Building Data Analytics Solutions Using Amazon Redshift

Transform your data analytics capabilities with our Amazon Redshift course, tailored for data engineers and architects to master cloud-based data warehousing for optimized analytics solutions.

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Essential Skills Gained

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Design and implement comprehensive data warehouse solutions using Amazon Redshift.

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Optimize data storage and processing to enhance analytics performance.

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Apply security measures and cost management strategies effectively.

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Integrate and manage data pipelines for advanced analytics and machine learning.

Format

  • Instructor-led
  • 1 days with lectures and hands-on labs.

Audience

  • Data warehouse engineers
  • Data platform engineers
  • Architects
  • Operators managing data analytics pipelines

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.

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Upcoming Course Dates

August 8-8, 2025

9:00 AM - 5:00 PM

Virtual: Online - US/Eastern

Enroll

$675

October 17-17, 2025

6:00 AM - 2:00 PM

Virtual: Online - America/Los_Angeles

Enroll

$675

Course Outline

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Module A: Overview of Data Analytics and the Data Pipeline

  1. Data analytics use cases

  2. Using the data pipeline for analytics

Module 1: Using Amazon Redshift in the Data Analytics Pipeline

  1. Why Amazon Redshift for data warehousing?

  2. Overview of Amazon Redshift

Module 2: Introduction to Amazon Redshift

  1. Amazon Redshift architecture

  2. Interactive Demo 1: Touring the Amazon Redshift console

  3. Amazon Redshift features

  4. Practice Lab 1: Load and query data in an Amazon Redshift cluster

Module 3: Ingestion and Storage

  1. Ingestion

  2. Interactive Demo 2: Connecting your Amazon Redshift cluster using a Jupyter notebook with Data API

  3. Data distribution and storage

  4. Interactive Demo 3: Analyzing semi-structured data using the SUPER data type

  5. Querying data in Amazon Redshift

  6. Practice Lab 2: Data analytics using Amazon Redshift Spectrum

Module 4: Processing and Optimizing Data

  1. Data transformation

  2. Advanced querying

  3. Practice Lab 3: Data transformation and querying in Amazon Redshift

  4. Resource management

  5. Interactive Demo 4: Applying mixed workload management on Amazon Redshift

  6. Automation and optimization

  7. Interactive demo 5: Amazon Redshift cluster resizing from the dc2.large to ra3.xlplus cluster

Module 5: Security and Monitoring of Amazon Redshift Clusters

  1. Securing the Amazon Redshift cluster

  2. Monitoring and troubleshooting Amazon Redshift clusters

Module 6: Designing Data Warehouse Analytics Solutions

  1. Data warehouse use case review

  2. Activity: Designing a data warehouse analytics workflow

Module B: Developing Modern Data Architectures on AWS

  1. Modern data architectures

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