Building Data Lakes on AWS

Part 1: Introduction to data lakes

1. Describe the value of data lakes
2. Compare data lakes and data warehouses
3. Describe the components of a data lake
4. Recognize common architectures built on data lakes

Part 2: Data ingestion, cataloging and preparation

1. Describe the relationship between data lake storage and data ingestion
2. Describe AWS Glue crawlers and how they are used to create a data catalog
3. Identify data formatting, partitioning, and compression for efficient storage and query
4. Lab 1: Set up a simple data lake

Part 3: Data processing and analytics

1. Recognize how data processing applies to a data lake
2. Use AWS Glue to process data within a data lake
3. Describe how to use Amazon Athena to analyze data in a data lake

Part 4: Building a data lake with AWS Lake Formation

1. Describe the features and benefits of AWS Lake Formation
2. Use AWS Lake Formation to create a data lake
3. Understand the AWS Lake Formation security model
4. Lab 2: Build a data lake using AWS Lake Formation

Part 5: Additional Lake Formation configurations

1. Automate AWS Lake Formation using blueprints and workflows
2. Apply security and access controls to AWS Lake Formation
3. Match records with AWS Lake Formation FindMatches
4. Visualize data with Amazon QuickSight
5. Lab 3: Automate data lake creation using AWS Lake Formation blueprints
6. Lab 4: Data visualization using Amazon QuickSight

Part 6: Architecture and course review

1. Post course knowledge check
2. Architecture review
3. Course review

• Working knowledge of core AWS services and public cloud implementation
• One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course
• Attended one of Architecting on AWS, Developing on AWS, or Systems Operations on AWS

• Apply data lake methodologies in planning and designing a data lake
• Articulate the components and services required for building an AWS data lake
• Secure a data lake with appropriate permission
• Ingest, store, and transform data in a data lake
• Query, analyze, and visualize data within a data lake

A full refund will be issued for class cancellations made at least 15 business days before the course begins. Payment is non‑refundable for cancellations or reschedules made within 15 business days from the course start date and for No‑Shows (students who do not attend class).
For reschedules made within 15 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of 90 days from the original date.
A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.