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.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning 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

  • Module A: Overview of Data Analytics and the Data Pipeline

  • Data analytics use cases
    Using the data pipeline for analytics

  • Module 1: Using Amazon Redshift in the Data Analytics Pipeline

  • Why Amazon Redshift for data warehousing?
    Overview of Amazon Redshift

  • Module 2: Introduction to Amazon Redshift

  • Amazon Redshift architecture
    Interactive Demo 1: Touring the Amazon Redshift console
    Amazon Redshift features
    Practice Lab 1: Load and query data in an Amazon Redshift cluster

  • Module 3: Ingestion and Storage

  • Ingestion
    Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API
    Data distribution and storage
    Interactive Demo 3: Analyzing semi-structured data using the SUPER data type
    Querying data in Amazon Redshift
    Practice Lab 2: Data analytics using Amazon Redshift Spectrum

  • Module 4: Processing and Optimizing Data

  • Data transformation
    Advanced querying
    Practice Lab 3: Data transformation and querying in Amazon Redshift
    Resource management
    Interactive Demo 4: Applying mixed workload management on Amazon Redshift
    Automation and optimization
    Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster

  • Module 5: Security and Monitoring of Amazon Redshift Clusters

  • Securing the Amazon Redshift cluster
    Monitoring and troubleshooting Amazon Redshift clusters

  • Module 6: Designing Data Warehouse Analytics Solutions

  • Data warehouse use case review
    Activity: Designing a data warehouse analytics workflow

  • Module B: Developing Modern Data Architectures on AWS

  • Modern data architectures


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




Students with a minimum one-year experience managing data warehouses will benefit from this course.


Length: 1.0 day (8 hours)


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