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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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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

The past, present, and future of data engineering

The data engineering practice has been there since the early internet era in the 1990s. Going back to Chapter 1, Fundamentals of Data Engineering, in the Start with knowing the roles of a data engineer section, in the past, data engineers were mostly ETL developers using specific tools. Most of these tools were proprietary tools and located on-premises. The term data engineer itself wasn’t commonplace; the more common terms used to be data modelers, database admin, and ETL developer (ETL references the proprietary ETL tool’s name). Each of the ETL tools had the necessary expertise and best practices surrounding them.

Now, in the present, data engineering has evolved into a more mature and singular role. This means that the practice is receiving a lot more common principles, concepts, and best practices. This is due to two reasons – the rapid improvement in the technologies supporting the practice and the fact...

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