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Building Data Lakes on AWS

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Last Update:

October 7, 2025

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Course Overview

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 architectures.

Course 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

Methodology

This course includes presentations, lecture, hands-on labs, and group exercises.

Target Audience

Data platform engineers
Solutions architects
IT professionals

Prerequisite

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

Training Content

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