After taking this course, you will be able 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
This course is for data platform engineers, solution architects, and IT professionals.
Prerequisites: one year of experience building data analytics pipelines or have completed the digital AWS Data Analytics Fundamentals.
Outline
Topics are covered in the order they appear, however some jumping back-and-forth is necessary to explore relevant topics at the time they are first encountered. Below is a list of topics that will be covered by module:
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
Lab exercises are designed to cover one particular topic and help with learning retention.
Amazon Web Services and AWS are trademarks of Amazon.com, Inc. or its affiliates in the United States and/or other countries.
This course is compatible with AWS official curriculum.
Net Cirque is an AWS Consulting Partner.
