Relational Database Support for Data Warehouses

SQL Subtotal Operators

Now that you have the informational context for relational database support of data warehouses, you’ll start using relational databases to write business intelligence queries! In module 2, you will learn an important extension of the SQL SELECT statement for subtotal operators. You’ll apply what you’ve learned in practice and graded problems using SQL (Oracle or PostgreSQL) for problems involving the CUBE, ROLLUP, and GROUPING SETS operators. Because the subtotal operators are part of the SQL standard, your learning will readily apply to other enterprise DBMSs. At the end of this module, you will have solid background to write queries using the SQL subtotal operators as a data warehouse analyst.

SQL Analytic Functions

After your experience using the SQL subtotal operators, you are ready to learn another important SQL extension for business intelligence applications. In module 3, you will learn about an extended processing model for SQL analytic functions that support common analysis in business intelligence applications. You’ll apply what you’ve learned in practice and graded problems using SQL (Oracle or PostgreSQL) for problems involving qualitative ranking of business units, window comparisons showing relationships of business units over time, and quantitative contributions showing performance thresholds and contributions of individual business units to a whole business. Because analytic functions are part of the SQL standard, your learning will apply to other enterprise DBMSs. At the end of this module, you will have solid background to write queries using the SQL analytic functions as a data warehouse analyst.

Materialized View Processing and Design

After acquiring query formulation skills for development of business intelligence applications, you are ready to learn about DBMS extensions for efficient query execution. Business intelligence queries can use lots of resources so materialized view processing and design has become an important extension of DBMSs. In module 4, you will learn about an SQL statement for creating materialized views, processing requirements for materialized views, and rules for rewriting queries using materialized views. To gain insight about the complexity of query rewriting, you will practice rewriting queries using materialized views. To provide closure about relational database support for data warehouses, you will learn about about Oracle tools for data integration, the Oracle Data Integrator, along with two SQL statements useful for specific data integration tasks. After this module, you will have a solid background to use materialized views to improve query performance and deploy the Extraction, Loading, and Transformation approach for data integration as a data warehouse administrator or analyst.

Physical Design and Governance

Module 5 continues the course with a return to conceptual material about physical design technologies and data governance practices. You will learn about storage architectures, scalable parallel processing, big data issues, and data governance. After this module, you will have background about conceptual issues important for data warehouse administrators.

SQL for Data Mining Input

Module 6 provides optional advanced material on query formulation for learners who seek expert level knowledge and skills. Advanced query formulation can help learners gain an edge in the workplace for expert status and high value to an organization. Module 6 covers original material for advanced query formulation skills that prepare learners to collaborate with data scientists on data mining tasks. The instructor developed material in Module 6 from his long experience using SQL for data mining projects. The SQL coding skills also transfer to other advanced query formulation tasks. Module 6 provides these specific knowledge areas and skills.• Examples and practice with data lakes and data warehouses as data mining projects can involve both types of data sources• SQL coding skills for two prominent data mining tasks, association rule mining and classification algorithms using training data with limited event history• New SQL elements for managing complex SQL coding, array results, independent subqueries with the IN comparison operator, a new analytic function, and conditional assignment of column values• New SQL coding skills for atypical join patterns• Unique pedagogy with statement patterns to write template SELECT statements as an initial step to a complete a SELECT statementDue to advanced material, Module 6 provides Lesson 9 as honors with problems, concept quiz, assignment, and self-evaluation. The concept quiz provides an assessment of learner understanding of the video lessons and associated notes. Learners should complete the concept quiz before starting practice problems and the graded assignment to ensure conceptual understanding of the material.