Book Image

Geospatial Data Analytics on AWS

By : Scott Bateman, Janahan Gnanachandran, Jeff DeMuth
Book Image

Geospatial Data Analytics on AWS

By: Scott Bateman, Janahan Gnanachandran, Jeff DeMuth

Overview of this book

Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data. You’ll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you’ll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you’ll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning. By the end of this book, you’ll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.
Table of Contents (23 chapters)
1
Part 1: Introduction to the Geospatial Data Ecosystem
4
Part 2: Geospatial Data Lakes using Modern Data Architecture
10
Part 3: Analyzing and Visualizing Geospatial Data in AWS
16
Part 4: Accessing Open Source and Commercial Platforms and Services

Architectural considerations

Cost optimization opportunities are realized automatically with AWS built-in features such as S3 Intelligent-Tiering, and the Amazon Aurora Serverless v2 auto-scaling improvements made in April 2022. As cloud technology and hardware improve, the compute cost of a given workload continues to go down. When coupled with AWS Lambda to modernize from traditional virtual servers, the cost of a well-architected enterprise GIS environment is a fraction of what it was a decade ago.

During the Athena setup and configuration topic, it was noted that Amazon Athena has the capability to connect to non-AWS databases – even those on-premises. While technically possible, it should be noted that latency can be the enemy of a highly distributed GIS environment. Access between two endpoints on a low-latency, high-bandwidth network is always going to outperform the alternative. To mitigate performance surprises, a network landscape architecture should be reviewed...