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Professional Cloud Architect Google Cloud Certification Guide

You're reading from   Professional Cloud Architect Google Cloud Certification Guide Build a solid foundation in Google Cloud Platform to achieve the most lucrative IT certification

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801812290
Length 664 pages
Edition 2nd Edition
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Authors (2):
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Konrad Cłapa Konrad Cłapa
Author Profile Icon Konrad Cłapa
Konrad Cłapa
Brian Gerrard Brian Gerrard
Author Profile Icon Brian Gerrard
Brian Gerrard
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Toc

Table of Contents (25) Chapters Close

Preface 1. Section 1: Introduction to GCP
2. Chapter 1: GCP Cloud Architect Professional FREE CHAPTER 3. Chapter 2: Getting Started with Google Cloud Platform 4. Chapter 3: Google Cloud Platform Core Services 5. Section 2: Manage, Design, and Plan a Cloud Solution Architecture
6. Chapter 4: Working with Google Compute Engine 7. Chapter 5: Exploring Google App Engine as a Compute Option 8. Chapter 6: Managing Kubernetes Clusters with Google Kubernetes Engine 9. Chapter 7: Deploying Cloud-Native Workloads with Cloud Run 10. Chapter 8: Managing Cloud-Native Workloads with Anthos 11. Chapter 9: Running Serverless Functions with Google Cloud Functions 12. Chapter 10: Networking Options in GCP 13. Chapter 11: Exploring Storage and Database Options in GCP – Part 1 14. Chapter 12: Exploring Storage and Database Options in GCP – Part 2 15. Chapter 13: Analyzing Big Data Options 16. Chapter 14: Putting Machine Learning to Work 17. Section 3: Secure, Manage and Monitor a Google Cloud Solution
18. Chapter 15: Security and Compliance 19. Chapter 16: Google Cloud Management Options 20. Chapter 17: Monitoring Your Infrastructure 21. Section 4: Exam Focus
22. Chapter 18: Case Studies 23. Chapter 19: Test Your Knowledge 24. Other Books You May Enjoy

Summary

In this chapter, we covered the main aspects of big data relating to the exam. We covered each service and showed that these can be used at different stages of our end-to-end solution. We took the time to see how we can configure Pub/Sub, Dataflow, and BigQuery from the GCP console and discussed Dataproc and Cloud IoT Core.

Exam Tip

The key takeaway from this chapter is to understand which services map to the ingest, process, and analysis stages of data.

Then, we looked at the processing stage of our solution. Cloud Dataflow will deploy Google Compute Engine instances to deploy and execute our Apache Beam pipeline, which will process data from Pub/Sub and pass it onto further stages for analysis or storage. We have shown how we can easily create a pipeline in the GCP console, which pulls information from Pub/Sub for analysis in BigQuery.

After, we covered BigQuery and understood that it is a data warehouse. It is designed to make data analysts more productive, crunching...

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