Azure #BigData #DataPlatform #CloudComputing #DataManagement #Spark
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George Pirvu gefällt dasOh yeah, world models are energy-based factor graphs. I even use the common factor graph symbols in my diagram: filled circles for observed variables, hollow circles for latents, rectangles for factors (additive energy terms). But I also add boxes with a rounded side (like a NAND gate) to represent functions (e.g. a neural net). I think this is necessary to indicate which variables are easily computed from others (by forward prop) and which are not (e.g. inferring latents by optimization, variational inference or whatever).George Pirvu gefällt dasThere is a lot of excitement around world models, JEPA, and energy-based approaches to intelligence. From a robotics point of view, this feels very familiar. Factor graphs already give us a structured way to represent dynamics, measurements, and objectives, and optimization already lets us do both estimation and planning. In a new blog post, https://lnkd.in/eh-FSAFR, I argue that if we anchor factor graphs at the present moment, we get a clean picture: - the recent past as a fixed-lag smoothing problem for perception - the near future as an MPC-style planning problem both tied together by the same state, dynamics, and optimization machinery I call this STAG: Sense-Think-Act via factor Graphs. Curious what people think, especially those working on world models, energy-based models, and robot planning.
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George Pirvu gefällt dasGeorge Pirvu gefällt daswe at Databricks love being with customers. It is always the best part of my job…especially the day today with PUMA Group at their HQ. Thx for having us Govind, Jan, Mihai & team - Sydne-Aline, Oliver, Thomas & I really enjoyed the time! ✨🤘🏻
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George Pirvu gefällt dasGeorge Pirvu gefällt dasWhat’s the opposite of speed dating? 🙃 We are at Microsoft today, taking part in Cloud & AI Customer Partner Meet-Up. A full day of 1:1 meetings discussing: ✔️Enterprise AI Platform: Scaling AI Safely Across the Organization ✔️AI for Logistics Optimization ✔️Chat with Your Data: AI-powered analytics for business users ✔️Building a Modern Data Platform, and more! 🔥 And the best thing is that we can really go in depth in these discussions, as we have 60 minutes per meeting. 🚀 To help clients get started and move from talk to action quickly, Adastra can secure a range of Microsoft client acceleration funds to support customer projects across both pre-sales and delivery phases. Ping me if this resonates. We’ve got you covered. 🙌 #Microsoft #Adastra #Cloud #Data #AI #AIGovernance Soňa Nováková Emilie Luedecke Petr Bergl Stanislav Kosík Martin Schaefer Zdeněk Soldán ✌︎ Jaroslav Škrabálek Ondrej Melichar Eduard Pfeiler Petr Žikovský Frantisek Raszyk Alzbeta Bartonickova David Frantik Andrea Barešová Karel Florian Ali Adil Alp Eren Kaplan
