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

Understanding Redshift partitioning

Redshift supports a couple of different partitioning models or, as Redshift calls them, distribution styles in a table – AUTO, EVEN, KEY, and ALL. Many first-time Redshift users simply ignore this optional parameter when they create their first tables, but this option can be the single most important attribute in your entire cluster. When you create a table, if this parameter is not set, it will default to AUTO, and Redshift will try and guess the correct style. So, let’s dive into these different distribution styles and their considerations.

One of the easiest to understand but potentially the most impactful distribution style is the ALL option. The significance of choosing the ALL option is that it will replicate the entire dataset across every node. A Redshift database can have up to 128 nodes in a cluster. If you have a table that is one petabyte in size and you choose the ALL option, you now have a 128-PB dataset. This is pretty...