Data Warehousing on AWS

Last Updated: 08-03-2025

The Data Warehousing on AWS course is designed for data professionals who want to build and manage high-performance, scalable data warehouses on AWS. This hands-on course covers the key AWS services like Amazon Redshift, AWS Glue, and Amazon S3, empowering you to design and implement data warehousing solutions that can handle vast amounts of data with high speed and cost efficiency. You’ll learn how to create data models, perform data extraction, transformation, and loading (ETL), and optimize query performance to gain actionable insights for business decision-making. Whether you're migrating on-premises data warehouses to the cloud or building a new data warehouse architecture from scratch, this course equips you with the skills needed to effectively manage data storage and analytics in the AWS cloud.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
December 21^st - 28^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours)		10% Off $960 $864 Fast Filling! Hurry Up.
December 22^nd - 24^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours) Guaranteed-to-Run		10% Off $960 $864
January 03^rd - 10^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours)		20% Off $960 $768
January 05^th - 07^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours)		20% Off $960 $768
January 11^th - 18^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours)		20% Off $960 $768
January 12^th - 14^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours)		20% Off $960 $768
January 19^th - 26^th 06:00 AM - 10:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours)		20% Off $960 $768
January 26^th - 28^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 24 Hours) Guaranteed-to-Run		20% Off $960 $768

Course Prerequisites

Basic knowledge of database management systems and SQL.
Familiarity with AWS services like EC2, S3, and IAM is beneficial but not mandatory.
Experience with data integration or ETL processes is helpful but not required.
Recommended: Some experience in data modeling or working with business intelligence tools.

Learning Objectives

By the end of this course, you will be able to:

Understand the concepts and architecture behind data warehousing, and how they apply in the cloud.
Set up, configure, and manage Amazon Redshift, AWS’s fully managed data warehouse solution, for high-performance analytics.
Implement ETL processes with AWS Glue, enabling you to transform, clean, and load data into Amazon Redshift or other data sources.
Optimize Amazon Redshift for performance, including techniques like query optimization, indexing, and partitioning.
Use Amazon S3 to manage large datasets and integrate them into your data warehousing solution.
Design a data warehousing architecture that scales with business needs and ensures high availability and fault tolerance.
Integrate AWS tools such as Amazon Kinesis and AWS Data Pipeline to manage real-time and batch data processing.
Set up and manage data security and compliance using AWS best practices and services like AWS IAM, AWS KMS, and Amazon VPC.
Implement business intelligence (BI) solutions on AWS, and connect them with Amazon Redshift or other data sources to generate reports and dashboards.
Leverage Amazon QuickSight for data visualization and reporting directly from your data warehouse.

Target Audience

This course is ideal for:

Data engineers, data architects, and database administrators who want to learn how to implement data warehousing solutions on AWS.
Business intelligence professionals looking to improve their skills in designing cloud-based data warehouses.
IT professionals involved in managing, securing, and optimizing large datasets for reporting and analytics.
Professionals interested in leveraging AWS to create scalable, high-performance data lakes and data warehouses for enterprise-level analytics.

Course Modules

Day 1:

Module 1: Introduction to Data Warehousing
- Topics Covered:
  - Relational databases and data warehousing concepts.
  - The intersection of data warehousing and big data solutions.
  - Overview of data management in AWS.
- Hands-on Lab: Introduction to Amazon Redshift.
Module 2: Introduction to Amazon Redshift
- Topics Covered:
  - Conceptual overview of Amazon Redshift.
  - Real-world use cases of Redshift.
- Hands-on Lab: Launching an Amazon Redshift cluster.
Module 3: Launching Clusters
- Topics Covered:
  - Building and connecting to Redshift clusters.
  - Managing access and database security.
  - Loading data into Redshift.
- Hands-on Lab: Optimizing database schemas.

Day 2:

Module 4: Designing the Database Schema
- Topics Covered:
  - Schemas, data types, columnar compression, data distribution styles, and sorting methods.
Module 5: Identifying Data Sources
- Topics Covered:
  - Identifying and integrating various data sources with Redshift.
Module 6: Architecting the Data Warehouse
- Topics Covered:
  - Best practices for designing a scalable and efficient data warehouse architecture.

Day 3:

Module 7: Loading Data into the Data Warehouse
- Topics Covered:
  - Techniques and best practices for loading data into Redshift.
Module 8: Performance Optimization
- Topics Covered:
  - Strategies for optimizing query performance and managing workloads.
Module 9: Security and Compliance
- Topics Covered:
  - Implementing security measures, access controls, and ensuring compliance within Redshift.
Module 10: Monitoring and Maintenance
- Topics Covered:
  - Tools and techniques for monitoring and maintaining Redshift clusters.
Module 11: Advanced Features
- Topics Covered:
  - Exploring advanced Redshift features such as Spectrum, Concurrency Scaling, and ML integration.
Module 12: Course Wrap-up
- Activities:
  - Review of key concepts.
  - Q&A session.
  - Final assessment.

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