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Data Engineering with Google Cloud Platform - Second Edition

You're reading from  Data Engineering with Google Cloud Platform - Second Edition

Product type Book
Published in Apr 2024
Publisher Packt
ISBN-13 9781835080115
Pages 476 pages
Edition 2nd Edition
Languages
Author (1):
Adi Wijaya Adi Wijaya
Profile icon Adi Wijaya

Table of Contents (19) Chapters

Preface 1. Part 1: Getting Started with Data Engineering with GCP
2. Chapter 1: Fundamentals of Data Engineering 3. Chapter 2: Big Data Capabilities on GCP 4. Part 2: Build Solutions with GCP Components
5. Chapter 3: Building a Data Warehouse in BigQuery 6. Chapter 4: Building Workflows 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 to Make Data-Driven Decisions with Looker Studio 10. Chapter 8: Building Machine Learning Solutions on GCP 11. Part 3: Key Strategies for Architecting Top-Notch Solutions
12. Chapter 9: User and Project Management in GCP 13. Chapter 10: Data Governance in GCP 14. Chapter 11: Cost Strategy in GCP 15. Chapter 12: CI/CD on GCP for Data Engineers 16. Chapter 13: Boosting Your Confidence as a Data Engineer 17. Index 18. 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 IDs, and author IDs
  • An author portal application using a MySQL Database to store authors’ personal information, including age

Do the following:

  1. List down important follow-up questions for your manager
  2. List down 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...

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