Foundational Data Engineering: Building Automated ELT Pipelines

Data is the lifeblood of modern business, but raw data is useless without proper infrastructure to move and process it efficiently. This course provides a solid, practical foundation in Data Engineering, enabling you to design, build, and maintain robust automated data pipelines necessary for modern data analysis and reporting. What you'll learn: * Understand the fundamental concepts of Data Warehousing (DWH) and Data Lakes, including common architectural patterns. * Configure and containerize local data infrastructure using Docker for reproducible development environments. * Apply Python and PySpark for efficient batch processing and large-scale data transformations. * Design and orchestrate complex workflows using Apache Airflow for scheduling and monitoring ETL/ELT jobs. * Implement data quality checks and basic data observability patterns within your pipelines. * Practice building modular and testable ELT workflows using modern data transformation frameworks like DBT. The course begins with foundational database concepts and data modeling principles before moving into practical pipeline construction using containerized environments. You will progress through batch processing, stream ingestion basics, and workflow orchestration. This course is designed for absolute beginners interested in Data Engineering, as well as data analysts or developers seeking to transition into building data infrastructure. No prior data engineering experience is required. Start building the infrastructure that powers data-driven decisions today.