Building Data Lakes on AWS – Intensive Training («AWSB04»)

• Applying data lake methodologies in planning and designing a data lake
• Articulating the components and services required for building an AWS data lake
• Securing a data lake with appropriate permission
• Ingesting, storing, and transforming data in a data lake
• Querying, analyzing, and visualizing data within a data lake

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.

Outline

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

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

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

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

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

6 Architecture and course review

• Post course knowledge check
• Architecture review
• Course review

This course includes presentations, interactive demos, practice labs, discussions, and class exercises.

This course is intended for the following job roles:

• Data Engineer
• Machine Learning & AI

We recommend that attendees of this course:

• have taken the free digital course Data Analytics Fundamentals 
• and have attended the following course (or have equivalent knowlege):

This course can be used as preparation for the following official AWS Certification: AWS Certified Machine Learning – Specialty