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Building Google Cloud Platform Solutions

You're reading from   Building Google Cloud Platform Solutions Develop scalable applications from scratch and make them globally available in almost any language

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Product type Course
Published in Mar 2019
Publisher Packt
ISBN-13 9781838647438
Length 778 pages
Edition 1st Edition
Languages
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Authors (3):
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Steven Porter Steven Porter
Author Profile Icon Steven Porter
Steven Porter
Legorie Rajan PS Legorie Rajan PS
Author Profile Icon Legorie Rajan PS
Legorie Rajan PS
Ted Hunter Ted Hunter
Author Profile Icon Ted Hunter
Ted Hunter
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Table of Contents (29) Chapters Close

Title Page
Copyright and Credits
About Packt
Contributors
Preface
1. Why GCP? FREE CHAPTER 2. The Google Cloud Console 3. APIs, CLIs, IAM, and Billing 4. Google App Engine 5. Google Kubernetes Engine 6. Google Cloud Functions 7. Google Compute Engine 8. NoSQL with Datastore and Bigtable 9. Relational Data with Cloud SQL and Cloud Spanner 10. Google Cloud Storage 11. Stackdriver 12. Change Management 13. GCP Networking for Developers 14. Messaging with Pub/Sub and IoT Core 15. Integrating with Big Data Solutions on GCP 16. Compute 17. Storage and Databases 18. Networking 19. Security 20. Machine Learning and Big Data 21. Management Tools 22. Best Practices 1. Other Books You May Enjoy Index

Big data and Google Cloud Platform


One of the major drivers towards the public cloud model is that public clouds drastically reduce both the upfront infrastructure and long-term operational cost of projects. This is largely due to the cost advantage public clouds gain by achieving economies of scale. One side effect of achieving economies of scale is that otherwise infeasible solutions become economically sound. As we saw in Chapter 10, Google Cloud Storage, one space this holds especially true is in data storage solutions, such as Google's ability to provide very cheap, always-online, nearline, and coldline storage. More generally, by lowering the cost of data storage, customers witness a shift in the cost-to-value ratio of storing large amounts of data. As a result, developers and analysts have access to much richer datasets, enabling very powerful data analysis and machine learning techniques.

Similarly, public clouds provide organizations with effectively unlimited amounts of on-demand...

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