This introductory and conceptual course will help you understand the fundamentals of data warehousing. You’ll gain a strong understanding of data warehousing basics through industry examples and real-world datasets. Some have forecasted that the global data warehousing market is expected to reach over $50 billion in 2028. This industry has continued to evolve over the years and has been a critical component of the data revolution for many organizations. There has never been a better time to learn about data warehousing.
Data Warehouse BasicsFree
Prepare for your data warehouse learning journey by grounding yourself in some foundational concepts. To begin this course, you’ll learn what a data warehouse is and how it compares and contrasts to similar-sounding technologies, data marts and data lakes. You’ll also learn how different personas help support the various stages of a data warehouse project.What is a data warehouse?50 xpKnowing the what and why50 xpPossible use cases for a data warehouse for Zynga50 xpWhat's the difference between data warehouses and data lakes?50 xpData warehouses vs. data lakes100 xpData warehouses vs. data marts50 xpDeciding between a data lake, warehouse, and mart50 xpData warehouses support organizational analysis50 xpData warehouse life cycle100 xpSupport where needed50 xpWho does what?100 xp
Warehouse Architectures and Properties
In chapter two, you’ll gain a better understanding of data warehouse architecture by learning the typical layers of a data warehouse and how the presentation layer supports analysts. Additionally, you’ll learn about Bill Inmon and his top-down approach and how it compares to Ralph Kimball and his bottom-up approach. Finally, you’ll understand the difference between OLAP and OLTP systems.What are the different layers of a data warehouse?50 xpOrdering data warehouse layers100 xpUnderstanding ETL50 xpPick the correct layer100 xpThe presentation layer50 xpStepping into a consultant's shoes50 xpSupporting analysts and data scientist users50 xpData warehouse architectures50 xpTop-down vs bottom-up50 xpCharacteristics of top-down and bottom-up100 xpChoosing a top-down approach50 xpOLAP and OLTP systems50 xpThe OLAP data cube50 xpOLAP vs. OLTP scenarios100 xpUnderstanding OLTP50 xp
Data Warehouse Data Modeling
Here, you’ll learn how to organize the data in your data warehouse with an excellent data model. First, you’ll cover the basics of data modeling by learning what a fact and a dimension table are and how you use them in the star and snowflake schemes. Then, you’ll review how to create a data model using Kimball's four-step process and how to deal with slowly changing dimensions.Data warehouse data modeling50 xpUnderstanding facts and dimensional tables50 xpOne starry and snowy night50 xpFact or dimension?50 xpKimball's four step process50 xpOrdering Kimball's steps100 xpDeciding on the grain50 xpSelecting reasonable facts50 xpSlowly changing dimensions50 xpPop-quiz on slow changes50 xpDifference between type I, II, and III50 xpRow vs. column data store50 xpCategorizing row and column store scenarios100 xpWhy is column store faster?50 xpWhich queries are faster?50 xp
Implementation and Data Prep
You’ll wrap up the course by learning the pros and cons of ETL and ELT processes and on-premise versus an in-cloud implementation. You’ll conclude by walking through an example, making key decisions on warehouse design and implementation.ETL and ELT50 xpETL compared to ELT100 xpDifferences between ETL and ELT50 xpSelecting ELT50 xpData cleaning50 xpCleaning operations50 xpFinding truth in data transformations50 xpUnderstanding data governance50 xpOn premise and cloud data warehouses50 xpKnowing the differences between on-premise and cloud100 xpMatching implementation to justification50 xpData warehouse design example50 xpConnecting it all50 xpSelecting bottom-up50 xpDo you know it all?100 xpWrap-up50 xp
PrerequisitesIntroduction to SQL
Aaren StubberfieldSee More
Senior Data Scientist @ Microsoft
I am a Senior Data Scientist with expertise in Machine Learning, AI, and data governance. Currently, I work for Microsoft's Digital Advertising, which has revenues of more than $10 billion in the fiscal year 2023. However, my experience is not limited to just the advertising industry. I have worked in the Supply Chain and Data Governance industries. With my vast experience, I have led numerous teams of data scientists and have been instrumental in the successful completion of many projects. My technical skills include the use of AI, like LLMs, Python, and other various tools necessary for the execution of data science projects. My passion lies in using data to gain insights and making data-driven decisions. I constantly strive to improve my skills and knowledge and am always open to learning new techniques and tools.