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FRCAWS-26AWS

Data Warehousing on AWS

Duration · 3 daysVirtual + In-PersonInstructor-Led

$2,050

Course Description

This three-day instructor-led course teaches participants how to design, build, optimize, and operate modern cloud-based data warehousing solutions on Amazon Web Services. Learners gain practical experience implementing scalable analytics platforms, integrating data pipelines, managing data storage, optimizing query performance, and securing enterprise data warehouse environments using AWS services and best practices.

The course focuses on modern data warehousing architectures, analytics workflows, operational excellence, and hands-on implementation using AWS analytics services.

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Audience Profile

This course is intended for:

  • Data Engineers
  • Data Architects
  • Database Administrators
  • BI Developers
  • Analytics Engineers
  • Cloud Engineers
  • Solutions Architects
  • ETL Developers

Prerequisites

Before enrolling, you should have:

  • Basic understanding of relational databases and SQL
  • Familiarity with data warehouse concepts
  • General understanding of AWS core services
  • Experience with data analytics or ETL processes is recommended

— What You'll Learn —

Learning Objectives

In this course, you will learn to:

  • 1Design modern data warehouse architectures on AWS
  • 2Build scalable analytics solutions using Amazon Redshift
  • 3Implement ETL and ELT data processing workflows
  • 4Integrate data lakes and data warehouses
  • 5Optimize analytical query performance
  • 6Secure and govern enterprise data warehouse environments
  • 7Automate warehouse operations and data pipelines
  • 8Monitor and troubleshoot analytics workloads
  • 9Apply operational best practices for cloud data warehousing

— Day-by-Day —

Course Outline

Day 1 — Foundations of Data Warehousing on AWS

Module 1

Introduction to Data Warehousing

Topics

  • Evolution of data warehousing
  • OLTP vs. OLAP systems
  • Data warehouse architecture concepts
  • Modern analytics platforms
  • Data lake vs. data warehouse
  • AWS analytics ecosystem overview
  • Common analytics and reporting use cases
  • AWS Services Covered
  • Amazon Redshift
  • Amazon S3
  • AWS Glue
  • Amazon Athena

Hands-On Lab

  • Explore AWS analytics services
  • Configure foundational warehouse resources
  • Create analytics storage environments

Module 2

Designing Data Warehouse Architectures

Topics

  • Data warehouse design principles
  • Star and snowflake schemas
  • Fact and dimension tables
  • Data modeling strategies
  • Partitioning and distribution concepts
  • Multi-tier warehouse architectures
  • Scalability and high availability design
  • AWS Services Covered
  • Amazon Redshift
  • Amazon Redshift Serverless

Hands-On Lab

  • Design analytical schemas
  • Create tables and warehouse structures
  • Configure Redshift clusters and workgroups

Module 3

Data Ingestion and ETL Workflows

Topics

  • Data ingestion patterns
  • Batch and streaming ingestion
  • ETL vs. ELT architectures
  • Data cleansing and transformation
  • Managing structured and semi-structured data
  • Data quality validation
  • Workflow orchestration concepts
  • AWS Services Covered
  • AWS Glue
  • Amazon Kinesis
  • AWS Database Migration Service (DMS)
  • Amazon S3

Hands-On Lab

  • Build ETL pipelines using AWS Glue
  • Load datasets into Amazon Redshift
  • Validate transformed datasets

Module 4

Querying and Analyzing Warehouse Data

Topics

  • SQL analytics fundamentals
  • Query execution concepts
  • Aggregations and analytical functions
  • Optimizing analytical queries
  • Working with large datasets
  • Federated query concepts
  • Data exploration techniques
  • AWS Services Covered
  • Amazon Redshift
  • Amazon Athena

Hands-On Lab

  • Execute analytical SQL queries
  • Analyze query execution performance
  • Explore warehouse datasets

Day 2 — Advanced Analytics and Performance Optimization

Module 5

Advanced Amazon Redshift Features

Topics

  • Redshift architecture deep dive
  • Compute and storage separation
  • Redshift Spectrum concepts
  • Materialized views
  • Data sharing capabilities
  • Concurrency scaling
  • Workload management
  • AWS Services Covered
  • Amazon Redshift
  • Amazon Redshift Spectrum
  • Amazon S3

Hands-On Lab

  • Query external datasets with Redshift Spectrum
  • Configure workload management settings
  • Implement materialized views

Module 6

Data Lakes and Lakehouse Integration

Topics

  • Data lake architecture concepts
  • Integrating data lakes with warehouses
  • Lakehouse architecture overview
  • Managing raw and curated datasets
  • Metadata management
  • Unified analytics strategies
  • Hybrid analytics architectures
  • AWS Services Covered
  • Amazon S3
  • AWS Lake Formation
  • AWS Glue Data Catalog
  • Amazon Redshift

