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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 Dataproc

Dataproc is a Google-managed service for Hadoop environments. It manages the underlying virtual machines (VMs), operating systems, and Hadoop software installations. Using Dataproc, Hadoop developers can focus on developing jobs and submitting them to Dataproc.

From a data engineering perspective, understanding Dataproc is equal to understanding Hadoop and the data lake concept. If you are not familiar with Hadoop, let’s learn about it in the next section.

A brief history of the data lake and Hadoop ecosystem

The popularity of the data lake rose in the 2010s. Companies started to talk about this concept a lot more, compared to the data warehouse, which is similar but different in principle. The concept of storing data as files in a centralized system makes a lot of sense in the modern era, compared to the old days when companies stored and processed data typically for regular reporting. In the modern era, people use data for exploration from...

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