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

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

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 the three different tables. It proved that using the...

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