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Data Warehousing Concepts

Data warehouse architectures

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Questo esercizio fa parte del corso

Data Warehousing Concepts

IntermediárioNível de habilidade

4.8+

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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.

Exercise 1: What is a data warehouse?Exercise 2: Knowing the what and why Exercise 3: Possible use cases for a data warehouse for Zynga Exercise 4: What's the difference between data warehouses and data lakes?Exercise 5: Data warehouses vs. data lakes Exercise 6: Data warehouses vs. data marts Exercise 7: Deciding between a data lake, warehouse, and mart Exercise 8: Data warehouses support organizational analysis Exercise 9: Data warehouse life cycle Exercise 10: Support where needed Exercise 11: Who does what?

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.

Exercise 1: What are the different layers of a data warehouse?Exercise 2: Ordering data warehouse layers Exercise 3: Understanding ETL Exercise 4: Pick the correct layer Exercise 5: The presentation layer Exercise 6: Stepping into a consultant's shoes Exercise 7: Supporting analysts and data scientist users Exercise 8: Data warehouse architectures

Esercizio in corso

Exercise 9: Top-down vs bottom-up Exercise 10: Characteristics of top-down and bottom-up Exercise 11: Choosing a top-down approach Exercise 12: OLAP and OLTP systems Exercise 13: The OLAP data cube Exercise 14: OLAP vs. OLTP scenarios Exercise 15: Understanding OLTP

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.

Exercise 1: Data warehouse data modeling Exercise 2: Understanding facts and dimensional tables Exercise 3: One starry and snowy night Exercise 4: Fact or dimension?Exercise 5: Kimball's four step process Exercise 6: Ordering Kimball's steps Exercise 7: Deciding on the grain Exercise 8: Selecting reasonable facts Exercise 9: Slowly changing dimensions Exercise 10: Pop-quiz on slow changes Exercise 11: Difference between type I, II, and III Exercise 12: Row vs. column data store Exercise 13: Categorizing row and column store scenarios Exercise 14: Why is column store faster?Exercise 15: Which queries are faster?

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.

Exercise 1: ETL and ELT Exercise 2: ETL compared to ELT Exercise 3: Differences between ETL and ELT Exercise 4: Selecting ELT Exercise 5: Data cleaning Exercise 6: Cleaning operations Exercise 7: Finding truth in data transformations Exercise 8: Understanding data governance Exercise 9: On premise and cloud data warehouses Exercise 10: Knowing the differences between on-premise and cloud Exercise 11: Matching implementation to justification Exercise 12: Data warehouse design example Exercise 13: Connecting it all Exercise 14: Selecting bottom-up Exercise 15: Do you know it all?Exercise 16: Wrap-up