Victor Buzy

🔍 Fabric & Databricks: Better Together — A Modern Data Platform Strategy   Microsoft Fabric and Databricks are reshaping how organizations build, scale, and operationalize modern data platforms. The number one question we hear is: How do they actually work together — and where should you start?   Join William Richard, Ian Dicker, and Brendon Collins for a webinar that explores how Microsoft Fabric and Databricks complement each other across analytics, AI, and data engineering — and how leading organizations are using both to move faster, reduce complexity, and drive real business outcomes.   ✅ What you’ll walk away with: -Clarity on when to use Fabric, when to use Databricks, and when to use both -A modern reference architecture for analytics + AI -Guidance for organizations evaluating, modernizing, or scaling their data platform If you’re responsible for data strategy, analytics, or AI enablement — this session is for you.   👉 Save your spot: https://lnkd.in/df23gstC   #MicrosoftFabric #Databricks #ModernDataPlatform #DataStrategy #Analytics #AI #SpyglassMTG

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🚀 I just launched a new course to help you pass the Databricks Generative AI Engineer Associate certification — and go beyond just passing the exam. This course is built for developers (software engineers, data scientists, data engineers) who have already built POCs with LangChain, LlamaIndex, or CrewAI and want to take that work all the way to production. Here's what we cover: - Vector Search — semantic retrieval done right - Model Serving — real-time LLM deployment - MLflow — managing the full model lifecycle - Unity Catalog — data governance at scale - RAG pipelines & LLM chains — end to end, in production All examples run on the Databricks Free Edition — no paid cluster needed. Theory + hands-on workshops + a GitHub project to keep as a reference. If you're ready to validate your GenAI production skills on Databricks, this course is for you. 👇 Link in the comments — happy learning! #Databricks #GenerativeAI #LLM #RAG #MLflow #DataEngineering #MachineLearning #AIEngineering #Udemy #LangChain #DataScience #Certification

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Most Databricks teams can see their costs. Explaining them is a different problem entirely. Visibility is having the numbers — DBU reports, cloud invoices, usage dashboards. Most teams have all of that. Explainability is being able to answer the questions those numbers raise: → Which job drove last month's spike? → Which team owns the spend? → Did that cluster optimisation actually reduce total cost — or just shift it from the Databricks bill to the cloud bill? Neither billing system answers those questions on its own. And bridging them takes more than a notebook. Our latest article breaks down exactly why — the structural reason the two systems can't reconcile themselves, and why having the data is not the same as having the answer. Full article linked in the comments → #Databricks #FinOps #DataEngineering #CostObservability #CloudCost #DataPlatform #lumin8

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We took the most-upvoted questions from r/dataengineering and brought them to 4 senior data engineers with 40+ years of combined experience. Benjamin Rogojan , Julien Hurault, Mehdi Ouazza, and Simon Späti answered 10 questions about the real challenges of data engineering work. Interview prep. Data quality. Schema changes. Real-time dashboards. Taking over abandoned data stacks. Cloud costs. Swipe through for all 10 questions. Full answers here: https://lnkd.in/gBJg-wTX

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Don't just build data pipelines; build data confidence. A robust pipeline that delivers flawed data is worse than no pipeline at all. #DataEngineeringTips

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Databricks isn’t just a data platform — it’s a unified workspace built for AI, ML, and data engineering teams. Here’s why I recommend learning it in 2025: ✅ Collaborative notebooks (Python, SQL, Scala, R) ✅ Easy model training & experiment tracking ✅ Built-in MLflow integration ✅ Scales effortlessly with Spark clusters ✅ Supports Delta Lake for reliable data pipelines ✅ Ideal for building production-grade AI solutions Whether you're building predictive models or real-time ML pipelines, Databricks helps you scale — from laptop to enterprise. 💡 My advice: Start with their free Community Edition and run your first ML pipeline! Have you tried Databricks yet? Comment below your favorite feature 👇 #Databricks #AItools #MLengineering #MLOps #MachineLearning #Spark #DeltaLake #MLflow #edAtoZ #AICareer

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The data silo wars just ended. Microsoft and Databricks dropped something huge this week that changes everything about how we think about data platforms. For years, we've been stuck choosing: → Best-in-class analytics OR seamless integration → Power BI's speed OR Databricks' AI capabilities → OneLake's simplicity OR Unity Catalog's governance Not anymore. Here's what just happened: OneLake now supports Unity Catalog Open APIs. This means zero-copy data sharing between Databricks and Fabric both ways. Your Databricks tables? Instantly available in Power BI Direct Lake mode. Your OneLake data? Seamlessly accessible in Databricks notebooks. No ETL. No duplication. No choosing. Why this matters: This isn't just another integration, it's a fundamental shift toward true interoperability using open standards. When two giants like Microsoft and Databricks embrace openness over lock-in, everyone wins. The practical impact: → Databricks already mirrors to OneLake (GA) → Reading OneLake from Databricks coming by year-end → All governed by open APIs (Unity Catalog + Iceberg REST) Your data engineers can build in Databricks. Your analysts can visualize in Power BI. Your business users can access insights in Teams or Excel. Same data. No compromise. The future of data platforms isn't about vendor lock-in, it's about choosing the right tool for each job while keeping your data unified and governed. What integration would make your data stack more powerful? #Databricks #MicrosoftFabric #OneLake #UnityCatalog #CollectivInsights

