Outline

Topics are covered in the order they appear, however some jumping back-and-forth is necessary to explore relevant topics at the time they are first encountered. Below is a list of topics that will be covered by module:

• Module 1: Introduction to data lakes
  • Describe the value of data lakes
  • Compare data lakes and data warehouses
  • Describe the components of a data lake
  • Recognize common architectures built on data lakes
• Module 2: Data ingestion, cataloging, and preparation
  • Describe the relationship between data lake storage and data ingestion
  • Describe AWS Glue crawlers and how they are used to create a data catalog
  • Identify data formatting, partitioning, and compression for efficient storage and query
  • Lab 1: Set up a simple data lake
• Module 3: Data processing and analytics
  • Recognize how data processing applies to a data lake
  • Use AWS Glue DataBrew to process data within a data lake
  • Describe how to use Amazon Athena to analyze data in a data lake
• Module 4: Building a data lake with AWS Lake Formation
  • Describe the features and benefits of AWS Lake Formation
  • Use AWS Lake Formation to create a data lake
  • Understand the AWS Lake Formation security model
  • Lab 2: Build a data lake using AWS Lake Formation
• Module 5: Additional Lake Formation configurations
  • Automate AWS Lake Formation using blueprints and workflows
  • Apply security and access controls to AWS Lake Formation
  • Match records with AWS Lake Formation FindMatches
  • Visualize data with Amazon QuickSight
  • Lab 3: Automate data lake creation using AWS Lake Formation blueprints
  • Lab 4: Data visualization using Amazon QuickSight
• Module 6: Architecture and course review
  • Post course knowledge check
  • Architecture review
  • Course review