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Course Description
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.The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.
The course glossary can be found on the right in the resources section.
To obtain CPE credits, you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.
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Start Course for FreeWhat you'll learn
- Assess when to apply ETL versus ELT processes, row-store versus column-store storage, OLAP versus OLTP systems, and on-premise versus cloud deployment for specific analytical requirements
- Differentiate Inmon and Kimball architectural methodologies, including their data flows and normalization strategies
- Distinguish between data warehouses, data lakes, and data marts with respect to structure, scope, and use cases
- Evaluate star and snowflake schema designs by selecting suitable fact tables, dimension tables, and slowly changing dimension techniques
- Identify the core components and lifecycle stages of a data warehouse
Prerequisites
Curriculum
Course outline
1
Data Warehouse Basics
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 XP
- Knowing the what and why50 XP
- Possible use cases for a data warehouse for Zynga50 XP
- What's the difference between data warehouses and data lakes?50 XP
- Data warehouses vs. data lakes100 XP
- Data warehouses vs. data marts50 XP
- Deciding between a data lake, warehouse, and mart50 XP
- Data warehouses support organizational analysis50 XP
- Data warehouse life cycle100 XP
- Support where needed50 XP
- Who does what?100 XP
2
Warehouse Architectures and Properties
Now, 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.
3
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.
4
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.
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Data Warehousing Concepts
Course
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