Module 1: Introduction to Business Intelligence (2 hours)

• BI foundations and value chain: data to decision
• User profiles (“user hats”): decision-makers, data engineers, analysts
• Organizational and technological context
• Use cases and cross-sector applications

Module 2: Data Warehousing (15 hours)

• Architectures (2h): Data warehouses, data marts, centralized vs distributed models
• Multidimensional Modeling (3h, incl. 1 lab): Star and snowflake schemas, fact and dimension tables
• Data Integration & ETL (3h, incl. 1 lab): ETL pipeline, tools and practical design scenarios [PDF]
• Physical Implementation (2h): Storage optimisation, indexing, partitioning [Part-1]
• OLAP & Querying (3h, incl. 2 labs): SQL challenge, OLAP cube exploration (see Hands On) [PDF]
• Project Design Lab (2h, incl. 1 TD): Requirements gathering, governance, data quality [PDF]

Module 3: Data Lakes & Lakehouses (4 hours) [PDF]

• Architectures (1h): Data lakes vs lakehouses, hybrid architectures
• Metadata Management (1h, incl. 1 lab): Data catalogs, schema-on-read, lineage tracking
• ELT & Data Processing (1h): Metadata extraction, pre-processing strategies
• Maintenance & Analytics (1h, incl. 1 lab): Dashboards, user access policies

Module 4: BI in the Cloud (4 hours) [PDF]

• Deployment Models (1h): Edge, fog, and cloud strategies
• Strategic Deployment Decisions (1h, incl. 1 lab): Economic vs human cost trade-offs
• Technological Independence & Sustainability (2h): Vendor lock-in, open standards, green IT

Module 5: Final Project – BI Demo Fest (13 hours)

• BI Project Management (4h): Agile frameworks, team roles, planning tools
• BI Lifecycle (5h): From data sourcing to dashboarding
• Final Presentation & Demo (4h): Project pitch, visual storytelling, peer review