Overview

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.

Syllabus

ETL Testing Basics for Databases

This module introduces learners to the foundations of ETL by explaining why reliable data movement begins with understanding databases, schemas, and source structures. Through a guided Apache NiFi walkthrough, learners learn how to open the workspace, connect to a database, inspect tables, and preview real data. The module builds a consistent, team-wide approach to exploring source data—laying the groundwork for accurate extraction, transformation, and loading in later modules.

Hands-on with Apache NiFi: Extract, Transform, and Load

This module guides learners through the full ETL workflow by breaking it into its core stages—extract, transform, and load—and demonstrating how each step ensures data reliability. Through hands-on activities in Apache NiFi, learners build a simple end-to-end pipeline that pulls raw data, cleans and enriches it, and loads it into a structured destination. The module emphasizes consistency, automation, and validation so learners can design repeatable pipelines that support accurate analytics and downstream systems.

ETL in the Real World

This module focuses on real-world ETL challenges, guiding learners through the process of identifying and diagnosing performance issues that arise as data volumes increase. It introduces practical optimization strategies—including tuning concurrency, improving transformation efficiency, and refining data flow design—to strengthen pipeline reliability and throughput. Learners also explore how AI can support smarter monitoring and optimization, preparing them to manage and enhance ETL workflows in production environments.