"Understanding Data Centre Architecture" webinar on Purpose Day

What does a practical path to Microsoft Fabric adoption look like? This presentation explores real modernization journeys—how enterprises unified data, simplified architecture, and accelerated analytics with Fabric. If you’re considering Fabric, this presentation reveals what works in practice and what pitfalls to avoid. Watch now and see how Fabric can drive your transformation. https://lnkd.in/dEVHKXzt #MicrosoftFabric #DataLeaders #Analytics #CloudDataArchitecture #DataIntegration #DigitalTransformation

Microsoft Fabric is reshaping how organizations unify data and accelerate analytics. This presentation highlights practical adoption strategies, real-world examples, and key lessons learned along the way. A valuable resource for data and IT leaders planning their Fabric journey. View the presentation: https://lnkd.in/dEVHKXzt #MicrosoftFabric #DataLeaders #Analytics #CloudDataArchitecture #DataIntegration #DigitalTransformation

Adopting Microsoft Fabric is more than just a technology shift—it’s a strategic move. This presentation breaks down real modernization journeys, offering clear insights into what works and what to watch out for. A must-watch for anyone exploring Fabric adoption. Watch here: https://lnkd.in/dEVHKXzt #MicrosoftFabric #DataLeaders #Analytics #CloudDataArchitecture #DataIntegration #DigitalTransformation

Adopting Microsoft Fabric is more than just a technology shift—it’s a strategic move. This presentation breaks down real modernization journeys, offering clear insights into what works and what to watch out for. A must-watch for anyone exploring Fabric adoption. Watch here: https://lnkd.in/dEVHKXzt #MicrosoftFabric #DataLeaders #Analytics #CloudDataArchitecture #DataIntegration #DigitalTransformation

Adopting Microsoft Fabric is more than just a technology shift—it’s a strategic move. This presentation breaks down real modernization journeys, offering clear insights into what works and what to watch out for. A must-watch for anyone exploring Fabric adoption. Watch here: https://lnkd.in/dEVHKXzt #MicrosoftFabric #DataLeaders #Analytics #CloudDataArchitecture #DataIntegration #DigitalTransformation

🏗️ Building a Future-Ready Data Platform in Microsoft Fabric with the Medallion Approach Today’s data platforms are no longer just about collecting data—they’re about scaling efficiently, building trust, and empowering every user to make data-driven decisions. By combining Microsoft Fabric with the Medallion Architecture and a metadata-driven design, organizations can achieve all three. Here’s how: 🔹 Bronze – Capture raw data exactly as it arrives 🔹 Silver – Clean, validate, and harmonize data for consistency 🔹 Gold – Transform it into actionable, business-ready insights ✨ The magic ingredient? A robust metadata layer that automates workflows, simplifies engineering, and embeds governance from day one. The result: ✅ Consistent, trustworthy data products ✅ Faster delivery of analytics and AI insights ✅ A scalable, future-proof foundation that grows with your business This isn’t just data architecture—it’s a framework for intelligent, self-service, and resilient data ecosystems in Microsoft Fabric. #MicrosoftFabric #DataEngineering #MedallionArchitecture #ModernDataPlatform #Analytics #SQLFamily #Azure #MSFabric #Microsoft #Metadata #DataIntegration #MVPBuzz #MCT #MDP

Three insights that changed how I look at data after successfully completing the course “Data Infrastructure and Technology“ @ The University of Chicago Booth School of Business 1️⃣ „Data gravity” is stronger than you think - architecture must follow the data, not the other way around. Many organizations start by designing a cloud or lakehouse architecture first and then try to fit data sources into it. The Booth class opened my eyes for the reverse: your data’s location, velocity, and sensitivity dictate where compute and governance should live. Most leaders assume moving everything to one cloud is efficient. In practice, data gravity pulls applications, AI models, and even teams toward where the data already resides. Hybrid setups (e.g., combining on-prem sensors, Azure analytics, and edge inference) are often strategically optimal, not a compromise. Cloud strategy is a data strategy. Decoupling compute from storage isn’t just cost optimization - it’s about organizational agility and compliance boundaries. 2️⃣ The real differentiator are not pipelines but metadata and lineage intelligence. We learned to shift the focus from “how we move data” to “how to find out what our data means.” Even sophisticated companies think a strong ETL/ELT system equals maturity. The course highlights that discoverability, trust, and explainability (via metadata catalogs, data lineage tracking, and semantic layers) are what make AI auditable and scalable. Metadata management becomes a governance enabler - a prerequisite for AI compliance under ISO 42001 and the EU AI Act, not just an IT hygiene task. It’s how CAIOs turn “black box” data estates into transparent, certifiable systems. 3️⃣ Centralization fails unless paired with a federated accountability model. In class, Daryush Laqab distinguished between centralized ownership (data platform team) and distributed accountability (domain data owners). Many executives assume a single data office ensures quality and control. In practice, that creates bottlenecks. A data mesh-style governance - where domains own data as a product but comply with common standards - is what enables both autonomy and trust. Success metrics shift from “data lakes built” to “domains delivering certified data products.” The CAIO’s job is less “build the platform,” more “design the contract between domains and the platform.” René Greiner Angela LE MATHON Serkan Katilmis Partha Roarke Onkar Kumar Roshy Francis Pravin Pandey Kelly Trindel, PhD Mozhdeh Koushki Ericc Diaz Georg Langlotz Gurpreet Atwal Jason Massey Giovanni Stella Mayoor Mahendra Ward Thils Vara Wuyyuru Barrett Campbell Aleck Matambo, FCIPS Rajani Kanth Yalamanchili Benevides Chissanga Jeronimo Herrera Jatinder Singh Amit Panalkar Bruno Bonfatti Julie Kessel MD Kristian Schmidt Matt Crego Guru Venkataramu Anupam Prakash Darren Silver Tanie Eio, IHRP-MP Steven Bentz

Day 16 of my #buildinginpublic journey into Data Engineering Today, I explored the modern data architecture and how it differs from traditional setups. Here’s what I covered: - Modern vs. Traditional Architectures: Modern systems focus on scalability, flexibility, and handling both real-time & batch data. - Examples of Modern Architectures: * Lambda Architecture: Combines batch and real-time layers for speed and accuracy. * Kappa Architecture: Focuses on real-time streaming, removing the batch layer. * Data Mesh: Decentralized ownership of data, treating data as a product. * Data Fabric: A unified layer to integrate, manage, and govern data across platforms. Modern data architectures ensure organizations can handle massive, diverse, and fast-moving data while keeping systems reliable and scalable. #BuildingInPublic #DataEngineering #DataArchitecture #Lambda #Kappa #DataMesh #DataFabric