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Data Engineering with Google Cloud Platform

You're reading from   Data Engineering with Google Cloud Platform A guide to leveling up as a data engineer by building a scalable data platform with Google Cloud

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Product type Paperback
Published in Apr 2024
Publisher Packt
ISBN-13 9781835080115
Length 476 pages
Edition 2nd 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 (19) Chapters Close

Preface 1. Part 1: Getting Started with Data Engineering with GCP FREE CHAPTER
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

Summary

In this chapter, we’ve practiced using 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.

After 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 a 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 practices of using BigQuery, we haven’t covered all of its features. There are a lot of other features in BigQuery that are worth checking out...

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