Building Data Analytics Solutions Using Amazon Redshift

Description

Duration

1 Days

6 CPD hours

This course is intended for

This course is intended for data warehouse engineers, data platform engineers, and architects and operators who build and manage data analytics pipelines.
Completed either AWS Technical Essentials or Architecting on AWS
Completed Building Data Lakes on AWS

Overview

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

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.

Module A: Overview of Data Analytics and the Data Pipeline

Module 1: Using Amazon Redshift in the Data Analytics Pipeline

Module 2: Introduction to Amazon Redshift

Module 3: Ingestion and Storage

Module 4: Processing and Optimizing Data

Module 5: Security and Monitoring of Amazon Redshift Clusters

Module 6: Designing Data Warehouse Analytics Solutions

Module B: Developing Modern Data Architectures on AWS