In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. You will learn the components and functionality of the services involved in creating a data lake. You will use AWS Lake Formation to build a data lake, AWS Glue to build a data catalog, and Amazon Athena to analyze data. The course lectures and labs further your learning with the exploration of several common data lake


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

In this course, you will learn to:

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

  • 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

  • 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

  • Data processing and analytics

  • Recognize how data processing applies to a data lake
    Use AWS Glue to process data within a data lake
    Describe how to use Amazon Athena to analyze data in a data lake

  • 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

  • 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

  • Architecture and course review

  • Post course knowledge check
    Architecture review
    Course review


This course is intended for Data platform engineers, Solutions architects, and IT professionals.




We recommend that attendees of this course have: Completed the AWS Technical Essentials classroom course One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course


Length: 1.0 day (8 hours)


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