Course Title: Data Engineering and ETL (Extract, Transform, Load)

Course Description: The Data Engineering and ETL course is designed to provide a comprehensive education in the field of data engineering, with a focus on Extract, Transform, Load (ETL) processes. Students will learn how to design, build, and manage data pipelines for collecting, processing, and transforming data for analysis and reporting. This course aims to equip participants with the skills and knowledge required to work as data engineers in the ever-evolving data-driven industry.

Course Outline:

Module 1: Introduction to Data Engineering

  • What is Data Engineering?
  • Role of a Data Engineer
  • Data Engineering vs. Data Science
  • Key Concepts: Data Pipeline, Data Architecture
  • Data Engineering Tools and Frameworks

Module 2: Data Sources and Collection

  • Data Sources (Databases, APIs, Logs)
  • Data Ingestion Methods
  • Data Extraction Techniques
  • Real-Time Data Collection
  • Data Quality and Data Governance

Module 3: Data Transformation and Cleaning

  • Data Transformation Principles
  • Data Cleaning and Preprocessing
  • Handling Missing Data
  • Data Enrichment and Augmentation
  • Data Deduplication

Module 4: ETL Concepts and Process

  • Understanding ETL (Extract, Transform, Load)
  • ETL Workflow and Pipeline
  • ETL Data Flow Diagram
  • ETL Tools and Platforms
  • Error Handling and Logging

Module 5: ETL Development and Design

  • ETL Design Best Practices
  • ETL Architecture Patterns
  • ETL Data Mapping and Data Dictionary
  • Managing ETL Workflows
  • Version Control for ETL

Module 6: Data Integration and Transformation Tools

  • ETL Tools (e.g., Apache Nifi, Talend, Informatica)
  • Data Transformation Languages (e.g., SQL, Python)
  • Scripting and Data Manipulation
  • Using ETL Libraries and Packages
  • Hands-On ETL Development

Module 7: ETL in the Cloud

  • Cloud ETL Services (e.g., AWS Glue, Azure Data Factory)
  • Serverless ETL with AWS Lambda
  • Managing ETL Workflows in the Cloud
  • Scalability and Elasticity
  • Data Security and Compliance

Module 8: ETL Monitoring and Optimization

  • Monitoring ETL Jobs
  • Performance Tuning and Optimization
  • ETL Job Scheduling
  • Error Handling and Exception Handling
  • Scalability and Auto-Scaling

Module 9: Data Warehousing and Storage

  • Data Warehousing Concepts
  • Data Storage Solutions (e.g., AWS S3, Google Cloud Storage)
  • Data Lake Architecture
  • Data Partitioning and Clustering
  • Data Compression and Serialization

Module 10: ETL and Big Data

  • ETL for Big Data (Hadoop, Spark)
  • Real-Time ETL with Streaming Data
  • Handling Massive Data Volumes
  • ETL for NoSQL Databases
  • Data Pipelines for Machine Learning

Module 11: Data Security and Compliance

  • Data Privacy and Protection
  • Data Encryption in Transit and at Rest
  • Compliance Regulations (e.g., GDPR, HIPAA)
  • Auditing and Access Control
  • ETL Best Practices for Data Security

Module 12: ETL Case Studies and Real-World Projects

  • Industry-Specific ETL Examples
  • Building ETL Pipelines from Scratch
  • ETL Challenges and Problem Solving
  • Capstone ETL Project
  • Presentation of ETL Project Findings

Module 13: Emerging Technologies and Trends

  • Data Engineering with IoT Data
  • Data Engineering for AI and Machine Learning
  • Real-Time ETL and Stream Processing
  • ETL Automation and DevOps
  • The Future of Data Engineering

Course Duration: The course is typically designed to be completed in 12-16 weeks, with a recommended pace of 6-8 hours of study per week. The Capstone ETL Project may require additional time for completion.

Please note that this outline is a general guideline, and the specific content and order of topics may vary depending on the instructor and the learning resources used. This course should provide a strong foundation in data engineering and ETL processes, which are essential for managing and processing data in modern organizations.