This module provides an overview of data lakes, their purpose, and how they differ from data warehouses. It also covers the components and architectures involved in data lakes.

This module focuses on the processes of ingesting data into a data lake, cataloging the data, and preparing it for analysis. It covers topics such as data lake storage, data ingestion methods, crawling and cataloging data, data formatting, partitioning, compression, and querying data with Amazon Athena.

This module introduces AWS Lake Formation, a service that helps build and manage data lakes on AWS. It covers the basic permission model, and provides an overview of the service’s features and capabilities.

This module covers data transformation techniques and tools like AWS Glue for processing and analyzing data in the data lake. It includes hands-on demos and a technical talk on Glue and Athena Federated Queries.

This module explores advanced features and configurations of AWS Lake Formation, including blueprints, workflows, and fine-grained access control. It also covers data visualization with Amazon QuickSight.

This module introduces the concept of modern data architecture and its implementation on AWS. It covers data movement scenarios, data sharing models, and relevant readings.