AMA18 – Building Data Analytics Solutions Using Amazon Redshift

Course description

In this course, you will build a data analytics solution using Amazon Redshift, a cloud data warehouse
service. The course focuses on the data collection, ingestion, cataloging, storage, and processing
components of the analytics pipeline. You will learn to integrate Amazon Redshift with a data lake to
support both analytics and machine learning workloads. You will also learn to apply security, performance,
and cost management best practices to the operation of Amazon Redshift.

Activities

This course includes presentations, interactive demos, practice labs, discussions, and class exercises.

Course objectives

In this course, you will learn to:
Compare the features and benefits of data warehouses, data lakes, and modern data architectures
Design and implement a data warehouse analytics solution
Identify and apply appropriate techniques, including compression, to optimize data storage
Select and deploy appropriate options to ingest, transform, and store data
Choose the appropriate instance and node types, clusters, auto scaling, and network topology for a particular business use case
Understand how data storage and processing affect the analysis and visualization mechanisms needed to gain actionable business insights
Secure data at rest and in transit
Monitor analytics workloads to identify and remediate problems
Apply cost management best practices

Intended audience

This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.

Prerequisites

Students with a minimum one-year experience managing data warehouses will benefit from this course.
We recommend that attendees of this course have:
Completed either AWS Technical Essentials or Architecting on AWS
Completed Building Data Lakes on AWS

Enroll today

Visit aws.training to find a class today.