by Scott Bateman, Janahan Gnanachandran, Jeff DeMuth
Build an end-to-end geospatial
data lake in AWS using popular AWS services such as RDS, Redshift,
DynamoDB, and Athena to manage geodata.
Key Features
- Explore the architecture and different use cases to build and manage geospatial data lakes in AWS
- Discover how to leverage AWS purpose-built databases to store and analyze geospatial data
- Learn how to recognize which anti-patterns to avoid when managing geospatial data in the cloud
Book Description
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.
What you will learn
- Discover how to optimize the cloud to store your geospatial data
- Explore management strategies for your data repository using AWS Single Sign-On and IAM
- Create effective SQL queries against your geospatial data using Athena
- Validate postal addresses using Amazon Location services
- Process structured and unstructured geospatial data efficiently using R
- Use Amazon SageMaker to enable machine learning features in your application
- Explore the free and subscription satellite imagery data available for use in your GIS
Who this book is for
If
you understand the importance of accurate coordinates, but not
necessarily the cloud, then this book is for you. This book is best
suited for GIS developers, GIS analysts, data analysts, and data
scientists looking to enhance their solutions with geospatial data for
cloud-centric applications. A basic understanding of geographic
concepts is suggested, but no experience with the cloud is necessary
for understanding the concepts in this book.
Table of Contents
- Introduction to Geospatial Data in the Cloud
- Quality and Temporal Geospatial Data Concepts
- Geospatial Data Lake Architecture
- Using Geospatial Data with Amazon Redshift
- Using Geospatial Data with Amazon Aurora PostgreSQL
- Serverless Geospatial
- Querying Geospatial Data with Amazon Athena
- Geospatial Containers on AWS
- Geospatial Data with Amazon EMR
- Geospatial Data Analysis using Python on AWS Cloud9
- Geospatial Data Analysis using SageMaker
- Using Amazon QuickSight to Visualize Geospatial Data
- Open Data on AWS
- Leveraging OpenStreetMap on AWS
- Map and Feature Services on AWS
- Satellite Imagery on AWS