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George Pirvu gefällt das𝗗𝗲𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗼𝗱𝗲𝗿 𝗨𝗽𝘀𝗸𝗶𝗹𝗹𝗶𝗻𝗴 𝗱𝘂𝗿𝗰𝗵 𝗞𝗜? Diese Frage stand im Panel der Plattform Lernende Systeme - Germany's AI Platform auf der DMEA - Connecting Digital Health im Raum – insbesondere mit Blick auf die Ausbildung in Medizin und Pflege. 𝐌𝐞𝐢𝐧 𝐏𝐮𝐧𝐤𝐭 𝐚𝐮𝐬 𝐝𝐞𝐫 𝐃𝐢𝐬𝐤𝐮𝐬𝐬𝐢𝐨𝐧 KI verändert nicht nur, wie wir Aufgaben lösen – sondern vor allem, ob wir dabei lernen. 𝐃𝐚𝐬 𝐳𝐞𝐢𝐠𝐭 𝐬𝐢𝐜𝐡 𝐤𝐨𝐧𝐤𝐫𝐞𝐭 𝐢𝐧 𝐝𝐞𝐫 𝐈𝐧𝐭𝐞𝐫𝐚𝐤𝐭𝐢𝐨𝐧 𝐦𝐢𝐭 𝐊𝐈: - „Mach das für mich“ - also Aufgaben delegieren. - „Zeig mir die Logik dahinter – warum funktioniert mein Ansatz noch nicht?“ – um Lösungen und Konzepte zu verstehen. Im ersten Fall wird Arbeit abgegeben - mit Risiko zum Deskilling. Im zweiten erweitern wir unsere Kompetenzen. Wenn wir KI einsetzen, können wir über Tools und Inhalte sprechen. Wir müssen aber auch festlegen und messen, wie damit gearbeitet wird - wenn wir Fähigkeiten fördern wollen. 𝐒𝐨𝐧𝐬𝐭 𝐨𝐩𝐭𝐢𝐦𝐢𝐞𝐫𝐞𝐧 𝐰𝐢𝐫 𝐚𝐮𝐟 𝐆𝐞𝐬𝐜𝐡𝐰𝐢𝐧𝐝𝐢𝐠𝐤𝐞𝐢𝐭 – 𝐮𝐧𝐝 𝐯𝐞𝐫𝐥𝐢𝐞𝐫𝐞𝐧 𝐝𝐚𝐛𝐞𝐢 𝐅ä𝐡𝐢𝐠𝐤𝐞𝐢𝐭𝐞𝐧. Danke für die spannende gemeinsame Diskussion an Klemens Budde, Andrea Schmidt-Rumposch und Bjoern Eskofier sowie für die Vorbereitung an das PLS-Team Pia Schroeder, Christine Wirth und Thomas Steiner. #DMEA #KI #PatientCare #Gesundheitswesen #Medizin #Pflege #DigitalHealth #Ausbildung #Ottobock
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George Pirvu gefällt dasMit Norman Hofer und seinem Bereich habe ich 4+ Jahre als Kunde zusammengearbeitet. Was uns immer verbunden hat: die Überzeugung, dass Data & AI nur dann Wirkung zeigen, wenn sie nachhaltig verankert sind – nicht als Buzzword, sondern als Arbeitsweise. Data & AI nachhaltig mit der Marke msg zu verknüpfen, daran arbeiten wir nun gemeinsam 🚀 Danke Norman, dass Du extra nach Wien gekommen bist, um mich willkommen zu heißen. Sagt einiges über die Kultur, in die ich einsteige. 🤝 msg Plaut Austria GmbH msg #data #ai #softwareGeorge Pirvu gefällt dasSeit 4+ Jahren arbeiten wir in der Kunde-Dienstleister-Konstellation zusammen. 𝗡𝘂𝗻 𝗮𝗹𝘀 𝗞𝗼𝗹𝗹𝗲𝗴𝗲𝗻! 🤝 Ich freue mich wahnsinnig, Dich bei msg begrüßen zu dürfen und gemeinsam mit Dir unsere #Data, #Analytics und #AI Präsenz in Österreich auszubauen. Ein Post, der so dermaßen von Herzen kommt lieber Daniel Wallner! 𝙒𝙚𝙡𝙘𝙤𝙢𝙚 𝙩𝙤 𝙢𝙨𝙜 🚀
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George Pirvu hat darauf reagiertGeorge Pirvu hat darauf reagiertOh noes.. The Mythos Superior AI that Misanthropic Dario was too aftaid to release to public, got hacked.. So first Claude Code leaked its own code (blamed on 'human error' bs) and now it comes out Mythos is Greek for Swiss Cheese.. 😬 So much for AGI claims again. Or maybe they were prompting it all wrong.. The truth is its just another LLM trained with carefully curated, 'distilled', and labeled security code and tooling datasets.. Still drifts. Still only serves to increase your org's attack surface. Could be a cute PenTest tool. #ai
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George Pirvu gefällt dasGeorge Pirvu gefällt dasEveryone talks about the future of industry. Here, it’s being built. Those who turn data into action will lead — the rest will follow. #HMI
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George Pirvu gefällt dasGeorge Pirvu gefällt dasWhat makes Seed IQ so effective in Quantum Error Correction.. Most standard quantum error correction (QEC) is reactive by design. Surface code workflows, stabilizer loops, minimum weight perfect matching (MWPM) + union find (UF), all follow the same logic.. Measure syndromes, infer what happened, apply a correction. The system acts only after deviation or error has already happened. It reconstructs failure from what is left behind rather than governing evolution before instability compounds. That is the core limitation. If MWPM + UF were enough, fault tolerant quantum computing (FTQC) would already be here. They resolve manifested error but they do not