What this book covers
Chapter 1, Introduction to Geospatial Data in the Cloud, shows us how to work with Geospatial Data in the cloud, and the economics of storing and analyzing the data in the cloud.
Chapter 2, Quality and Temporal Geospatial Concepts, explores the different quality characteristics of geospatial data. Additionally, concepts will be presented that show how the time-specific (temporal) aspects of data can be captured and designated in the data structure.
Chapter 3, Geospatial Data Lake Architecture, talks about designing and building a Data Lake architecture to ingest, store manage, analyze, and visualize the geospatial data in AWS
Chapter 4, Using Geospatial data with Redshift, shows an overview of Amazon Redshift and how to store and analyze geospatial data using Amazon Redshift
Chapter 5, Using Geospatial Data with Amazon Aurora PostgresSQL, provides an overview of Amazon Aurora PostgreSQL along with the PostGIS extension. We will also understand how to store and analyze geospatial data using Amazon Aurora PostgreSQL with hands-on examples
Chapter 6, Serverless Options for Geospatial, provides an overview AWS serverless technologies and how to use AWS Lambda and other managed services to collect, store, and analyse geospatial data. We will also learn about event-driven mechanisms for both on-demand and scheduled workloads.
Chapter 7, Querying Geospatial Data with Amazon Athena, Amazon Athena provides scalable, robust access to a wide range of geospatial data sources on AWS. Powerful geospatial functions allow for on-the-fly analysis and transformation capabilities that can be applied in a scalable and cost-effective manner. This chapter will explore geospatial use patterns with Athena to gain insights and create new datasets.
Chapter 8, Geospatial Containers on AWS, covers what containers are and how they benefit geospatial workloads on the cloud.
Chapter 9, Using Geospatial Data with Amazon EMR, explores Elastic MapReduce (EMR). We will walk through a demo of Hadoop, EMR and visualize geospatial data using them.
Chapter 10, Geospatial Analysis using R on AWS, explores use of the R programming language to construct commands and procedures for geospatial analysis on AWS
Chapter 11, Geospatial Machine Learning with SageMaker, SageMaker is the cornerstone AWS service for statistical and machine learning computing. This chapter provides readers with step by step guidance to import, analyze, and visualize geospatial data on AWS using SageMaker
Chapter 12, Using Amazon QuickSight to Visualize Geospatial Data, delves into how geospatial data on AWS can be converted into visualizations that can be shared with others and combined with web maps and other geospatial visualizations.
Chapter 13, Open Data on AWS, Open Data on AWS offers public data made available through AWS services from across the globe. Whether directly interacting with the source or downloading for analysis and transformation, datasets on demographics, public health, industry, and environment are ready to use.
Chapter 14, Leveraging OpenStreetMap, OpenStreetMap has more crowdsourced updates than any other geospatial dataset on the planet. From roads and buildings to businesses and parks, millions of places can be found on OpenStreetMap. This chapter will show what data can be leveraged using Amazon Athena queries directly against the latest updates.
Chapter 15, Map and Feature Services on AWS, looks at tools and services available on AWS to create a durable, scalable platform optimized for the cloud.
Chapter 16, Satellite Imagery on AWS, talks about how to find and use this data and use Amazon SageMaker for incorporating near real-time machine learning into your applications.
