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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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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

You are a data engineer at a book publishing company and your product manager has asked you to build a dashboard to show the total revenue and customer satisfaction index in a single dashboard. 

Your company doesn't have any data infrastructure yet, but you know that your company has these three applications that contain TBs of data:

  • The company website
  • A book sales application using MongoDB to store sales transactions, including transactions, book ID, and author ID
  • An author portal application using MySQL Database to store authors' personal information, including age

Do the following:

  1. List important follow-up questions for your manager.
  2. List your technical thinking process of how to do it at a high level. 
  3. Draw a data pipeline architecture.

There is no right or wrong answer to this practice. The important thing is that you can imagine how the data flows from upstream to downstream, how it should be processed at each step, and finally, how you want to serve the information to end users. 

You have been reading a chapter from
Data Engineering with Google Cloud Platform
Published in: Mar 2022
Publisher: Packt
ISBN-13: 9781800561328
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