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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

As a summary of the first chapter, we’ve learned the fundamental knowledge we need as data engineers. Here are some key takeaways from this chapter. First, data doesn’t stay in one place. Data moves from one place to another, called the data life cycle. We also understand that data in a big organization is mostly in silos, and we can solve these data silos using the concepts of a data warehouse and data lake.

As someone who has started to look into data engineer roles, you may be a little bit lost. The role of data engineers may vary. The key takeaway is not to be confused about the broad expectations in the market. First, you should focus on the core and then expand as you get more experience from the core. In this chapter, we’ve learned what the core of a data engineer is. At the end of the chapter, we learned some of the key concepts. There are three key concepts as a data engineer that you need to be familiar with. These concepts are ETL, big data...

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