Data Engineering Fundamentals Training Course

This course equips participants with foundational skills in data engineering, focusing on how data is collected, processed, stored, and made available for analysis. It covers data pipelines, databases, ETL processes, cloud data platforms, and data architecture. Participants will learn how to build reliable and scalable data systems that support analytics, business intelligence, and data science applications.

Target Groups

• Aspiring data engineers
• Data analysts and data scientists
• Software developers and IT professionals
• Business intelligence developers
• System administrators
• Database administrators
• Cloud computing enthusiasts
• Researchers and data professionals
• Students in computer science and IT
• Anyone interested in data infrastructure and systems

Course Objectives

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

• Understand core concepts of data engineering
• Design basic data pipelines and workflows
• Work with relational and non-relational databases
• Apply ETL (Extract, Transform, Load) processes
• Understand data storage and processing systems
• Build and manage data pipelines
• Use cloud-based data engineering tools
• Ensure data quality and reliability in systems
• Support data analytics and machine learning systems
• Understand data architecture principles

Course Modules

Module 1: Introduction to Data Engineering

• Definition and role of data engineering
• Data engineering vs data science
• Data lifecycle overview
• Importance of data infrastructure
• Real-world applications

Module 2: Data Architecture Fundamentals

• Data architecture concepts
• Data warehouses and data lakes
• Batch vs real-time architecture
• Data flow design principles
• Scalable system design basics

Module 3: Databases and Data Storage

• Relational databases (SQL)
• NoSQL databases (document, key-value, graph)
• Data modeling concepts
• Indexing and query optimization
• Data storage best practices

Module 4: ETL (Extract, Transform, Load) Processes

• Understanding ETL pipelines
• Data extraction techniques
• Data transformation processes
• Data loading strategies
• ETL tools and frameworks

Module 5: Data Pipelines and Workflow Orchestration

• Designing data pipelines
• Batch processing systems
• Real-time data streaming basics
• Workflow automation tools
• Pipeline monitoring and troubleshooting

Module 6: Big Data Processing Systems

• Introduction to big data systems
• Hadoop ecosystem overview
• Apache Spark fundamentals
• Distributed computing concepts
• Scalability and performance considerations

Module 7: Cloud Data Engineering

• Introduction to cloud platforms (AWS, Azure, GCP)
• Cloud storage systems
• Managed data services
• Serverless data processing
• Cloud data security basics

Module 8: Data Quality and Governance

• Data validation techniques
• Data cleaning and standardization
• Data governance frameworks
• Metadata management
• Ensuring data reliability

Module 9: Data Integration and APIs

• Data integration techniques
• API-based data exchange
• Data ingestion methods
• System interoperability
• Building connected data ecosystems

Module 10: Capstone Project and Case Studies

• End-to-end data pipeline project
• Database design and implementation exercise
• ETL pipeline development task
• Real-world data engineering case studies
• Emerging trends in data engineering, including real-time streaming systems, AI-powered data pipelines, cloud-native architectures, data mesh, and automated data orchestration platforms