Relational Database Support for Data Warehouses

Go to Course: https://www.coursera.org/learn/dwrelational

Introduction

### Course Review: Relational Database Support for Data Warehouses **Course Overview** The *Relational Database Support for Data Warehouses* is the third module in the *Data Warehousing for Business Intelligence* specialization on Coursera. This course is crafted for individuals seeking to enhance their SQL skills related to business intelligence queries and relational database management systems (RDBMS). Its comprehensive curriculum guides learners through essential SQL functionalities while focusing on the critical structures used in the realm of data warehouses. **Syllabus Breakdown** The course is highly structured, comprising six modules, each meticulously designed to build upon the last, ensuring a thorough understanding and practical application of concepts. 1. **DBMS Extensions and Example Data Warehouses**: This introductory module establishes a strong foundation, discussing DBMS extensions and schema patterns crucial for business intelligence. Practical examples from education and healthcare aim to illustrate the applicability of these concepts. A key recommendation is to install Oracle Cloud or PostgreSQL—this hands-on approach ensures you're ready for subsequent assignments. 2. **SQL Subtotal Operators**: Diving straight into the application of SQL, learners engage with subtotal operators such as CUBE, ROLLUP, and GROUPING SETS. This module empowers students to write business intelligence queries effectively, highlighting the significance of these SQL extensions in a practical context. 3. **SQL Analytic Functions**: Building on the previous module, students learn about analytic functions which play a vital role in qualitative analysis and relational comparisons in business environments. Through practice problems, participants will gain confidence in crafting analytical queries that add value to data reporting. 4. **Materialized View Processing and Design**: The focus shifts to the efficiency of query execution with materialized views—a critical area in optimizing performance. The course delves into the intricacies of creating and managing these views, equipping learners with the necessary skills to deploy them effectively. 5. **Physical Design and Governance**: This module includes important discussions on storage architectures, processing technologies, and governance practices related to data warehousing. This conceptual grounding is vital for those aiming toward data warehouse administration roles. 6. **SQL for Data Mining Input**: The final module is an optional advanced section that covers sophisticated query formulation. This aspect of the course is particularly valuable for those looking to collaborate with data scientists, providing them with enhanced SQL coding skills and knowledge relevant to data mining projects. **Course Recommendations** The *Relational Database Support for Data Warehouses* course is highly recommendable for business analysts, data analysts, and anyone keen on deepening their understanding of data warehousing and relational databases. - **Strengths**: - The practical applications and hands-on assignments provide a robust learning experience. - The course is structured logically, enabling students to progressively build their knowledge. - Expert insights and advanced material in the final module offer unique value for those seeking to elevate their skills. - **Considerations**: - While the course is accessible, a basic understanding of SQL and relational databases will be beneficial, especially for those less familiar with data warehousing concepts. - The recommendation to install Oracle Cloud or PostgreSQL may require additional time, so planning accordingly is advisable. **Conclusion** In conclusion, the *Relational Database Support for Data Warehouses* course on Coursera is an excellent investment for professionals looking to enhance their SQL skills in the context of data warehousing and business intelligence. Whether you're targeting a career in data analysis or looking to elevate your current skill set, this course provides the essential knowledge and practical experience needed to excel in the field.

Syllabus

DBMS Extensions and Example Data Warehouses

Module 1 introduces the course and covers concepts that provide a context for the remainder of this course. In the first two lessons, you’ll understand the objectives for the course and know what topics and assignments to expect. In the remaining lessons, you will learn about DBMS extensions, a review of schema patterns, data warehouses used in practice problems and assignments, and examples of data warehouses in education and health care. This informational module will ensure that you have the background for success in later modules that emphasize details and hands-on skills. You should also read about the software requirements in the lesson at the end of module 1. I recommend that you install Oracle Cloud or PostgreSQL this week before assignments begin in week 2. If you have taken other courses in the specialization, you may already have installed Oracle Cloud or PostgreSQL.

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.

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.

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.

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.

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.