Data Warehouse Fundamentals for Beginners

Best Practices and Concepts for Architecture and Dimensional Design
Data Warehouse Fundamentals for Beginners
File Size :
1.93 GB
Total length :
5h 9m



Alan Simon


Last update

Last updated 3/2020



Data Warehouse Fundamentals for Beginners

What you’ll learn

Master the techniques needed to build a data warehouse for your organization.
Determine your options for the architecture of your data warehousing environment.
Apply the key design principles of dimensional data modeling.
Combine various models and approaches to unify and load data within your data warehouse.

Data Warehouse Fundamentals for Beginners


A basic understanding of (but not necessarily programming experience with) relational databases and SQL fundamentals, specifically how you use the SQL CREATE TABLE statement to create data structures in a relational database.


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 fundamentalsThe symbiotic relationship between data warehousing and business intelligenceHow data warehousing co-exists with data lakes and data virtualizationYour many architectural alternatives, from highly centralized approaches to numerous multi-component alternativesThe fundamentals of dimensional analysis and modelingThe key relational database capabilities that you will put to work to build your dimensional data modelsDifferent alternatives for handling changing data history within your environment, and how to decide which approaches to apply in various situationsHow to organize and design your Extraction, Transformation, and Loading (ETL) capabilities to keep your data warehouse up to dateData 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!


Section 1: Welcome

Lecture 1 Welcome

Lecture 2 About This Course

Lecture 3 Reflection: The Value of Data Warehousing

Section 2: Data Warehousing Concepts

Lecture 4 Introduction to Data Warehousing Concepts

Lecture 5 What is a Data Warehouse?

Lecture 6 Reasons for You to Build a Data Warehouse

Lecture 7 Compare a Data Warehouse to a Data lake

Lecture 8 Compare a Data Warehouse to Data Virtualization

Lecture 9 Look at a Simple End-to-End Data Warehousing Environment

Lecture 10 Summarize Data Warehousing Concepts

Section 3: Data Warehousing Architecture

Lecture 11 Introduction to Data Warehousing Architecture

Lecture 12 Build a Centralized Data Warehouse

Lecture 13 Compare a Data Warehouse to a Data Mart

Lecture 14 Decide Which Component-Based Architecture is Your Best Fit

Lecture 15 Include Cubes in Your Data Warehousing Environment

Lecture 16 Include Operational Data Stores in Your Data Warehousing Environment

Lecture 17 Explore the Role of the Staging Layer Inside a Data Warehouse

Lecture 18 Compare the Two Types of Staging Layers

Lecture 19 Summarize Data Warehousing Architecture

Section 4: Bring Data Into Your Data Warehouse

Lecture 20 Introduction to ETL and Data Movement for Data Warehousing

Lecture 21 Compare ETL to ELT

Lecture 22 Design the Initial Load ETL

Lecture 23 Compare Different Models for Incremental ETL

Lecture 24 Explore the Role of Data Transformation

Lecture 25 More Common Transformations Within ETL

Lecture 26 Implement Mix-and-Match Incremental ETL

Lecture 27 Summarize ETL Concepts and Models

Section 5: Data Warehousing Design: Building Blocks

Lecture 28 Data Warehousing Structure Fundamentals

Lecture 29 Deciding What Your Data Warehouse Will Be Used For

Lecture 30 The Basic Principles of Dimensionality

Lecture 31 Compare Facts, Fact Tables, Dimensions, and Dimension Tables

Lecture 32 Compare Different Forms of Additivity in Facts

Lecture 33 Compare a Star Schema to a Snowflake Schema

Lecture 34 Database Keys for Data Warehousing

Lecture 35 Summarize Data Warehousing Structure

Section 6: Design Facts, Fact Tables, Dimensions, and Dimension Tables

Lecture 36 Introduction to Dimensional Modeling

Lecture 37 Design Dimension Tables for Star Schemas and Snowflake Schemas

Lecture 38 The Four Main Types of Data Warehousing Fact Tables

Lecture 39 The Role of Transaction Fact Tables

Lecture 40 The Rules Governing Facts and Transaction Fact Tables

Lecture 41 Primary and Foreign Keys for Fact Tables

Lecture 42 The Role of Periodic Snapshot Fact Tables

Lecture 43 Periodic Snapshots and Semi-Additive Facts

Lecture 44 The Role of Accumulating Snapshot Fact Tables

Lecture 45 Accumulating Snapshot Fact Table Example

Lecture 46 Why a Factless Fact Table isn’t a Contradiction in Terms

Lecture 47 Compare the Structure of Fact Tables in Star Schemas vs. Snowflake Schemas

Lecture 48 SQL for Dimension and Fact Tables

Lecture 49 Summarize Fact and Dimension Tables

Section 7: Managing Data Warehouse History Through Slowly Changing Dimensions

Lecture 50 Introduction to Slowly Changing Dimensions

Lecture 51 Slowly Changing Dimensions (SCDs) and Data Warehouse History

Lecture 52 Design a Type 1 SCD

Lecture 53 Design a Type 2 SCD

Lecture 54 Maintain Correct Data Order with Type 2 SCDs

Lecture 55 Design a Type 3 SCD

Lecture 56 Summarize SCD concepts and implementations

Section 8: Designing Your ETL

Lecture 57 Introduction to ETL Design

Lecture 58 Build your ETL Design from your ETL Architecture

Lecture 59 Dimension Table ETL

Lecture 60 Process SCD Type 1 Changes to a Dimension Table

Lecture 61 Process SCD Type 2 Changes to a Dimension Table

Lecture 62 Design ETL for Fact Tables

Lecture 63 Summarize ETL Design

Section 9: Selecting Your Data Warehouse Environment

Lecture 64 Introduction to Data Warehousing Environments

Lecture 65 Decide Between Cloud and On-Premises Settings for Your Data Warehouse

Lecture 66 Architecture and Design Implications for Your Selected Platform

Section 10: Conclusion

Lecture 67 Thank you for taking the course!

Lecture 68 Additional resources for further study

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.

Course Information:

Udemy | English | 5h 9m | 1.93 GB
Created by: Alan Simon

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