Building Data Analytics Solutions using Amazon Redshift - Course Overview

This course guides you through creating an end-to-end analytics solution using Amazon Redshift, a managed cloud-based data warehouse. It covers the complete data lifecycle—from gathering and ingesting data to organizing, storing, and transforming it for analysis. You will also learn how to integrate Redshift with a data lake to support advanced analytics and machine learning scenarios. Along the way, the course highlights practical approaches to ensure data security, improve system performance, and manage costs effectively.

After completing Building Data Analytics Solutions using Amazon Redshift, students will be able to

  • Design and implement a data warehouse analytics solution
  • Compare the features and benefits of data warehouses, data lakes, and modern data architectures
  • Determine and use the best methods, such as compression, to maximise data storage Understanding how data processing and storage affect the analysis and visualisation mechanisms required to obtain actionable business insights. Determining the best clusters, auto scaling, and network topology for a given business use case. Selecting and implementing the right options to ingest, transform, and store data.
  • Safeguard data both in transit and at rest; keep an eye on analytics workloads to spot issues and fix them; and use best practices for cost management.

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Building Data Analytics Solutions using Amazon Redshift - Key Features

  • The Building Data Analytics Solutions using Amazon Redshift offers several key features, including:

  • Comprehensive Curriculum: The course covers all aspects of data warehousing.

  • Participants will have the opportunity to practice hands-on labs.

  • Experienced Instructors: The course is taught by AWS Authorized Instructors with years of experience in AWS, ensuring high-quality instruction.

  • Flexible Schedule: The course is offered in both online and in-person formats, with options for full-time or part-time study.

Who should attend this Course?

  • Data warehouse engineers.
  • Data Platform engineers.
  • Architects and operators who build and manage data pipeline.

Prerequisites for this Course:

  • Participant who has minimum of 1 year experience managing data warehouse.
  • Participant must have undergone either AWS Technical Essentials or Architecting on AWS course.

    Building Data Analytics Solutions using Amazon Redshift - Course Outline Download Course Outline

    • Data analytics use cases
    • Using the data pipeline for analytics

    • Why Amazon Redshift for data warehousing?
    • Overview of 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

    • 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

    • 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

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

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

    • Modern data architectures

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    Course ID: 20039

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