Data Warehouse Fundamentals for Beginners

If you are a current or aspiring IT professional in search of sound, practical techniques to plan, design, and build a data warehouse or data mart, this is the course for you.
During the course, you’ll put what you learn to work and define sample data warehousing architectures and dimensional data structures to help emphasize the best practices and techniques covered in this course. Each section has either
scenario based quiz questions
or
hands on assignments
that emphasizes key learning objectives for that section’s material. This way, you can be confident as you move through the course that you’re picking up the key points about data warehousing.
To build this course, I drew from more than 30 years of my own data warehousing work on more than 40 client projects and engagements. I’ve been a thought leader in the discipline of data warehousing since the early 1990s when modern data warehousing came onto the scene. I’ve literally seen it all...and written about the discipline of data warehousing in books such as the original Data Warehousing For Dummies ® , along with articles, white papers, and as a monthly data warehousing columnist. I’ve led global consulting practices delivering data warehousing (and its related discipline, business intelligence) to some of the most recognizable brand name customers, along with smaller-sized organizations and governmental agencies. My own consulting firm, Thinking Helmet, Inc., specializes in data warehousing, business intelligence, and related disciplines. I’ve rolled up my sleeves and personally tackled every aspect of what you’ll learn in this course. I’ve even learned a few painful lessons, and have built a healthy share of “lessons learned” into the course material.
In this course, I take you from the fundamentals and concepts of data warehousing all the way through best practices for the architecture, dimensional design, and data interchange that you’ll need to implement data warehousing in your organization. You’ll find many examples that clearly demonstrate the key concepts and techniques covered throughout the course. By the end of the course, you’ll be all set to not only put these principles to work, but also to make the key architecture and design decisions required by the “art” of data warehousing that transcend the nuts-and-bolts techniques and design patterns.
Specifically, this course will cover:
Foundational data warehousing concepts and fundamentals
The symbiotic relationship between data warehousing and business intelligence
How data warehousing co-exists with data lakes and data virtualization
Your many architectural alternatives, from highly centralized approaches to numerous multi-component alternatives
The fundamentals of dimensional analysis and modeling
The key relational database capabilities that you will put to work to build your dimensional data models
Different alternatives for handling changing data history within your environment, and how to decide which approaches to apply in various situations
How to organize and design your Extraction, Transformation, and Loading (ETL) capabilities to keep your data warehouse up to date
Data warehousing is both an art and a science. While we have developed a large body of best practices over the years, we still have to make this-or-that types of decisions from the earliest stages of a data warehousing project all the way through architecture, design, and implementation. That’s what I’ve instilled into this course: the fusion of data warehousing art and science that you can bring to your organization and your own work. So come join me on this journey through the world of data warehousing!
Who this course is for:
A business analyst, data engineer, or database designer, currently with little or no exposure to or experience with data warehousing, who desires to build a personal toolbox of data warehousing best practices and techniques.
After completing this course, you will be ready to begin working on real-world data warehousing projects, either with expanded responsibilities as part of an existing role or to find a new position involving data warehousing. Example positions include data warehousing architect, dimensional data modeler, ETL architect and designer, and data warehousing business analyst.