maintain an ongoing forward hypothesis structure over evolving system state, and they do not determine whether the current evolution should be allowed to continue, be redirected, or left alone before decoherence amplifies. Seed IQ does. Its strength is not better post hoc decoding. Its strength is continuous sensing of the system, maintenance of concurrent hypotheses over possible forward evolution, and real time determination of whether the present trajectory remains admissible under the adaptive and fixed priors, and constraints governing coherence preservation. The priors matter because they define what valid evolution looks like, which transitions remain acceptable, which perturbations are tolerable, and which deviations indicate genuine departure from the goal. Not every deviation is something that should be touched. Some deviations or errors remain inside admissible evolution and will relax without intervention. Others are the early formation of decoherence and need redirection before they propagate. A standard reactive decoder does not really know that until the problem has already expressed itself as syndrome structure. Seed IQ is operating earlier than that. It is not waiting for full manifestation. It is maintaining hypotheses over evolving state and determining whether the system is still moving in a coherence preserving way or whether intervention has become necessary. So Seed IQ is not simply correcting errors. It is governing evolution. That is also where the multiagent advantage comes from, or the distributed coordination of Adaptive Multiagent Autonomous Control. Each agent is local in sensing and updating, but all agents operate under the same priors and constraints. They are not isolated local decoders, and they do not need heavy explicit message passing or consensus overhead. Coordination is implicit because they are all participating in the same admissible mathematical structure. Global coherence emerges from locally consistent belief propagation across the system. Standard QEC detects, reconstructs, and corrects. Seed IQ senses, maintains hypotheses, determines whether evolution remains admissible, and intervenes only when coherence preserving execution is at risk. Which is why it scales and performs so much more effectively. AIX Global Innovations #ai #quantum
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George Pirvu gefällt dasFragmented data. Inconsistent reporting. Delayed decisions. Most organizations don’t have a reporting problem-they have a data foundation problem. At Adastra, we helped design and deliver numerous enterprise-scale data platforms that unify data across on-prem and cloud, enabling: A single, trusted source of truth Faster, decision-ready insights Scalable self-service analytics Reduced reliance on legacy systems The impact is tangible-better decisions, faster. Because real transformation doesn’t start with dashboards. It starts with getting the data right. #DigitalTransformations AdastraGeorge Pirvu gefällt dasAt Volkswagen Group IT data was distributed across multiple systems – both on-prem and in the cloud. Reporting required manual consolidation, which slowed down access to insights and made it harder to work with consistent data. Adastra implemented a centralized IT4IT platform that connects data ingestion, processing, and reporting into one framework. Under the hood: - Azure-based architecture combining cloud and on-prem data - Azure Data Factory pipelines orchestrating data ingestion and transformation - Power BI Service for reporting and self-service access - Governance with role-based access and audit capabilities What this enabled: ✔️ consistent data across reporting ✔️ faster report delivery ✔️ access to data for non-technical users ✔️ reduced dependency on on-prem infrastructure A good reminder that reporting is not just about visualization – it’s about how data is integrated and prepared upstream. 👉 Full story: https://lnkd.in/dQbZkfsw
