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Building Data Analytics Solutions Using Amazon Redshift


Course description

You will create a data analytics solution in this course utilizing the cloud data warehouse provider Amazon Redshift. The course focuses on the parts of the analytics pipeline that deal with data collection, ingestion, cataloging, storage, and processing. You will discover how to use Amazon Redshift with a data lake to support machine learning and analytics workloads. Additionally, you will find how to use Amazon Redshift’s best practices for security, performance, and cost control.


This AWS course will include the following activities: 

  • Group Discussions 
  • Live Demonstrations 
  • Presentations 
  • Hands-On Labs 
  • Knowledge Checks

Course Objectives

Here’s what you’ll learn in this AWS course: 

  • Compare the attributes and advantages of data lakes, data warehouses, and contemporary data architectures. 
  • Create and put in place an analytics solution for a data warehouse. 
  • Determine the most effective methods for data storage, including compression, and use them. 
  • Choose and implement the best alternatives for ingesting, transforming, and storing data. 
  • For a specific business use case, select the proper clusters, auto-scaling, instance and node kinds, and network topology. 
  • Recognize how data processing and storage impact the analysis and visualization techniques required to produce relevant business insights. 
  • Secure analytics workloads to find and fix issues with data in transit and at rest. 
  • Implement best practices for cost management

Intended Audience

You can take on this course if you belong to the following category of individuals: 

  • Data warehouse engineers, data platform engineers, architects, and operators who create and oversee data analytics pipelines are the target audience for this course.


To qualify for taking up this course, you’ll need the following: 

This course will be most helpful to learners with at least a year of experience managing data warehouses.

Module Breakdown

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


This certification enables businesses to find and nurture personnel with the essential competencies to carry out cloud activities. AWS Certified Data Analytics – Specialty status verifies knowledge of how to use AWS data lakes and analytics services to extract insights from data.

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The AWS Certified Data Analytics – Specialty exam verifies candidates’ proficiency in building, deploying, and fine-tuning data models, as well as in utilizing AWS services to scale up this process. In this intermediate-level course, learn how to study for the exam by reviewing the subject areas and how they relate to Data analysis on AWS.

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Course Schedule

Course Name Date Register

Course Overview

Duration 1D / 8 HRS
Modality ViLT/ Classroom
Check Dates CLICK HERE
Price 15,000.00