Building Data Lakes on AWS (BDLA)

Module 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

Module 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

Module 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

Module 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

Module 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

Module 6: Architecture and course review

• Post course knowledge check
• Architecture review
• Course review

Wir empfehlen, dass die Teilnehmer dieses Kurses zuvor folgenden Kenntnisse haben:

• Abschluss des AWS Technical Essentials (AWSE) Trainings
• Ein Jahr Erfahrung im Aufbau von Datenanalyse-Pipelines oder Abschluss des digitalen Kurses „Data Analytics Fundamentals”

• Data platform engineers
• Solutions architects
• IT professionals