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andamp
2331 Follower:innen
𝗕𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗦𝗰𝗲𝗻𝗲𝘀: 𝗠𝗶𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 𝗔𝘇𝘂𝗿𝗲 𝗖𝗼𝘀𝗺𝗼𝘀 𝗗𝗕 𝘁𝗼 𝗣𝗼𝘀𝘁𝗴𝗿𝗲𝗦𝗤𝗟 What happens when a "simple" CSV upload takes 20 minutes to process? 😬 In our latest blog post, Katharina Bühn breaks down the migration journey from Azure Cosmos DB to PostgreSQL—and why sometimes the best solution isn't the newest technology, but the one that actually solves your problems. Spoiler: We went from 20+ minutes to under 30 seconds. 🔗 Find the link to the blog post in the comments! 👇 #Blogpost #CosmosDB #PostgreSQL #TechInsights #EngineeringJourney
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1 Kommentar -
JCON
1572 Follower:innen
Stop the #Hibernate bottleneck! Markus Kett’s keynote "Java's Ignored Potential" reveals how to transform sluggish #databases into lightning-fast in-memory powerhouses. #JVM speed, simplified dev, massive scalability—your apps deserve it! 🎥 https://lnkd.in/e6BmAMSH #JCON2025 #Java #JCON MicroStream
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JCON
1572 Follower:innen
Stop the #Hibernate bottleneck! Markus Kett’s keynote "Java's Ignored Potential" reveals how to transform sluggish #databases into lightning-fast in-memory powerhouses. #JVM speed, simplified dev, massive scalability—your apps deserve it! 🎥 https://lnkd.in/e6BmAMSH #JCON2025 #Java #JCON
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Fredrik Svensson
Västra Götalandsregionen • 1573 Follower:innen
Thought-provoking piece by Åke Edlund In fast-moving AI and agentic systems, static diagrams age fast. Treating code as an architectural instrument and validating decisions through executable proof feels like a necessary evolution of enterprise architecture, not a rejection of it. Is code first, then document becoming essential for EA to stay relevant? #EnterpriseArchitecture #TOGAF #AIArchitecture #Architecting #TechStrategy #CodeFirst #ArchitectureDecisions https://lnkd.in/e_4MTk8R
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Kieran Green
Barclay Simpson • 12.198 Follower:innen
Germany's Cloud Security Scene is Heating Up! AWS is rapidly becoming the go-to hub for cloud security in Germany, and it's not just about storage anymore. With enterprises leaning into multi-hybrid cloud strategies, AWS is now the centrepiece of secure, scalable, and flexible infrastructures across industries. From finance to manufacturing, German companies are locking in on AWS to keep their data safe while staying agile. Key Highlights: 1)AWS is leading the charge in multi-hybrid cloud adoption across Europe. 2)Enhanced compliance and security standards tailored for German and EU regulations. 3)Growing demand for cloud-native security solutions in hybrid environments Looking to break into cloud security? I’m currently hiring for cloud security roles ranging from €100K–€150K – from cloud architects to security engineers.
