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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 CÅ‚apa Konrad CÅ‚apa
Author Profile Icon Konrad CÅ‚apa
Konrad CÅ‚apa
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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

Big data services

Big data services enable the user to process large amounts of data to provide answers to complex problems. GCP offers many services that tightly integrate to create an End-to-End (E2E) data analysis pipeline. These services are as follows:

  • BigQuery: BigQuery is a highly scalable and fully managed cloud data warehouse. It allows users to perform analytics operations with built-in ML. BigQuery is completely serverless and can host petabytes of data. The underlying infrastructure scales seamlessly and allows parallel data processing. The data can be stored in BigQuery Storage, Cloud Storage, Bigtable, Sheets, or Google Drive. The user defines datasets containing tables. BigQuery uses familiar ANSI-compliant SQL for queries and provides ODBC and JDBC drivers. Users can choose from two types of payment models—one is flexible and involves paying for storage...
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