Relational Database Support for Data Warehouses

This module provides an in-depth understanding of DBMS extensions and offers practical examples of data warehouses. Students will gain insights into relational database schema patterns and learn about real-world data warehouse standards, including the Colorado Education Data Warehouse and data warehouse standards in healthcare.

Module 2 focuses on SQL subtotal operators, covering the GROUP BY clause, SQL CUBE, ROLLUP, and GROUPING SETS operators. Students will also explore variations of these operators and gain hands-on experience through additional problems and assignments.

Module 3 delves into SQL analytic functions, covering the processing model, basic and extended syntax, ranking functions, window comparisons, functions for ratio comparisons, and PostgreSQL query patterns for RATIO_TO_REPORT. Students will have the opportunity to apply their knowledge through assignments and concept quizzes.

This module provides a comprehensive understanding of materialized view processing and design, including the background on traditional views, materialized view definition and processing, query rewriting rules, and examples. Students will also engage in practical problem-solving through additional problems and assignments.

Module 5 covers storage architectures, scalable parallel processing approaches, big data issues, and data governance. Students will explore the nuances of managing physical design and governance in the context of data warehouses, gaining valuable insights into storage architectures and scalable parallel processing.

The final module focuses on SQL for data mining input, providing students with the necessary skills to utilize SQL for data mining. Through a series of video lectures and practical assignments, students will gain hands-on experience in using SQL for data mining input.