This course introduces the principles and practice of Extract-Transform-Load (ETL) systems—the backbone of modern data-driven operations. Learners begin by exploring database fundamentals, including schemas, tables, and source structures, and then examine how ETL pipelines move, clean, and shape data for reliable use across analytics and AI workflows. Building on this foundation, the course provides hands-on experience using Apache NiFi to construct visual, end-to-end ETL flows, guiding learners through essential tasks such as extracting raw data from multiple sources, applying meaningful transformations, enriching records, standardizing formats, and loading clean results into destination systems. Each module builds practical fluency: from understanding core ETL concepts, designing extract–transform–load pipelines, to applying automation, optimization, and AI-supported improvements.

This course is designed for beginners with an interest in data engineering and database management. Whether you're a new data analyst, aspiring data engineer, or anyone looking to understand the role of ETL in modern data workflows, this course will equip you with the knowledge and skills needed to build effective ETL systems. No prior experience with ETL, programming, or advanced data science concepts is required. A basic understanding of databases, CSV files, and general data concepts will be helpful but is not mandatory. By the end of the course, learners will design and optimize a complete ETL workflow and understand how modern teams integrate these pipelines into analytics platforms, operational dashboards, and machine-learning feature pipelines.