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Data Engineering Notes

Welcome to Kenny Gorman's collection of data engineering articles, tutorials, and working examples. This repository contains technical content focused on data architecture, database engineering, streaming systems, and AI-driven data workflows.

About

This repository serves as a comprehensive resource for data engineering professionals, featuring:

  • In-depth technical articles on data pipelines, databases, and streaming systems
  • Working code examples with extensive documentation and comments
  • Real-world case studies from production data engineering environments
  • Best practices for building scalable data infrastructure

Topics Covered

  • Data Architecture: System design patterns, data modeling, and infrastructure planning
  • Database Engineering: Performance optimization, schema design, and database administration
  • Streaming Data: Real-time data processing, event-driven architectures, and stream analytics
  • Stream Processing: Apache Kafka, Apache Flink, and other streaming technologies
  • AI and Data Systems: ML pipelines, data preparation for AI, and AI-driven data workflows

Content Structure

Each article includes:

  • Comprehensive explanations with practical context
  • Complete, tested code examples
  • Diagrams and visualizations where helpful
  • Links to related concepts and further reading

Author

Kenny Gorman is a seasoned data engineering professional with expertise in:

  • Database Engineering and Performance Optimization
  • Streaming Data Architecture and Real-time Processing
  • AI/ML Pipeline Development
  • Large-scale Data Infrastructure

Contact: kgorman@me.com

Contributing

This repository is primarily authored by Kenny Gorman. If you find errors or have suggestions for improvements, please feel free to open an issue.


This repository serves as the canonical source for Kenny Gorman's technical content, with articles also published on Medium, LinkedIn, and other platforms.

About

Data Engineering Notes — Working notes, full-text articles, and runnable examples on data pipelines, databases, streaming systems, and AI-driven data workflows.

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