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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

Building a Data Lake Using Dataproc

A data lake shares similarities with a data warehouse, yet its fundamental distinction lies in the nature of stored content. Unlike a data warehouse, a data lake is designed to manage extensive raw data, agnostic to its eventual value or purpose. This pivotal divergence reshapes approaches to data storage and retrieval within a data lake, setting it apart from the principles that we learned in Chapter 3, Building a Data Warehouse in BigQuery.

This chapter helps you understand how to build a data lake using Dataproc, which is a managed Hadoop cluster in Google Cloud Platform (GCP). But, more importantly, it helps you understand the key benefit of using a data lake in the cloud, which is allowing the use of ephemeral clusters.

Here is a high-level outline of this chapter:

  • Introduction to Dataproc
  • Exercise – Building a data lake on a Dataproc cluster
  • Exercise – Creating and running jobs on a Dataproc cluster
  • Understanding...
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