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🎯 The Problem: Your ETL jobs take 6 hours. Business users want data "as fresh as possible." You're juggling Dataflows, Notebooks, Pipelines, and now Mirroring - but which one is actually right for which scenario? 💡 This Evening: We'll cut through the confusion with a clear decision framework. You'll see each tool in action and understand when to use Spark vs. Dataflows vs. Mirroring - with real-world examples. ⏱️ Save yourself: Weeks of painful refactoring. Choose the right ETL approach from day one. What you'll learn: ⚙️ Spark Environments - Managing libraries and configurations\, and WHY environment isolation matters for production 🚀 Spark Job Definitions - Automating Spark jobs at scale\, and WHY scheduled jobs beat manual notebook runs 🔄 Data Factory Pipelines - Enterprise orchestration with activities\, and WHY Fabric Pipelines are simpler than classic ADF 🪞 Database Mirroring - Near real-time replication from SQL Server\, Cosmos DB\, Snowflake\, and WHY mirroring beats traditional CDC approaches Decision Framework:

• Simple transformations → Dataflow Gen2
• Complex logic, ML → Notebook → Spark Job
• Orchestration, dependencies → Data Pipeline
• Real-time sync from source → Mirroring

Who should attend: Data Engineers building ETL pipelines, DBAs managing data integration, Architects designing data platforms Agenda:

• 18:30 - Welcome & Networking
• 18:45 - Environments & Spark Jobs
• 19:10 - Data Factory Pipelines Deep Dive
• 19:35 - Database Mirroring - The Game Changer
• 19:50 - ETL Decision Framework
• 19:55 - Q&A and Discussion
• 20:00 - Networking

🎯 The Problem: Your ETL jobs take 6 hours. Business users want data "as fresh as possible." You're juggling Dataflows, Notebooks, Pipelines, and now Mirroring - but which one is actually right for which scenario? 💡 This Evening: We'll cut through the confusion with a clear decision framework. You'll see each tool in action and understand when to use Spark vs. Dataflows vs. Mirroring - with real-world examples. ⏱️ Save yourself: Weeks of painful refactoring. Choose the right ETL approach from day one. What you'll learn: ⚙️ Spark Environments - Managing libraries and configurations\, and WHY environment isolation matters for production 🚀 Spark Job Definitions - Automating Spark jobs at scale\, and WHY scheduled jobs beat manual notebook runs 🔄 Data Factory Pipelines - Enterprise orchestration with activities\, and WHY Fabric Pipelines are simpler than classic ADF 🪞 Database Mirroring - Near real-time replication from SQL Server\, Cosmos DB\, Snowflake\, and WHY mirroring beats traditional CDC approaches Decision Framework:

• Simple transformations → Dataflow Gen2
• Complex logic, ML → Notebook → Spark Job
• Orchestration, dependencies → Data Pipeline
• Real-time sync from source → Mirroring

Who should attend: Data Engineers building ETL pipelines, DBAs managing data integration, Architects designing data platforms Agenda:

• 18:30 - Welcome & Networking
• 18:45 - Environments & Spark Jobs
• 19:10 - Data Factory Pipelines Deep Dive
• 19:35 - Database Mirroring - The Game Changer
• 19:50 - ETL Decision Framework
• 19:55 - Q&A and Discussion
• 20:00 - Networking

🎯 The Problem: Your ETL jobs take 6 hours. Business users want data "as fresh as possible." You're juggling Dataflows, Notebooks, Pipelines, and now Mirroring - but which one is actually right for which scenario? 💡 This Evening: We'll cut through the confusion with a clear decision framework. You'll see each tool in action and understand when to use Spark vs. Dataflows vs. Mirroring - with real-world examples. ⏱️ Save yourself: Weeks of painful refactoring. Choose the right ETL approach from day one. What you'll learn: ⚙️ Spark Environments - Managing libraries and configurations\, and WHY environment isolation matters for production 🚀 Spark Job Definitions - Automating Spark jobs at scale\, and WHY scheduled jobs beat manual notebook runs 🔄 Data Factory Pipelines - Enterprise orchestration with activities\, and WHY Fabric Pipelines are simpler than classic ADF 🪞 Database Mirroring - Near real-time replication from SQL Server\, Cosmos DB\, Snowflake\, and WHY mirroring beats traditional CDC approaches Decision Framework:

• Simple transformations → Dataflow Gen2
• Complex logic, ML → Notebook → Spark Job
• Orchestration, dependencies → Data Pipeline
• Real-time sync from source → Mirroring

Who should attend: Data Engineers building ETL pipelines, DBAs managing data integration, Architects designing data platforms Agenda:

• 18:30 - Welcome & Networking
• 18:45 - Environments & Spark Jobs
• 19:10 - Data Factory Pipelines Deep Dive
• 19:35 - Database Mirroring - The Game Changer
• 19:50 - ETL Decision Framework
• 19:55 - Q&A and Discussion
• 20:00 - Networking