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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 how to use Data Studio using BigQuery as the data source. We learned how to connect the data, create charts in Explorer, and create reports for sharing the charts and information with other users. 

Through the exercises in this chapter, you have learned about not only how to create charts but also the point of view of your end users. In the exercises, you realized how important it is to create a proper data model in your datasets. Imagine if your tables didn't have proper naming conventions, weren't aggregated properly, or followed any other bad practices that can happen in a data warehouse. Since we already learned all the good data engineering practices and code from the previous chapters, it's now very easy for us to use our example tables to visualize things in Data Studio.

And lastly, as data engineers, we need to be the ones who understand and are aware of the cost implications in our data ecosystem. In this chapter...

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