Data Warehouse Fundamentals for Beginners
Data Warehouse Fundamentals for Beginners, available at $109.99, has an average rating of 4.47, with 68 lectures, 4 quizzes, based on 26024 reviews, and has 100543 subscribers.
You will learn about 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. This course is ideal for individuals who are 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. or 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. It is particularly useful 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. or 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.
Enroll now: Data Warehouse Fundamentals for Beginners
Summary
Title: Data Warehouse Fundamentals for Beginners
Price: $109.99
Average Rating: 4.47
Number of Lectures: 68
Number of Quizzes: 4
Number of Published Lectures: 68
Number of Published Quizzes: 4
Number of Curriculum Items: 77
Number of Published Curriculum Objects: 77
Original Price: $34.99
Quality Status: approved
Status: Live
What You Will 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.
Who Should Attend
- 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.
Target Audiences
- 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.
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:
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Foundational data warehousing concepts and fundamentals
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The symbiotic relationship between data warehousing and business intelligence
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How data warehousing co-exists with data lakes and data virtualization
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Your many architectural alternatives, from highly centralized approaches to numerous multi-component alternatives
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The fundamentals of dimensional analysis and modeling
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The key relational database capabilities that you will put to work to build your dimensional data models
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Different alternatives for handling changing data history within your environment, and how to decide which approaches to apply in various situations
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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!
Course Curriculum
Chapter 1: Welcome
Lecture 1: Welcome
Lecture 2: About This Course
Lecture 3: Reflection: The Value of Data Warehousing
Chapter 2: Data Warehousing Concepts
Lecture 1: Introduction to Data Warehousing Concepts
Lecture 2: What is a Data Warehouse?
Lecture 3: Reasons for You to Build a Data Warehouse
Lecture 4: Compare a Data Warehouse to a Data lake
Lecture 5: Compare a Data Warehouse to Data Virtualization
Lecture 6: Look at a Simple End-to-End Data Warehousing Environment
Lecture 7: Summarize Data Warehousing Concepts
Chapter 3: Data Warehousing Architecture
Lecture 1: Introduction to Data Warehousing Architecture
Lecture 2: Build a Centralized Data Warehouse
Lecture 3: Compare a Data Warehouse to a Data Mart
Lecture 4: Decide Which Component-Based Architecture is Your Best Fit
Lecture 5: Include Cubes in Your Data Warehousing Environment
Lecture 6: Include Operational Data Stores in Your Data Warehousing Environment
Lecture 7: Explore the Role of the Staging Layer Inside a Data Warehouse
Lecture 8: Compare the Two Types of Staging Layers
Lecture 9: Summarize Data Warehousing Architecture
Chapter 4: Bring Data Into Your Data Warehouse
Lecture 1: Introduction to ETL and Data Movement for Data Warehousing
Lecture 2: Compare ETL to ELT
Lecture 3: Design the Initial Load ETL
Lecture 4: Compare Different Models for Incremental ETL
Lecture 5: Explore the Role of Data Transformation
Lecture 6: More Common Transformations Within ETL
Lecture 7: Implement Mix-and-Match Incremental ETL
Lecture 8: Summarize ETL Concepts and Models
Chapter 5: Data Warehousing Design: Building Blocks
Lecture 1: Data Warehousing Structure Fundamentals
Lecture 2: Deciding What Your Data Warehouse Will Be Used For
Lecture 3: The Basic Principles of Dimensionality
Lecture 4: Compare Facts, Fact Tables, Dimensions, and Dimension Tables
Lecture 5: Compare Different Forms of Additivity in Facts
Lecture 6: Compare a Star Schema to a Snowflake Schema
Lecture 7: Database Keys for Data Warehousing
Lecture 8: Summarize Data Warehousing Structure
Chapter 6: Design Facts, Fact Tables, Dimensions, and Dimension Tables
Lecture 1: Introduction to Dimensional Modeling
Lecture 2: Design Dimension Tables for Star Schemas and Snowflake Schemas
Lecture 3: The Four Main Types of Data Warehousing Fact Tables
Lecture 4: The Role of Transaction Fact Tables
Lecture 5: The Rules Governing Facts and Transaction Fact Tables
Lecture 6: Primary and Foreign Keys for Fact Tables
Lecture 7: The Role of Periodic Snapshot Fact Tables
Lecture 8: Periodic Snapshots and Semi-Additive Facts
Lecture 9: The Role of Accumulating Snapshot Fact Tables
Lecture 10: Accumulating Snapshot Fact Table Example
Lecture 11: Why a Factless Fact Table isn't a Contradiction in Terms
Lecture 12: Compare the Structure of Fact Tables in Star Schemas vs. Snowflake Schemas
Lecture 13: SQL for Dimension and Fact Tables
Lecture 14: Summarize Fact and Dimension Tables
Chapter 7: Managing Data Warehouse History Through Slowly Changing Dimensions
Lecture 1: Introduction to Slowly Changing Dimensions
Lecture 2: Slowly Changing Dimensions (SCDs) and Data Warehouse History
Lecture 3: Design a Type 1 SCD
Lecture 4: Design a Type 2 SCD
Lecture 5: Maintain Correct Data Order with Type 2 SCDs
Lecture 6: Design a Type 3 SCD
Lecture 7: Summarize SCD concepts and implementations
Chapter 8: Designing Your ETL
Lecture 1: Introduction to ETL Design
Lecture 2: Build your ETL Design from your ETL Architecture
Lecture 3: Dimension Table ETL
Lecture 4: Process SCD Type 1 Changes to a Dimension Table
Lecture 5: Process SCD Type 2 Changes to a Dimension Table
Lecture 6: Design ETL for Fact Tables
Lecture 7: Summarize ETL Design
Chapter 9: Selecting Your Data Warehouse Environment
Lecture 1: Introduction to Data Warehousing Environments
Lecture 2: Decide Between Cloud and On-Premises Settings for Your Data Warehouse
Lecture 3: Architecture and Design Implications for Your Selected Platform
Chapter 10: Conclusion
Lecture 1: Thank you for taking the course!
Lecture 2: Additional resources for further study
Instructors
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Alan Simon
Thought leader in business intelligence and enterprise data
Rating Distribution
- 1 stars: 231 votes
- 2 stars: 390 votes
- 3 stars: 2798 votes
- 4 stars: 10199 votes
- 5 stars: 12406 votes
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