Course Outline
Pre-Requisites
One year of experience building data analytics pipelines or have completed the digital AWS Data Analytics Fundamentals .
Lessons
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.
WHO SHOULD ATTEND?
- Data platform engineers
- Solution Architects
- IT Professionals
WHAT YOU'LL LEARN
- 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
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
Cancellation Policy
We require 16 calendar days notice to reschedule or cancel any registration. Failure to provide the required notification will result in 100% charge of the course. If a student does not attend a scheduled course without prior notification it will result in full forfeiture of the funds and no reschedule will be allowed. Within the required notification period, only student substitutions will be permitted. Reschedules are permitted at anytime with 16 or more calendar days notice. Enrollments must be rescheduled within six months of the cancel date or funds on account will be forfeited.
Training Location
Online Classroom
your office
your city,
your province
your country
I would never take another course that starts at 11AM and goes to 9PM again. The way the course was laid out really took away from the capturing of what was presented as it was 5-6 hours of watching a screen before getting to the actual labs. There has to be a better way to lay out this particular course. In my previous course, the lectures were broken up by labs which worked out fantastic and kept you engaged in the course. There were days when in order to actually complete the labs, would go over the 9PM day end time frame. Was able to get the primary labs done, but if you want to get all the content completed, you cannot complete it in the window of this course, you will need to come back on your own time.