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

Transmission methods

An important topic we should touch on is transmission methods for transmitting data from remote operations into your data center or cloud. There are several mediums and methods, and each has pros, cons, and varied characteristics. A common scenario is if you have a GPS tracker on a truck or a ship; as your asset moves around the world, it streams its coordinates back to your system. It might even be a piece of hardware with multiple sensors such as GPS, temperature, vibration, and maybe different atmospheric readings all sending data at a specified frequency. An example of this could be some type of weather balloon or a tracker used to track endangered species.

When you have sensors deployed in the field, the common network solutions are Wi-Fi, LoRaWAN, cellular, and satellite. Wi-Fi is generally the cheapest and most effective solution but isn’t always an option in certain environments. Some environments, depending on building materials, can be difficult...