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Professional Cloud Architect –  Google Cloud Certification Guide

You're reading from   Professional Cloud Architect – Google Cloud Certification Guide A handy guide to designing, developing, and managing enterprise-grade GCP cloud solutions

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781838555276
Length 520 pages
Edition 1st Edition
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Authors (2):
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Brian Gerrard Brian Gerrard
Author Profile Icon Brian Gerrard
Brian Gerrard
Konrad Cłapa Konrad Cłapa
Author Profile Icon Konrad Cłapa
Konrad Cłapa
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Toc

Table of Contents (26) Chapters Close

Preface 1. Section 1: Introduction to GCP FREE CHAPTER
2. GCP Cloud Architect Professional 3. Getting Started with Google Cloud Platform 4. Google Cloud Platform Core Services 5. Section 2: Managing, Designing, and Planning a Cloud Solution Architecture
6. Working with Google Compute Engine 7. Managing Kubernetes Clusters with Google Kubernetes Engine 8. Exploring Google App Engine as a Compute Option 9. Running Serverless Functions with Google Cloud Functions 10. Networking Options in GCP 11. Exploring Storage Options in GCP - Part 1 12. Exploring Storage Options in GCP - Part 2 13. Analyzing Big Data Options 14. Putting Machine Learning to Work 15. Section 3: Designing for Security and Compliance
16. Security and Compliance 17. Section 4: Managing Implementation
18. Google Cloud Management Options 19. Section 5: Ensuring Solution and Operations Reliability
20. Monitoring Your Infrastructure 21. Section 6: Exam Focus
22. Case Studies 23. Test Your Knowledge 24. Assessments 25. Other Books You May Enjoy

Summary

In this chapter, we have covered the main aspects of big data relating to the exam. We have covered each service and shown 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.

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 to process data from Pub/Sub and pass 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 to analyze in BigQuery.

We covered BigQuery and understand now that...

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