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Data Engineering with Google Cloud Platform

You're reading from   Data Engineering with Google Cloud Platform A practical guide to operationalizing scalable data analytics systems on GCP

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Product type Paperback
Published in Mar 2022
Publisher Packt
ISBN-13 9781800561328
Length 440 pages
Edition 1st 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 (17) Chapters Close

Preface 1. Section 1: Getting Started with Data Engineering with GCP
2. Chapter 1: Fundamentals of Data Engineering FREE CHAPTER 3. Chapter 2: Big Data Capabilities on GCP 4. Section 2: Building Solutions with GCP Components
5. Chapter 3: Building a Data Warehouse in BigQuery 6. Chapter 4: Building Orchestration 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 for Making Data-Driven Decisions with Data Studio 10. Chapter 8: Building Machine Learning Solutions on Google Cloud Platform 11. Section 3: Key Strategies for Architecting Top-Notch Data Pipelines
12. Chapter 9: User and Project Management in GCP 13. Chapter 10: Cost Strategy in GCP 14. Chapter 11: CI/CD on Google Cloud Platform for Data Engineers 15. Chapter 12: Boosting Your Confidence as a Data Engineer 16. Other Books You May Enjoy

Chapter 5: Building a Data Lake Using Dataproc

A data lake is a concept similar to a data warehouse, but the key difference is what you store in it. A data lake's role is to store as much raw data as possible without knowing first what the value or end goal of the data is. Given this key differentiation, how to store and access data in a data lake is different compared to what 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 the high-level outline of this chapter:

  • Introduction to Dataproc
  • Building a data lake on a Dataproc cluster
  • Creating and running jobs on a Dataproc cluster
  • Understanding the concept of the ephemeral cluster
  • Building an ephemeral...
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