Data Management For Business Intelligence

Credits: 
2
Hours: 
24
Area: 
Big Data Technology
Teachers: 
Ruggieri Salvatore
Academic Year: 
2023-2024
Description: 

The module presents the methodological aspects, technologies and systems for designing, populating and querying Data Warehouses for decision support. The emphasis is placed on the analysis of application problems using examples and case studies, with laboratory exercises.

Prerequisites: knowledge of basic SQL, Excel, Python programming.

Notions: 
  • Lesson1: Introduction to Datawarehousing
    • OLAP vs. OLTP
    • Design phases of a DW
    • Data model: logic model
    • Case Studies
  • Lesson 2: Analytical SQL
    • ROLLUP and CUBE
    • OVER clause
    • Windowing
    • SQL Server tutorials
  • Lesson 3: Extract Transform and Load (ETL)
    • RDBMS access standard
    • ETL operations: control flow and data flow
    • The SSIS System: SQL Server Integration Services
    • Tutorials in SSIS
  • Lesson 4: Online Analytical Processing (OLAP)
    • The multidimensional model
    • The SSAS system: SQL Server Analysis Services
    • Reporting: Microsoft Power BI
    • SSAS/Power BI tutorials
  • Lesson 5: Scalability and API
    • Scalability of DW systems
    • NoSQL Data Model
    • NO-SQL Big Data Platforms
    • Python API for SQL and NoSQL
Technics and tools: 

pyodbc

Competences: 

The student will acquire knowledge and skills on the main Business Intelligence technologies such as ETL (Extract, Transform and Load), Data Warehousing, Analytic SQL, OLAP (Online Analytical Processing). It will also have references to scalability issues and NoSQL architectures.

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