Summary
In this chapter, we presented a comprehensive overview of the geospatial data lake architecture and its implementation using AWS services. We went through the technical details of building and managing a geospatial data lake, covering topics such as data collection and ingestion, data storage, data processing and transformation, data analytics and insights, and data visualization and mapping. You could also use other AWS services for security, privacy, governance, and orchestration requirements of your geospatial data lake. You can use Amazon CloudWatch and other monitoring tools to monitor your application’s performance and troubleshoot any issues that arise. This reference architecture would enable you to efficiently store and manage large volumes of geospatial data and to perform advanced analytics and ML on the data to gain insights and make informed decisions. In the next chapter, we will explore how we can use Redshift for geospatial tasks.
