Data Engineering Fundamentals: Pipelines, SQL, and Orchestration

Data Engineering is the backbone of modern data science and analytics, yet mastering the fundamentals of data flow and infrastructure can be complex. This course provides a structured foundation in the essential tools and architectural patterns required to design, build, and maintain reliable data ingestion and transformation pipelines. This course focuses on practical, foundational knowledge, preparing you to tackle entry-level Data Engineer challenges and technical reviews. What you'll learn: * Understand data modeling principles, including dimensional modeling, and manage relational data using PostgreSQL. * Practice writing performant SQL queries and applying Python for complex data processing and transformation logic. * Design reliable batch and streaming data architectures using concepts from Apache Kafka and distributed storage systems. * Configure and monitor reproducible data workflows using the fundamental features of Apache Airflow. * Apply basic data warehousing patterns, including concepts related to columnar stores like ClickHouse and analytical query optimization. * Learn essential best practices for structuring, testing, and deploying data engineering jobs efficiently. The course begins with core terminology and data modeling theory, progresses through practical application using Python and SQL for data manipulation, and concludes with lessons on structuring complex workflows and integrating various storage and streaming technologies. This course is designed specifically for absolute beginners interested in becoming Data Engineers. No prior experience with data infrastructure or complex coding is required, only basic programming knowledge. Start building your foundational data engineering knowledge today.