Data Warehousing on AWS

You will learn strategies and best practices for designing a cloud based data warehousing solution using Amazon Redshift, the petabyte-scale data warehouse in AWS. 

The learner will also learn how to collect, store, and prepare data for the data warehouse by using AWS services such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3. Additionally, this course demonstrates how to use Amazon QuickSight to perform analysis on your data. 

This course also helps you prepare for the AWS Certified Data Analytics exam – speciality.

The program is intended for Database Architects, Database Administrators, Database Developers, Data Analysts, Data Scientists.

3 Days / 24 Hours

Live Class

Certificate on completion


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

  • Talk about the fundamental principles of data warehousing and how big data solutions and data warehousing connect.
  • Launch an Amazon Redshift cluster and construct a cloud data warehouse using its components, features, and capabilities.
  • Contribute to the data warehousing solution by leveraging other AWS data and analytical services, such as Amazon DynamoDB, Amazon EMR, Amazon Kinesis, and Amazon S3.
  • The data warehouse’s design
  • Recognize performance problems, improve queries, and tune the database for improved performance.
  • To analyze data directly from an Amazon S3 bucket, utilize Amazon Redshift Spectrum.
  • Perform data analysis and visualization tasks against the data warehouse using Amazon QuickSight.


We recommend that attendees of this course have:

Intended Audience

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

  • Database Architects
  • Database Administrators
  • Database Developers
  • Data Analysts
  • Data Scientists


The AWS data warehousing course includes the following activities: 

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

Module 1: Introduction to Data Warehousing

  • Relational databases
  • Data warehousing concepts
  • The intersection of data warehousing and big data
  • Overview of data management in AWS
  • Hands-on lab 1: Introduction to Amazon Redshift

Module 2: Introduction to Amazon Redshift

  • Conceptual overview
  • Real-world use cases
  • Hands-on lab 2: Launching an Amazon Redshift cluster

Module 3: Launching clusters

  • Building the cluster
  • Connecting to the cluster
  • Controlling access
  • Database security
  • Load data
  • Hands-on lab 3: Optimizing database schemas

Module 4: Designing the database schema

  • Schemas and data types
  • Columnar compression
  • Data distribution styles
  • Data sorting methods

Module 5: Identifying data sources

  • Data sources overview
  • Amazon S3
  • Amazon DynamoDB
  • Amazon EMR
  • Amazon Kinesis Data Firehose
  • AWS Lambda Database Loader for Amazon Redshift
  • Hands-on lab 4: Loading real-time data into an Amazon Redshift database

Module 6: Loading data

  • Preparing Data
  • Loading data using COPY
  • Maintaining tables
  • Concurrent write operations
  • Troubleshooting load issues
  • Hands-on lab 5: Loading data with the COPY command

Module 7: Writing queries and tuning for performance

  • Amazon Redshift SQL
  • User-Defined Functions (UDFs)
  • Factors that affect query performance
  • The EXPLAIN command and query plans
  • Workload Management (WLM)
  • Hands-on lab 6: Configuring workload management

Module 8: Amazon Redshift Spectrum

  • Amazon Redshift Spectrum
  • Configuring data for Amazon Redshift Spectrum
  • Amazon Redshift Spectrum Queries
  • Hands-on lab 7: Using Amazon Redshift Spectrum

Module 9: Maintaining clusters

  • Audit logging
  • Performance monitoring
  • Events and notifications
  • Lab 8: Auditing and monitoring clusters
  • Resizing clusters
  • Backing up and restoring clusters
  • Resource tagging and limits and constraints
  • Hands-on lab 9: Backing up, restoring and resizing clusters

Module 10: Analyzing and visualizing data

  • Power of visualizations
  • Building dashboards
  • Amazon QuickSight editions and features

Talk to a Learning Advisor

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Exam Readiness

Exam Voucher

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.


AWS Certified Data Analytics - Specialty

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.


Yes, we are an AWS Advanced Tier Training Partner

Anyone who wants to start a profession in AWS cloud is fit to enroll in this course. No prior knowledge of coding or other technical skills is required.

To enroll in this course, choose the starting date and make an online payment. Once your payment is confirmed, our team will reach out to you.

You may reach out at the contact number listed on our official website or write to us at

Wire Transfer, Credit Card, Debit Card, UPI & Purchase Order

There is no minimum number of candidates required, we are happy to train 1 to 1 should you wish. With regard to the maximum number, we can accommodate 30 learners in one batch.

1. Training delivered by an Amazon Authorised Instructor
2. AWS Content E-Kit
3. Hands-on labs- 30 days
4. Class attendance certificate

You will get the access to course content & lab on first day of your training session.

The course completion certificate will be issued to your email id within 2 weeks of completing your course.

A one-day course could be delivered over two half day sessions (4 hours a day), or a three-day course could be delivered over five days (4 hours a day).


Data Warehousing on AWS

3 Days / 24 Hours

Live Class

Certificate on completion


(Taxes Extra)

Choose a date