About This Course
At Karpov.Courses, I authored and delivered a comprehensive Data Engineering course designed to take students from foundational concepts to production-ready skills. The curriculum was built on real-world experience, not theoretical abstractions — every concept is paired with hands-on exercises using actual tools and realistic datasets.
Curriculum
Foundations — Relational databases, SQL (from basics to advanced window functions), data modeling principles, Linux command line, and version control. These fundamentals underpin everything that follows.
The Modern Data Stack — Hands-on work with Apache Airflow for orchestration, dbt for transformation, ClickHouse for analytical queries, and PostgreSQL for transactional workloads. Students build end-to-end pipelines that ingest, transform, and serve data.
Data Warehousing — Kimball’s dimensional modeling, Data Vault methodology, layered warehouse architecture (staging, core, data marts), and Slowly Changing Dimensions. Understanding when and why to apply each approach.
Data Quality & Testing — Data validation strategies, testing frameworks for data pipelines, monitoring and alerting, and building trust in data through systematic quality practices.
Career Development — What employers look for, how to build a portfolio, interview preparation, and navigating the data engineering job market.
Teaching Philosophy
Data engineering is learned by doing, not watching. Every lecture is accompanied by practical exercises, and the course culminates in a capstone project where students build a complete data platform. The goal is not just knowledge transfer — it’s building the confidence to architect and operate data systems independently.
