Data Warehouse 1 Course Syllabus

This course introduces the fundamentals of data warehousing, covering key concepts, architectures, and practical implementations using open-source tools. The course is designed for master’s students with diverse backgrounds and prepares them for an advanced Data Warehouse course.

Course Structure

  • 7 weeks of classes (3 hours each)
  • 1 final exam session (3 hours)
  • Combination of lectures, in-class exercises, quizzes, and computer labs (starting from Week 3)

Weekly Schedule

Week 1: Introduction to DBMS and Data Warehouses

  • Challenges for DBs
  • Brief history of DBs
  • Founding principles of DBMS
  • Data Warehouse motivations and definitions
  • OLTP vs OLAP
  • DW industrial landscape
  • In-class exercises: Identifying OLTP vs OLAP scenarios

Materials:

Week 2: Data Warehouse Concepts and Relational Schemas

  • Multi-dimensional model
  • DW architecture and components
  • Data model: facts, measures, dimensions, cube
  • Multi-dimensional queries: OLAP operations
  • Relational schemas for DWHs
  • Dimensional modeling
  • Fact tables and dimension hierarchies

Materials:

Week 3: SQL 101

  • Quiz on Week 1 and Week 2 material (first 30 minutes)
  • SQL review with focus on DW-specific queries
  • Advanced SQL techniques for data warehousing

Materials:

Week 4: SQL for Data Warehouses

  • Avanced SQL course
  • Lab: Advanced SQL

Materials:

Week 5: SQL for Data Warehouses (continued)

Week 6: TBA

Week 7: Data Manipulation with Python

  • Quiz on Week 3 to Week 6 material (first 30 minutes)
  • Graded Lab

Week 8: Final Exam

  • Comprehensive exam covering all course topics (3 hours)

Assessment

  • In-class exercises and participation (20%)
  • Graded quizzes (2 or 3 quizzes, total 30%)
  • Lab reports (20%)
  • Final exam (30%)

Required Tools (Open-source and Free)

  • PostgreSQL for relational database examples
  • Python with NumPy and Pandas (a recent version)
  • MongoDB for NoSQL examples
  • Additional open-source tools to be announced for specific labs
Pierre-Henri Paris
Pierre-Henri Paris
Associate Professor in Artificial Intelligence

My research interests include Knowlegde Graphs, Information Extraction, and NLP.