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Building Data Lakes on AWS

Last Updated: 08-03-2025

The Building Data Lakes on AWS course is designed to help you understand how to architect, implement, and manage scalable and secure data lakes using Amazon Web Services. In this hands-on training, you'll learn how to collect, store, analyze, and secure massive amounts of data in a centralized repository, leveraging AWS services like S3, Glue, Redshift, and Lake Formation. This course provides the skills necessary to create a robust data lake solution that powers analytics, machine learning, and business intelligence.

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Schedule Learners Course Fee Register Your Interest
April 28th - 28th
09:00 - 17:00 (CST)
Live Virtual Classroom
USD 320
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April 21st - 21st
09:00 - 17:00 (CST)
Live Virtual Classroom
USD 320
May 12th - 15th
09:00 - 13:00 (CST)
Live Virtual Classroom
USD 320
June 02nd - 02nd
09:00 - 17:00 (CST)
Live Virtual Classroom
USD 320

Course Prerequisites

  • Basic understanding of cloud computing concepts.
  • Familiarity with AWS services (e.g., S3, EC2, IAM) is beneficial but not mandatory.
  • Recommended: Knowledge of data warehousing concepts or experience with data processing tools like SQL or Python.

 

Learning Objectives

By the end of this course, you will be able to:

  1. Design and implement a scalable and cost-effective data lake architecture on AWS.
  2. Use AWS services like Amazon S3, AWS Glue, and AWS Lake Formation to manage data ingestion, transformation, and storage.
  3. Implement security and compliance measures to protect sensitive data in your data lake.
  4. Integrate structured and unstructured data into a unified data lake for analytics and machine learning.
  5. Set up automated data pipelines and data cataloging systems for better data discovery and accessibility.
  6. Optimize performance and cost-efficiency for big data processing using AWS tools like Redshift and Athena.
  7. Enable real-time analytics and decision-making by integrating data lakes with AWS analytics and machine learning services.

Target Audience

This course is ideal for:

  • Data engineers, architects, and professionals responsible for designing and managing data lakes.
  • Data scientists and analysts who want to learn how to work with large-scale datasets in the cloud.
  • Cloud architects looking to deepen their knowledge of data lake architectures and AWS data services.
  • IT professionals interested in implementing data lake solutions for analytics and big data applications.

Course Modules

  • Introduction to Data Lakes:

    • Value and components of data lakes.
    • Common architectures and comparisons with data warehouses.
  • Data Ingestion, Cataloging, and Preparation:

    • Data ingestion strategies and AWS Glue crawlers.
    • Data formatting, partitioning, and compression techniques.
    • Lab: Setting up a simple data lake.
  • Data Processing and Analytics:

    • Data processing applications within a data lake.
    • Using AWS Glue for data processing.
    • Analyzing data with Amazon Athena.
  • Building a Data Lake with AWS Lake Formation:

    • Features and benefits of AWS Lake Formation.
    • Creating and securing data lakes using Lake Formation.
    • Lab: Building a data lake with AWS Lake Formation.
  • Additional Lake Formation Configurations:

    • Automating data lake creation with blueprints and workflows.
    • Applying security and access controls.
    • Data visualization with Amazon QuickSight.
    • Labs: Automating data lake creation and visualizing data.
  • Architecture Review and Course Wrap-up:

    • Review of course content and architecture best practices.

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