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2 Kommentare -
tecRacer
3000 Follower:innen
Lambda or container? You don’t always have to choose! 🦎 At the beginning of an AWS software development project, some major architectural decisions have to be made. One classic question: does AWS Lambda or Docker/Microservices make more sense? In this blog post, our colleague Gernot Glawe shows how you can use the same code in both environments using Go, Gin, and the Ginadapter. 🔄 Learn more here ► https://lnkd.in/etNc_f9C #AWS #GO #AWSLambda #Docker #Container #Cloud #Gin #Serverless
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Performetriks
4329 Follower:innen
If you’re a performance engineer wanting to know where to focus performance efforts for maximum impact, 🚀 check out this hands-on demo with Andreas (Andi) Grabner and Josef Mayrhofer. Josef showcases a custom Service Scorecard Dashboard built on Dynatrace Grail for trace-based analytics. With this dashboard, performance engineers can: ✅ Instantly identify “chatty” services ✅ Visualize the slowest queries ✅ Perform service-by-service analysis ✅ Drill down endpoint by endpoint ✅ Quickly pinpoint main performance bottlenecks 📽️ Watch the full video to see how we use it on a day-to-day basis. #Observability #PerformanceEngineering #Dynatrace #Grail #DevOps #SRE #Monitoring
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1 Kommentar -
Splendid Data
438 Follower:innen
We’re excited to announce a strategic partnership between credativ GmbH and Splendid Data. Together, we help enterprises modernize complex Oracle database landscapes by migrating to pure, enterprise-grade PostgreSQL, in a predictable, scalable and future-proof way. This partnership directly addresses: • Eliminating excessive Oracle licensing costs • The need for autonomy and digital sovereignty • Preparing data platforms for AI-driven workloads By combining Splendid Data’s Cortex migration automation with credativ’s deep PostgreSQL expertise and 24×7 enterprise operations, we set a new standard for large-scale, open-source database modernization in Europe. Initial focus is Germany, with a clear path to scale internationally. Read the full press release here: https://lnkd.in/eGJmRdqD #PostgreSQL #OracleMigration #OpenSource #DigitalSovereignty #EnterpriseIT
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Grouper Networks
667 Follower:innen
Snowflake is a cloud-based data platform that unifies data warehousing, data lakes, data engineering, and data sharing into a single service. It allows organizations to store, analyze, and securely exchange their data at scale. Snowflake is designed to handle diverse data workloads and analytics needs, making it an all-inclusive solution for data management. Here's a more detailed breakdown of what Snowflake does: Key Functions: Data Warehousing: Snowflake serves as a central repository for storing and analyzing large volumes of data. Data Lakes: It supports the creation and management of data lakes, where raw data is stored in its native format. Data Engineering: Snowflake provides tools and capabilities for building data pipelines and transforming data. Data Sharing: Snowflake allows organizations to securely share data with other organizations or internal teams. Data-Driven Applications: Snowflake enables the development of data-driven applications that leverage the power of its data platform. Analytics: Snowflake supports various analytical workloads, including business intelligence, data science, and machine learning. Key Features: Cloud-Native Architecture: Snowflake is built on a cloud-native architecture, leveraging the capabilities of public cloud platforms like AWS, Azure, and GCP. Separated Compute and Storage: Snowflake decouples compute and storage, allowing for scalable, on-demand analytics. Massively Parallel Processing (MPP): Snowflake utilizes MPP to distribute workloads across multiple processing units, enhancing performance and scalability. SQL Query Engine: Snowflake features a powerful SQL query engine that allows users to interact with data using familiar SQL syntax. Data Sharing and Collaboration: Snowflake offers a marketplace where organizations can discover, consume, and share live data. AI Integration: Snowflake integrates AI capabilities, enabling users to build and train AI models directly within the platform. In essence, Snowflake provides a comprehensive data platform for organizations looking to modernize their data management and analytics workflows.
