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Data Engineering with Google Cloud Platform

You're reading from   Data Engineering with Google Cloud Platform A practical guide to operationalizing scalable data analytics systems on GCP

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781800561328
Length 440 pages
Edition 1st Edition
Languages
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Author (1):
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Adi Wijaya Adi Wijaya
Author Profile Icon Adi Wijaya
Adi Wijaya
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: Getting Started with Data Engineering with GCP
2. Chapter 1: Fundamentals of Data Engineering FREE CHAPTER 3. Chapter 2: Big Data Capabilities on GCP 4. Section 2: Building Solutions with GCP Components
5. Chapter 3: Building a Data Warehouse in BigQuery 6. Chapter 4: Building Orchestration for Batch Data Loading Using Cloud Composer 7. Chapter 5: Building a Data Lake Using Dataproc 8. Chapter 6: Processing Streaming Data with Pub/Sub and Dataflow 9. Chapter 7: Visualizing Data for Making Data-Driven Decisions with Data Studio 10. Chapter 8: Building Machine Learning Solutions on Google Cloud Platform 11. Section 3: Key Strategies for Architecting Top-Notch Data Pipelines
12. Chapter 9: User and Project Management in GCP 13. Chapter 10: Cost Strategy in GCP 14. Chapter 11: CI/CD on Google Cloud Platform for Data Engineers 15. Chapter 12: Boosting Your Confidence as a Data Engineer 16. Other Books You May Enjoy

Exercise – Scenario 3

As a final activity in this chapter, you can do a self-assessment exercise to solve an additional business question from business users. Our operational user from scenario 2 wants to ask this additional question:

Show me the top three regions that have the most female riders as of the most recent date (2018-01-02).

Because the gender of members is not yet included in our fact and dimension table, you need to create a different fact and dimension table for this. 

Remember that the data model is subjective, especially in the Kimball method. There is no right or wrong answer to the question. As we've discussed in this chapter, everyone can have different data models to represent the real world. 

Try to solve it yourself and compare it to the solution in the Git code example:

  • Chapter-3\code\bigquery_self_excercise_create_dim_table_regions.py
  • Chapter-3\code\bigquery_self_excercise_create_fact_table_daily_by_gender_region...
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