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Geospatial Data Analytics on AWS

You're reading from   Geospatial Data Analytics on AWS Discover how to manage and analyze geospatial data in the cloud

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Product type Paperback
Published in Jun 2023
Publisher Packt
ISBN-13 9781804613825
Length 276 pages
Edition 1st Edition
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Authors (3):
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Scott Bateman Scott Bateman
Author Profile Icon Scott Bateman
Scott Bateman
Jeff DeMuth Jeff DeMuth
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Jeff DeMuth
Janahan Gnanachandran Janahan Gnanachandran
Author Profile Icon Janahan Gnanachandran
Janahan Gnanachandran
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Table of Contents (23) Chapters Close

Preface 1. Part 1: Introduction to the Geospatial Data Ecosystem
2. Chapter 1: Introduction to Geospatial Data in the Cloud FREE CHAPTER 3. Chapter 2: Quality and Temporal Geospatial Data Concepts 4. Part 2: Geospatial Data Lakes using Modern Data Architecture
5. Chapter 3: Geospatial Data Lake Architecture 6. Chapter 4: Using Geospatial Data with Amazon Redshift 7. Chapter 5: Using Geospatial Data with Amazon Aurora PostgreSQL 8. Chapter 6: Serverless Options for Geospatial 9. Chapter 7: Querying Geospatial Data with Amazon Athena 10. Part 3: Analyzing and Visualizing Geospatial Data in AWS
11. Chapter 8: Geospatial Containers on AWS 12. Chapter 9: Using Geospatial Data with Amazon EMR 13. Chapter 10: Geospatial Data Analysis Using R on AWS 14. Chapter 11: Geospatial Machine Learning with SageMaker 15. Chapter 12: Using Amazon QuickSight to Visualize Geospatial Data 16. Part 4: Accessing Open Source and Commercial Platforms and Services
17. Chapter 13: Open Data on AWS 18. Chapter 14: Leveraging OpenStreetMap on AWS 19. Chapter 15: Feature Servers and Map Servers on AWS 20. Chapter 16: Satellite and Aerial Imagery on AWS 21. Index 22. Other Books You May Enjoy

Understanding containers

So, to kick off this chapter, let’s provide a quick overview of what a container is and why they are all the rage. In this chapter, I don’t want to get into the specifics of the inner workings of containers, but in short, you can think of them as very lightweight and portable virtual machines or machine images but only in the sense of starting multiple instances of an application. From an inner-workings standpoint, containers more closely resemble a single server with multiple processes running. A typical virtual machine with all the OS components can easily be a couple of gigabytes – and in the case of Windows images, upward of 10, 20, 30, or more gigabytes. A container’s size can usually be measured in megabytes. Most containers share the resources of their host OS and only contain the libraries they need to run their application. Another notable benefit is how quickly they can launch. When you boot a full computer image, you usually...

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