Learn to build scalable and secure data lakes with AWS Lake Formation.
This intermediate-level course helps you explore the process of building a data lake manually and automatically. Our expert, accredited AWS instructors help you learn to scale your storage environments within a data lake configuration while optimizing for cost and performance. Throughout the course, you’ll use:
- AWS Lake Formation to build a data lake from scratch
- AWS Glue for data cataloging
- Amazon Athena for analytics
- AWS QuickSight for data visualization
Whether you attend virtually or in person, you’ll have the opportunity to ask questions, work through solutions, and get real-time feedback from accredited AWS instructors with deep technical knowledge.
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Overview
Understand the data analytics pipeline and prepare to deploy data lakes that solve your business’s data challenges.
Do you want to develop business insights from siloed data stores with the help of a data lake? This Building Data Lakes on AWS course helps you practice building in a hands-on environment and walks you through several data lake architectures and the first three stages of the data analytics pipeline: ingestion, cataloging, and processing.
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Skills Covered
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
Prerequisites
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
Target Audience
This course is intended for:
- Data platform engineers
- Solutions architects
- IT professionals

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 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
Dates & Locations
July 13, 2026 - July 13, 2026
July 13, 2026 - July 13, 2026
October 12, 2026 - October 12, 2026
October 12, 2026 - October 12, 2026

Exam & Certification
There is no exam directly associated with this course. However, AWS offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2026 and beyond. Achieving AWS credentials is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
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