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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

A quick overview of GCP services for data engineering

As you can see in the GCP Console navigation bar, there are a lot of services in GCP. The services are not only limited to data and analytics. They also cover other areas such as application development, machine learning, networks, source repositories, and many more. As a data engineer working on GCP, you will face situations when you need to decide which services you need to use for your organization.

You might be wondering, who in an organization should decide on the services to use? Is it the CTO, IT manager, solution architect, or data engineers? The answer depends on the experience of using GCP of each of them. But most of the time, data engineers need to be involved in the decision.

So how should we decide? In my experience, there are three important decision factors:

  • Choose services that are serverless.
  • Understand the mapping between the service and the data engineering areas.
  • If there is more than...
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