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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

Summary

In this chapter, we learned about two different things. First, we learned about how to estimate an end-to-end data solution cost. Second, we understood how BigQuery partitioned and clustered tables play a significant role in cost.

These two topics are usually needed by data engineers in different situations. Understanding how to calculate cost will help in the early stages of GCP implementation. This is usually a particularly important step for an organization to decide its future solution as a whole.

The second topic usually occurs when you’re designing BigQuery tables, and at a time when you need to evaluate the running BigQuery solution. Even though it’s obvious that using partitioned and clustered tables is beneficial, it’s not a surprise in a big organization, as many tables are not optimized and can be improved.

Lastly, we performed an experiment using three different tables. It proved that using partitioned and clustered tables can reduce...

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