Cloud Data Platforms: AWS, Azure, and GCP for Analytics

Data Analytics and Business Intelligence October 25, 2025
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Introduction

This intensive course provides a comprehensive tour of the leading hyperscale cloud data platforms: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Participants will learn the core infrastructure, services, and tools necessary to build, secure, and manage scalable data pipelines and analytical workloads in a multi-cloud environment. We will compare architecture patterns, key database services, and data warehousing solutions specific to each vendor. The program is essential for data professionals seeking to leverage the immense power and flexibility of cloud computing for modern analytics.

Objectives

Upon completion of this course, participants will be able to:

  • Differentiate the core data warehousing and lake house architectures of AWS, Azure, and GCP.
  • Design and provision scalable data storage solutions using S3, Azure Blob Storage, and Google Cloud Storage.
  • Implement serverless data processing pipelines using services like AWS Glue, Azure Data Factory, and Google Cloud Dataflow.
  • Manage and query large datasets using cloud-native data warehouses such as Amazon Redshift, Azure Synapse, and Google BigQuery.
  • Securely manage access, networking, and data encryption across all three major cloud providers.
  • Select the appropriate cloud service for specific analytical tasks, maximizing performance and cost efficiency.
  • Understand and apply fundamental DevOps principles to cloud data deployments.
  • Optimize cloud data infrastructure for cost monitoring and expenditure control.

Target Audience

  • Data Engineers and Architects
  • Cloud Solutions Architects
  • Business Intelligence (BI) Developers
  • IT Infrastructure Managers
  • Database Administrators (DBAs) migrating to the cloud
  • Advanced Data Analysts

Methodology

The methodology relies heavily on hands-on labs and comparative exercises, reflecting real-world migration and multi-cloud scenarios. Participants will engage in **group activities** focused on designing a hypothetical data lake architecture for a mid-sized enterprise, choosing between AWS, Azure, and GCP components. **Mini-case studies** will involve troubleshooting common performance bottlenecks in Redshift vs. BigQuery. **Individual exercises** will guide attendees through provisioning secure storage and running basic ETL jobs in the cloud console of each platform. **Syndicate discussions** will cover the trade-offs of vendor lock-in versus specialization.

Personal Impact

  • Gain immediate proficiency in deploying services across the three most dominant cloud providers.
  • Elevate career profile by mastering multi-cloud data architecture and deployment principles.
  • Achieve higher confidence in proposing and defending cloud technology decisions to stakeholders.
  • Streamline personal workflow by automating data infrastructure using cloud-native tools.
  • Improve understanding of cloud cost models to manage budgets effectively.
  • Secure better job prospects in high-demand DataOps and Cloud Engineering roles.

Organizational Impact

  • Accelerate cloud adoption and migration initiatives with a skilled, multi-platform team.
  • Reduce vendor lock-in risk by enabling architecture comparison and service selection flexibility.
  • Improve data governance and security posture by implementing unified best practices across cloud environments.
  • Optimize cloud expenditure by ensuring teams select the most cost-effective services for specific analytical tasks.
  • Increase the resilience and scalability of data pipelines to handle explosive data growth.
  • Standardize organizational terminology and best practices for modern data infrastructure.

Course Outline

UNIT 1: Cloud Data Architecture Fundamentals

Core Concepts and Comparison
  • Understanding Public, Private, and Hybrid Cloud Models
  • Defining Data Lake, Data Warehouse, and Lakehouse Concepts in the Cloud
  • Key Architectural Differences: AWS vs. Azure vs. GCP
  • Cloud Deployment and Resource Management Models
  • Identity and Access Management (IAM) across platforms

UNIT 2: Cloud Storage and Data Lakes

AWS, Azure, and GCP Storage Services
  • AWS S3: Buckets, Classes, and Lifecycle Management
  • Azure Storage: Blob Storage, Data Lake Storage Gen2, and Tiers
  • GCP Storage: Buckets, Storage Classes, and Regionality
  • Designing Data Lake Folders and Governance Structures
  • Securing Data at Rest: Encryption Strategies and Best Practices

UNIT 3: Cloud Data Warehousing Solutions

Modern Massively Parallel Processing (MPP) Warehouses
  • Amazon Redshift: Cluster Management, Scaling, and Workload Management
  • Azure Synapse Analytics: Dedicated SQL Pools and Serverless Options
  • Google BigQuery: Architecture, Query Optimization, and Billing
  • Comparing Query Performance and Cost Models
  • Implementing ETL/ELT Directly within Cloud Data Warehouses

UNIT 4: Serverless Data Processing and ETL/ELT

Managed Services for Data Transformation
  • AWS Glue: Data Catalog, Crawlers, and Serverless ETL Jobs
  • Azure Data Factory and Azure Databricks Integration
  • Google Cloud Dataflow and Cloud Composer for Orchestration
  • Introduction to Cloud-Native Stream Processing (Kinesis, Event Hubs, Pub/Sub)
  • Best Practices for Orchestrating Multi-Step Data Pipelines

UNIT 5: Advanced Security and Governance

Networking, Compliance, and Cost Management
  • Virtual Networking (VPC, VNet, VPC Network) Configuration
  • Implementing Private Endpoints and Service Connections
  • Auditing and Logging Services (CloudTrail, Azure Monitor, Cloud Logging)
  • Data Masking and Tokenization Techniques
  • Strategies for Cost Monitoring, Budget Alerts, and Optimization
  • Regulatory Compliance and Data Sovereignty Considerations

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