Data and AI for Intelligent Cloud Basel

🎯 The Problem: Your batch ETL runs overnight. But production issues happen at 2 PM. By the time you see them in your dashboard, it's already too late. Sound familiar?
💡 This Evening: We'll build a complete real-time pipeline from scratch - from event ingestion to live dashboards. No Kafka expertise required. Fabric handles the complexity.
⏱️ Save yourself: Months of learning Azure Data Explorer, Event Hubs, and Kafka. Real-Time Intelligence brings it all together in one place.
What you'll learn:
🏠 Eventhouse - The real-time data store optimized for time-series, and WHY Eventhouse uses Kusto under the hood (Azure Data Explorer heritage)
🌊 Eventstreams - No-code streaming pipelines, and WHY Eventstreams abstract away Kafka complexity
📊 KQL Database - Time-series and log analytics at scale, and WHY KQL outperforms SQL for time-based queries
🔍 KQL Querysets - Mastering Kusto Query Language, and WHY KQL syntax is different from SQL (but easier for time-series)
End-to-End Demo: IoT Device → Event Hub → Eventstream → Eventhouse → Real-Time Dashboard → Data Activator (Alerts)
Who should attend: IoT developers building sensor analytics, DevOps engineers analyzing logs, Analysts working with time-series data
Agenda:

• 18:30 - Welcome & Networking
• 18:45 - Eventhouse & KQL Fundamentals
• 19:10 - Eventstreams - Building the Pipeline
• 19:30 - Live Demo: IoT to Dashboard
• 19:50 - KQL Tips & Tricks
• 19:55 - Q&A and Discussion
• 20:00 - Networking

🎯 The Problem: Your alerts fire - but by the time someone reads the email, the damage is done. You need systems that don't just notify, but ACT. Automatically. In real-time.
💡 This Evening: We'll build proactive monitoring that triggers Power Automate flows, sends Teams messages, or even shuts down machines - all without writing code.
⏱️ Save yourself: Building custom alert logic with Azure Functions. Data Activator does it in minutes with a visual editor.
What you'll learn:
🎯 Real-Time Hub - Central catalog for all streaming data, and WHY a unified hub matters for discoverability across teams
📊 Real-Time Dashboards - Live visualizations with auto-refresh, and WHY Real-Time Dashboards differ from Power BI (hint: tile-based vs report-based)
🔔 Data Activator - No-code alerting and automation, and WHY Data Activator replaces custom alert logic with visual rules
📋 Event Schema Sets - Structure for streaming data, and WHY schemas prevent downstream chaos when sources change
Use Case Demo: Sensor Data → Eventstream → Eventhouse → Data Activator (IF temperature > 80°C) → Email Alert + Power Automate (Shut down machine)
Who should attend: Operations teams building monitoring, IoT developers implementing alerting, Business analysts creating KPI alerts
Agenda:

• 18:30 - Welcome & Networking
• 18:45 - Real-Time Hub Overview
• 19:00 - Real-Time Dashboards vs Power BI
• 19:20 - Data Activator Deep Dive
• 19:40 - Live Demo: Alert → Action Pipeline
• 19:55 - Q&A and Discussion
• 20:00 - Networking

🎯 The Problem: Your boss says "just add AI to our reports." Your users want to "ask questions in natural language." But you're not a data scientist, and ChatGPT doesn't know your data. Where do you even start?
💡 This Evening: We'll show you the AI spectrum in Fabric - from traditional ML to conversational analytics - and which approach fits which use case. No PhD required.
⏱️ Save yourself: The frustration of picking the wrong AI tool. Copilot, AI Functions, and Data Agents solve very different problems.
What you'll learn:
🤖 ML Models - Train and deploy models in Fabric, and WHY MLflow is the backbone of Fabric ML (experiment tracking, model registry)
🧪 Experiments - Track runs, compare models, version artifacts, and WHY experiment tracking prevents ML chaos
✨ Copilot in Fabric - AI-assisted development across all workloads, and WHY Copilot context matters for quality (semantic model awareness)
🧠 AI Functions - Call LLMs from T-SQL and Spark, and WHY AI Functions democratize GenAI for SQL developers
💬 Data Agents - Conversational analytics with natural language, and WHY agents beat chatbots for data Q&A (they understand your schema)
AI Spectrum: Traditional ML (Train models) → Experiments (Track) → AI Functions (Call LLMs) → Data Agents (Conversational)
Who should attend: Data Scientists exploring Fabric, Analysts curious about AI capabilities, Developers building AI-powered apps
Agenda:

• 18:30 - Welcome & Networking
• 18:45 - ML Models & Experiments
• 19:10 - Copilot Across Fabric
• 19:25 - AI Functions Demo
• 19:40 - Data Agents - The Future of BI
• 19:55 - Q&A and Discussion
• 20:00 - Networking

🎯 The Problem: Import mode is fast but stale. DirectQuery is fresh but slow. You've been making this tradeoff for years. What if you didn't have to choose anymore?
💡 This Evening: Direct Lake changes everything. We'll show you how it delivers Import-speed performance with DirectQuery-level freshness - and why it's the future of Power BI.
⏱️ Save yourself: Weeks of performance tuning. Direct Lake + proper semantic model design = reports that just work.
What you'll learn:
📊 Semantic Models - Direct Lake, Import, and DirectQuery modes, and WHY Direct Lake uses Delta parquet files directly (no data copy!)
📈 Reports - Best practices for Fabric-native reports, and WHY report design matters for Direct Lake performance (hint: fewer visuals = faster)
📋 Dashboards vs Reports - When to pin vs when to build, and WHY dashboards are being deprecated in favor of reports
📄 Paginated Reports - Pixel-perfect printing and exports, and WHY paginated reports still matter for invoices, statements, regulatory docs
🎯 Scorecards - KPI tracking and goal management, and WHY metrics need a dedicated home (not buried in reports)
Mode Comparison:

• Import: Scheduled refresh, fast, limited size
• DirectQuery: Real-time, slow, unlimited size
• Direct Lake: Real-time AND fast AND large datasets

Who should attend: Power BI developers migrating to Fabric, Report authors optimizing performance, Architects designing semantic layer
Agenda:

• 18:30 - Welcome & Networking
• 18:45 - Direct Lake Deep Dive
• 19:15 - Reports, Dashboards, Scorecards
• 19:35 - Paginated Reports Use Cases
• 19:50 - Performance Tips
• 19:55 - Q&A and Discussion
• 20:00 - Networking