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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

Introduction to data governance

Data governance is the set of processes, policies, standards, and practices that organizations use to manage their data ecosystem.

The roles involved in data governance vary depending on the size of the organization. In some cases, when the organization is not that big, data engineers are responsible for data governance. In other cases, when the organization is very big and requires a deeper understanding of legal aspects, usually there is a dedicated team called the data governance team.

data governance encompasses a wide spectrum of practices and principles, much like the vast realm of big data. If we want to implement data governance, understanding the underlying motivations behind implementing it is crucial. Implementing data governance with the wrong motivation usually leads to a wasted effort.

The motivations can be defined into three primary pillars:

  • Usability
  • Security
  • Accountability

The preceding three primary...

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