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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 how to use Looker Studio using BigQuery as the data source. We learned how to connect the data and create charts and reports for sharing it with other users.

Throughout the exercises in this chapter, we learned not only about how to create charts but also the point of view of our end users. In the exercises, we realized how important it is to create a proper data model in our datasets. Imagine if our 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 Looker 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, we learned about BigQuery INFORMATION_SCHEMA...

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