Cloud Data Engineering Training Course

This course equips participants with practical skills to design, build, and manage modern data systems using cloud platforms. It focuses on cloud data architecture, scalable storage, ETL/ELT pipelines, data lakes, data warehouses, and real-time data processing. Participants will learn how to use cloud technologies to support analytics, machine learning, and enterprise data solutions efficiently and securely.

Target Groups

• Data engineers and aspiring data engineers
• Cloud computing professionals
• Software developers and IT engineers
• Data analysts and data scientists
• Business intelligence developers
• Database administrators
• DevOps and system engineers
• Government and NGO data professionals
• Students in computer science and IT
• Anyone interested in cloud-based data systems

Course Objectives

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

• Understand cloud data engineering concepts and architecture
• Build scalable data pipelines in cloud environments
• Work with cloud data storage and processing services
• Design data lakes and data warehouses
• Implement ETL/ELT workflows in the cloud
• Manage real-time and batch data processing systems
• Ensure data security and governance in cloud platforms
• Optimize performance and cost of cloud data systems
• Integrate data engineering with analytics and AI systems
• Deploy and monitor cloud-based data solutions

Course Modules

Module 1: Introduction to Cloud Data Engineering

• Overview of cloud computing
• Role of cloud in modern data engineering
• Data engineering lifecycle in the cloud
• Benefits of cloud-based data systems
• Key cloud service models (IaaS, PaaS, SaaS)

Module 2: Cloud Data Architecture

• Cloud data architecture principles
• Data lakes vs data warehouses
• Batch vs real-time processing systems
• Scalable and distributed systems design
• Multi-cloud and hybrid architectures

Module 3: Cloud Storage Systems

• Object storage (Amazon S3, Azure Blob, Google Cloud Storage)
• File and block storage concepts
• Data partitioning and organization
• Storage optimization techniques
• Data lifecycle management in the cloud

Module 4: ETL and ELT in Cloud Environments

• ETL vs ELT approaches
• Building cloud data pipelines
• Data ingestion methods
• Transformation and processing tools
• Workflow orchestration basics

Module 5: Data Warehousing Solutions

• Cloud data warehouses (BigQuery, Redshift, Snowflake)
• Data modeling for analytics
• Query optimization techniques
• Data aggregation and reporting
• Integration with BI tools

Module 6: Real-Time Data Processing

• Streaming data concepts
• Event-driven architectures
• Real-time analytics pipelines
• Message queues and streaming services
• Use cases in business and industry

Module 7: Data Security and Governance in the Cloud

• Identity and access management (IAM)
• Encryption and data protection
• Compliance and governance frameworks
• Monitoring and auditing cloud data systems
• Risk management in cloud environments

Module 8: Cloud Data Tools and Platforms

• AWS data services overview
• Microsoft Azure data services overview
• Google Cloud Platform data tools
• Open-source cloud data tools
• Choosing the right platform for projects

Module 9: Performance Optimization and Cost Management

• Query optimization techniques
• Resource scaling strategies
• Cost monitoring and control
• Performance tuning of pipelines
• Efficient cloud architecture design

Module 10: Capstone Project and Case Studies

• End-to-end cloud data pipeline project
• Data lake and warehouse design exercise
• Real-time data streaming case study
• Cloud analytics dashboard integration
• Emerging trends in cloud data engineering, including serverless data pipelines, AI-driven cloud optimization, data mesh architecture, and fully automated cloud-native data platforms