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

Redshift AQUA

Advanced Query Accelerator (AQUA) is a feature of Redshift that allows you to perform analysis with massive parallelization. It’s also a good feature for our previous geohash scenario. AQUA would be a good fit if we needed to read the entire planet or basically all or most geohashes that are stored as folders in S3. AQUA works in combination with SQL LIKE queries. You would write a query that says select ID where GEOHASH LIKE %A%, and Redshift would spin up multiple smaller compute instances that would go out and pull the data in parallel, combine it, and then return it. This is a similar approach to how Hadoop clusters use parallelization on big datasets to improve speed in performance. You take a massive amount of data, split it up, set individual nodes to pull and process the data in parallel, and then aggregate the results when they are finished. Having a capability such as this is incredibly powerful when it’s natively built into the data warehouse...