1 days.


Students with a minimum one-year experience managing data warehouses will benefit from this course. We recommend that attendees of this course have:

    Skills Gained

    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

    Who Can Benefit?

    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

    Outline for Building Data Analytics Solutions Using Amazon Redshift Training

    Course outline

    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
    02/15/2024 - 02/15/2024
    09:00 AM - 05:00 PM
    Eastern Standard Time
    Online Virtual Class
    USD $730.00
    03/29/2024 - 03/29/2024
    09:00 AM - 05:00 PM
    Eastern Standard Time
    Online Virtual Class
    USD $730.00
    04/09/2024 - 04/09/2024
    09:00 AM - 05:00 PM
    Eastern Standard Time
    Online Virtual Class
    USD $730.00