Automated Data Pipelines with Spark, dbt, and Airflow

In today's data-driven world, organizations rely on robust pipelines to process massive datasets efficiently. This text-based course guides you through the foundational concepts of modern data engineering, showing you how to orchestrate complex workflows from scratch. You will transition from understanding basic data terms to confidently designing automated pipelines that scale. What you'll learn: - Understand the core architecture of Spark, dbt, and Airflow and how they work together. - Build structured data transformation models using dbt to clean and organize raw data. - Configure Airflow DAGs to schedule, automate, and monitor your data workflows. - Apply modern data quality tests and schema validation to ensure pipeline reliability. - Integrate Spark processing jobs seamlessly within your orchestrated Airflow pipelines. - Practice pipeline design principles through step-by-step written exercises and code walkthroughs. You will begin by mastering essential terminology and architectural patterns. From there, you will explore how to write Spark transformations, structure dbt projects, and tie everything together using Airflow's powerful orchestration engine. This course is designed for aspiring data engineers, analysts, and developers who want a clear, beginner-friendly introduction to pipeline automation without any complex prerequisites. Start reading today to build your practical data engineering foundation.