Building Data Lakes on AWS Course Overview

Target Audience for Building Data Lakes on AWS

This AWS data lake course offers in-depth training on setting up and managing data lakes, ideal for IT professionals focused on data management and analytics.

• Data Engineers
• Data Scientists
• Data Analysts
• Cloud Architects
• IT Managers
• Database Administrators
• Big Data Specialists
• Business Intelligence Professionals
• System Administrators
• Developers interested in data lake architectures

Learning Objectives - What you will Learn in this Building Data Lakes on AWS?

Introduction to Learning Outcomes:

The Building Data Lakes on AWS course is designed to equip students with the skills needed to effectively construct, manage, and utilize data lakes on the AWS platform, focusing on concepts such as storage, processing, analysis, and security.

Learning Objectives and Outcomes:

• Understand the fundamental value and concepts of data lakes compared to traditional data warehouses.
• Learn the key components that constitute a data lake and explore common architectures integrating data lakes.
• Gain knowledge of data ingestion methods, cataloging with AWS Glue, and preparation techniques for optimal data storage and retrieval in AWS.
• Acquire hands-on experience in setting up a basic data lake on AWS through practical labs.
• Recognize the importance of data processing within a data lake and how to apply these concepts using AWS Glue.
• Learn to analyze data efficiently using Amazon Athena within a data lake environment.
• Explore the features, benefits, and security model of AWS Lake Formation for creating and managing data lakes.
• Gain practical skills in building a data lake using AWS Lake Formation through guided laboratory exercises.
• Understand how to automate data lake creation with AWS Lake Formation blueprints and workflows and enforce security and access controls.
• Develop the ability to match records and visualize data effectively using AWS Lake Formation FindMatches and Amazon QuickSight, respectively.