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Rino Mentil
Informatec • 15.054 Follower:innen
SKAN AG, together with Informatec, successfully stabilized and strategically enhanced its Microsoft-based BI landscape. A modernized ETL architecture, consistent data models, and targeted knowledge building have laid the foundation for a sustainable and future-proof BI organization. ➡️ Read the full success story: https://lnkd.in/gnPvdzys #SKAN #Skangroup #PowerBI #BusinessIntelligence #DataArchitecture #ETL #SelfServiceBI #MicrosoftBI SKAN
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Phil Seamark
Microsoft • 12.069 Follower:innen
Many teams in and around Hanau are already building Semantic Models in Microsoft Fabric, but too few are taking full advantage of Direct Lake. If you are working with Fabric, Direct Lake should be your default approach for delivering fast, scalable models that avoid unnecessary duplication and refresh overhead. At SQL Konferenz this March, I’m running a full day workshop focused entirely on building, optimising and managing Direct Lake Semantic Models. We will look at practical patterns; performance techniques and the operational guidance you need to run these models confidently in production. If Direct Lake is part of your roadmap, you should be in this room. Registration details are on the SQL Konferenz website. Register via SQL Konferenz: https://lnkd.in/enfKdgt7 #SQLKonferenz2026 #MicrosoftFabric #DirectLake #PowerBI #SemanticModels #DataModeling #Hanau
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3 Kommentare -
ITuziast
393 Follower:innen
#Article: Architect #AIWorkloads in Microsoft Azure (Part 2) by Dimitar Grozdanov Part 1 was about frameworks, tools and why it matters. Part 2 explores more advanced strategies for designing scalable, secure, and efficient #AIworkloads on Microsoft Azure, including practical patterns and governance considerations. Check out the article and let us know how you’re embedding AI workloads in your organization. https://lnkd.in/dnxHKkD3 #CloudAdoption #CloudStrategy #CloudArchitecture
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Paresh Nana
Databricks • 1527 Follower:innen
SRF is advancing public service media in Switzerland with the Databricks Data Intelligence Platform. By centralizing and analyzing 90 billion data points and processing 200GB daily, SRF delivers timely, relevant content to 1.3 million unique users daily. With solutions like Delta Lake, Lakeflow, and Unity Catalog, SRF has accelerated insights, improved collaboration, and empowered teams to make data-driven decisions that enhance the media experience for Swiss citizens.
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Johnathan Brierley
Metric DCX • 8910 Follower:innen
Recent reports indicate that Oracle and OpenAI have dropped plans to expand the flagship Stargate data center campus in Abilene, a site originally expected to add around 600MW of additional AI capacity. Instead, Meta is reportedly in early talks to take over some of the unused capacity, with NVIDIA helping facilitate discussions. While the Abilene expansion has stalled, the broader Stargate initiative remains intact, with capacity still expected to be built across multiple sites. https://lnkd.in/eHbeKKBU
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Aleksandro Matejic
Lime Technologies • 1440 Follower:innen
Did you know? Running Amazon EKS at a larger scale can lead to challenges related to IP address limits, impacting the number of pods that can operate on a node. 👉 https://lnkd.in/epH8e5Bn #AWS #Containers #Docker #CloudNative #PlatformEngineering #Kubernetes
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12 Kommentare -
Kubermatic
10.334 Follower:innen
How CID GmbH drives digital innovation with Kubernetes automation. 🚀 Standardizing infrastructure is the first step toward true scalability. At #CDS25, we spoke with Rouven Loerch, General Manager at CID GmbH, about their cloud-native journey. In this interview, Rouven breaks down how CID is using Kubermatic Kubernetes Platform (KKP) to: ✅Modernize Infrastructure: Moving from legacy setups to agile, cloud-native systems. ✅ Standardize Operations: Managing multiple environments with a unified approach. ✅ Accelerate Delivery: Freeing up teams to focus on innovation rather than cluster maintenance. ✅Multi-tenancy approach to load balancing, providing seamless scalability, security, and management for distributed applications and teams achieved with KubeLB. If you are a tech leader looking to streamline your cloud strategy, this is a must-watch. 📺 Watch the full interview: https://hubs.li/Q046TzNJ0 #Kubernetes #DigitalTransformation #CloudNative #Leadership #CID #Kubermatic #Automation
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M&M Software Development Centre India
1246 Follower:innen
𝐓𝐡𝐞 𝐭𝐡𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐉𝐮𝐩𝐲𝐭𝐞𝐫 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤𝐬... In the beginning, everything is easy: testing code, visualizing data, implementing ideas immediately. But with every new feature, the complexity grows. At some point, #Jupyter notebooks reach their limits. Our data engineer Bianca Leßmann shares her experiences in the blog and explains when classic #Python scripts are the better choice: https://lnkd.in/gJsBfQyC #DataEngineering #JupyterNotebooks #DataScience #DigitalizationbyMM
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