Building Data Lakes on AWS

Learn how to build an AWS Data Lake.

In this course, you will learn how to build an operational data lake that supports analysis of both structured and unstructured data. 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.

1 day/7 hours of instruction
Public Classroom Pricing

Starting at: $675(USD)

Group Rate: $625

Private Group Pricing

Have a group of 5 or more students? Request special pricing for private group training today.

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Part 1: Introduction to data lakes

  1. Describe the value of data lakes
  2. Compare data lakes and data warehouses
  3. Describe the components of a data lake
  4. Recognize common architectures built on data lakes

Part 2: Data ingestion, cataloging and preparation

  1. Describe the relationship between data lake storage and data ingestion
  2. Describe AWS Glue crawlers and how they are used to create a data catalog
  3. Identify data formatting, partitioning, and compression for efficient storage and query
  4. Lab 1: Set up a simple data lake

Part 3: Data processing and analytics

  1. Recognize how data processing applies to a data lake
  2. Use AWS Glue to process data within a data lake
  3. Describe how to use Amazon Athena to analyze data in a data lake

Part 4: Building a data lake with AWS Lake Formation

  1. Describe the features and benefits of AWS Lake Formation
  2. Use AWS Lake Formation to create a data lake
  3. Understand the AWS Lake Formation security model
  4. Lab 2: Build a data lake using AWS Lake Formation

Part 5: Additional Lake Formation configurations

  1. Automate AWS Lake Formation using blueprints and workflows
  2. Apply security and access controls to AWS Lake Formation
  3. Match records with AWS Lake Formation FindMatches
  4. Visualize data with Amazon QuickSight
  5. Lab 3: Automate data lake creation using AWS Lake Formation blueprints
  6. Lab 4: Data visualization using Amazon QuickSight

Part 6: Architecture and course review

  1. Post course knowledge check
  2. Architecture review
  3. Course review

  • Working knowledge of core AWS services and public cloud implementation
  • One year of experience building data analytics pipelines or have completed the Data Analytics Fundamentals digital course
  • Attended one of Architecting on AWS, Developing on AWS, or Systems Operations on AWS

  • 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

A full refund will be issued for class cancellations made at least 10 business days before the course begins. Payment is non‑refundable for cancellations or reschedules made within 10 business days from the course start date and for No‑Shows (students who do not attend class).

For reschedules made within 10 business days from the course start date, students must reschedule immediately for a current, published course, up to a maximum of six months from the original date.

A student may reschedule a class or exam up to 2 times. Any additional reschedules will not be allowed.

Building Data Lakes on AWS Schedule

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