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Dataiku Expands Enterprise AI Capabilities With Cobuild on Snowflake “Consumer AI tools can make code appear instantly, but enterprises cannot afford to unleash opaque, unvalidated workflows into environments where accuracy, compliance, safety, and cost control matter,” explained Florian Douetteau, co-founder and CEO of Dataiku. “With Cobuild on Snowflake, organizations can bring the speed of AI-assisted development into a governed process with visual workflows that teams can inspect, validate, and improve before production.” Read More: https://lnkd.in/dp6F6dyK #AI #AIApplications #AITech365 #Cobuild #Dataiku #enterpriseAI #machinelearning #news #productionworkflows #Snowflake

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Databricks has joined STATION F to accelerate AI adoption among European founders, opening a dedicated office on campus and giving startups access to training, coaching, office hours, and technical sessions. The partnership is designed to help founders build enterprise-ready AI applications and agents on their data using Databricks tools, including Lakebase and Genie. Led by VP Nico Gaviola in EMEA’s emerging enterprise business, Databricks provides a unified data and AI platform used to build analytics, apps, and AI agents at scale. Roxanne Varza said the partnership gives STATION F founders direct access to both Databricks’ technology and team, strengthening Europe’s startup ecosystem as demand for production-grade AI continues to grow. More at: https://lnkd.in/eHauaDTd #AI #Startups #DataAI #Europe

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💡 Databricks Quick Tips #3: Cell Tagging Welcome back to the Databricks Quick Tips Series! In this episode, we explore how Cell Tagging in Databricks can simplify collaboration and boost team productivity. Discover how to: 🔹 Assign tasks within notebook cells 🔹 Organize code for better reviews 🔹 Track changes more efficiently From clean documentation to seamless teamwork — small features can deliver big impact. We share practical data tips that help you work smarter, only at NICE Software Solutions. Pravin Pardhi Mohammad Abdul Hameed Sarthak Manalwar Sumit Chandwani Akshay Lodhi #Databricks #CellTagging #DataEngineering #NotebookTips #CollaborativeDataScience #ProductiveTeams #NICESoftwareSolutions #QuickTips

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Most SQL environments don’t struggle because of the database itself. They struggle because of everything built around it over the years as technical debt.   Your team shouldn’t need 5 different tools just to answer one business question.   Disconnected reporting layers, duplicate pipelines, manual governance process and growing maintenance overheads turn data operations into a bottleneck.   The real cost isn’t just infrastructure. It’s: • Engineering time spent maintaining fragmented systems • Slow reporting cycles delaying business decisions • Governance and compliance gaps across tools • Increasing complexity as data scales   Microsoft Fabric changes this approach by bringing data engineering, warehousing, analytics, governance, and BI into a unified platform. Instead of managing disconnected ecosystems, teams can focus on delivering insights faster and driving measurable business outcomes.   At WaferWire Cloud Technologies, we help organizations assess their current SQL landscape, identify modernization opportunities, and build a practical roadmap for modernization with Fabric. Contact us: https://lnkd.in/gcb7pJB2 #DataModernization #MicrosoftFabric #SQLServer #DigitalTransformation #WCT

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AI in financial services is moving beyond hype, into real execution. Our VP of Data + AI, Josue “Josh” Bogran, and Antoine Amend, FSI Sr. Technical Director at Databricks, break down what production-grade AI actually requires: ✅ From chat-driven experiences → workflow automation  ✅ From “AI-first” thinking → truly data-driven execution  ✅ From insights → action at scale  ✅ Built on security, compliance, and full data lineage The takeaway: AI delivers value when it’s embedded into business processes, not layered on top. At zeb, we focus on helping enterprises make this shift with scalable, production-ready architectures. 👉 Watch the full discussion: https://lnkd.in/dGUDC2aB #zeb #Databricks #AI #FinancialServices #EnterpriseAI

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🥳 Databahn.ai has joined forces 🦾 with Databricks to transform how organizations secure and act on their data—an impactful step in modernizing security operations and shaping the future of cybersecurity! 🚀 Learn more here 👉 https://lnkd.in/gu9nRZv8

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