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Course Overview
By 2025, over 80% of the world’s data will be unstructured, challenging organizations to rethink how they store, process, and analyze information. This hands-on course equips you with the skills to build an operational data lake that supports analysis of both structured and unstructured data using AWS.
Through expert-led instruction and real-world labs, you'll learn how to build scalable, secure data lakes on AWS using services like AWS Lake Formation, AWS Glue, and Amazon Athena. You'll explore the components of a modern data lake architecture, understand the functionality of the services involved in creating a data lake, and gain experience designing solutions that turn raw data into business insight.
Who Should Attend
Data platform engineers
Solutions Architects
IT professionals
Course Objectives
This course is designed to give you practical, hands-on experience with the key AWS services used in building data lakes. You’ll explore common architecture patterns and learn how to ingest, catalog, transform, and query data—all within a secure and scalable framework. With labs and guided instruction, you’ll walk away ready to build your own data lake infrastructure and support modern data analytics efforts.
By the end of the course, you’ll be able to:
- Identify the core components of a modern data lake architecture
- Use AWS Lake Formation to build a data lake and manage access controls
- Catalog and transform data with AWS Glue
- Query datasets using Amazon Athena and visualize results with QuickSight
Course Outline
Module 1: Introduction to Data Lakes
- Define what a data lake is and why it matters
- Compare data lakes to traditional data warehouses
- Break down the key components of a data lake architecture
- Review several common data lake architectures used in AWS environments
Module 2: Ingesting, Cataloging, and Preparing Data
- Ingest structured and unstructured data into your data lake
- Use AWS Glue crawlers to create a searchable data catalog
- Apply data formatting, partitioning, and compression for efficient storage
- Lab: Set up and populate a simple data lake
Module 3: Data Processing and Analysis
- Process data within your AWS data lake using AWS Glue
- Perform ad hoc queries using Amazon Athena
- Explore real-time analytics use cases
- Lab: Transform and query data sets
Module 4: Building and Securing with AWS Lake Formation
- Use AWS Lake Formation to build a data lake from start to finish
- Implement fine-grained access control and secure permissions
- Understand the functionality of the services involved in creating a data lake
- Lab: Build and secure your own AWS data lake
Module 5: Automation and Visualization
- Automate data lake setup using Lake Formation blueprints
- Create visual dashboards using Amazon QuickSight
- Lab: Automate workflows and visualize data analytics results
Module 6: Review and Wrap-Up
- Review key concepts and best practices
- Take a final knowledge check to reinforce learning
- Participate in a group discussion of data lake architecture strategies