UNF Professional and Lifelong Learning
UNF Professional and Lifelong Learning · AWS

Building Data Lakes on AWS Training

LevelIntermediate
Duration1 Day
Experience1 year: Data Analytics
Average Salary$115,000
LabsYes

Offered to the Jacksonville and Northeast Florida community through UNF Professional and Lifelong Learning in partnership with Applied Technology Academy — live online or in person, taught by ATA's practitioner instructors.

Course Overview

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
Course Outline
  • Lesson 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
  • Lesson 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
  • Lesson 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
  • Lesson 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
  • Lesson 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
  • Lesson 6:
    • Architecture and course review
    • Post course knowledge check
    • Architecture review
    • Course review
Intended Audience

This course is intended for:

  • Data platform engineers
  • Solutions architects
  • IT professionals
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