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

What is Redshift?

Redshift is a column-based data warehouse. So, what is a columnar database, you might ask? Most of the databases you will come across would be considered row-based databases. These architectures are common in transactional databases, also known as Online Transaction Processing (OLTP), which provide the backend for most enterprise applications. When a query is sent to a row-based database, it will read through its table one row at a time, reading all the attributes from all of the columns in that row, before moving on to the next row of data. These databases are good at reading tables that may have many columns and fewer rows. When I say fewer rows, I typically mean under a billion, but that exact number can depend on a few variables.

Where columnar databases differ is that when they read a table, they read it column by column. They first read every value within the first column before reading data from the second column. So, if I want to only read the first column...