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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

Summary

In this chapter, we've gone through a lot of practice in terms of how to use BigQuery to build a data warehouse. In general, we've covered the three main aspects of how to use the tools, how to load the data to BigQuery, and the data modeling aspect of a data warehouse.

By following all the steps in this chapter, you will have a better understanding of the data life cycle and you will understand that data moves from place to place. We also practiced the ELT process in this chapter, extracting data from the MySQL database, loading it to BigQuery, and doing some transformations to answer business questions. And on top of that, we did it all on a fully managed service in the cloud, spending zero time worrying about any infrastructure aspects.

By way of a footnote for this chapter, I want to remind you that, even though we have covered the common practice of using BigQuery, we haven't covered all of its features. There are a lot of other features in BigQuery...

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