Hands-On Lab

  • Integrate Redshift with a data lake
  • Configure metadata catalogs
  • Query shared datasets across platforms

Module 7

Performance Tuning and Cost Optimization

Topics

  • Query optimization strategies
  • Compression and encoding
  • Distribution styles and sort keys
  • Caching mechanisms
  • Resource utilization analysis
  • Cost management best practices
  • Capacity planning concepts
  • AWS Services Covered
  • Amazon Redshift
  • Amazon CloudWatch

Hands-On Lab

  • Optimize query performance
  • Tune warehouse configurations
  • Monitor resource utilization

Module 8

Business Intelligence and Visualization

Topics

  • Reporting and dashboard concepts
  • Data visualization best practices
  • Connecting BI tools to data warehouses
  • Interactive analytics workflows
  • Self-service analytics concepts
  • Operational reporting strategies
  • AWS Services Covered
  • Amazon QuickSight
  • Amazon Redshift
  • Amazon Athena

Hands-On Lab

  • Build dashboards using QuickSight
  • Create visual analytics reports
  • Connect BI tools to warehouse datasets

Day 3 — Security, Automation, and Operational Excellence

Module 9

Security and Governance for Data Warehouses

Topics

  • Identity and access management
  • Data encryption strategies
  • Role-based access control
  • Data governance frameworks
  • Compliance and auditing
  • Secure data sharing
  • Managing sensitive data
  • AWS Services Covered
  • AWS IAM
  • AWS KMS
  • AWS Lake Formation
  • AWS CloudTrail

Hands-On Lab

  • Configure warehouse security policies
  • Implement encryption and auditing
  • Manage user and role permissions

Module 10

Monitoring and Troubleshooting Analytics Workloads

Topics

  • Monitoring warehouse performance
  • Logging and observability
  • Query performance diagnostics
  • Troubleshooting ETL workflows
  • Managing failures and retries
  • Alerting and notifications
  • Operational best practices
  • AWS Services Covered
  • Amazon CloudWatch
  • AWS CloudTrail
  • Amazon Redshift monitoring tools

Hands-On Lab

  • Monitor analytics workloads
  • Analyze logs and metrics
  • Troubleshoot query performance issues

Module 11

Automating Data Warehouse Operations

Topics

  • Workflow orchestration concepts
  • Scheduling ETL pipelines
  • Infrastructure as Code fundamentals
  • Automating warehouse deployments
  • CI/CD for analytics platforms
  • Managing repeatable environments
  • Operational automation strategies
  • AWS Services Covered
  • AWS Step Functions
  • AWS Lambda
  • AWS CloudFormation
  • AWS CodePipeline

Hands-On Lab

  • Automate warehouse provisioning
  • Build scheduled ETL workflows
  • Implement deployment automation

Module 12

End-to-End Data Warehousing Capstone Project

Topics

  • Designing enterprise analytics solutions
  • Building scalable data warehouse pipelines
  • Integrating ingestion, transformation, and reporting
  • Securing analytics environments
  • Operationalizing cloud data warehouses
  • Best practices for production systems
  • Capstone Hands-On Lab
  • Participants build a complete cloud data warehousing solution that includes:
  • Data ingestion
  • ETL processing
  • Data lake integration
  • Warehouse optimization
  • BI dashboard creation
  • Security implementation
  • Monitoring and automation
  • Course Wrap-Up
  • Final Review
  • Review of modern data warehousing architectures
  • AWS analytics best practices
  • Common implementation patterns and challenges
  • Skills Validation
  • Practical lab assessments
  • Scenario-based discussions
  • Capstone review and presentation
  • Next Steps
  • Advanced analytics learning paths
  • AWS certification recommendations
  • Enterprise implementation guidance
  • AWS Services Covered
  • Amazon Redshift
  • Amazon Redshift Serverless
  • Amazon Redshift Spectrum
  • Amazon S3
  • AWS Glue
  • AWS Glue Data Catalog
  • AWS Lake Formation
  • Amazon Athena
  • Amazon QuickSight
  • Amazon Kinesis
  • AWS Database Migration Service (DMS)
  • AWS IAM
  • AWS KMS
  • Amazon CloudWatch
  • AWS CloudTrail
  • AWS Lambda
  • AWS Step Functions
  • AWS CloudFormation
  • AWS CodePipeline

Note: Course outlines are provided as a general guide. Content, pacing, labs, and instructional emphasis may vary based on instructor expertise, student experience levels, and customer-specific learning objectives.

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