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.
Requirements
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.
Description
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!
Overview
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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