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.
ETL Testing Basics for Databases
Recommended experience
What you'll learn
Explain the core concepts, architecture, and role of ETL within modern data ecosystems.
Design and implement complete ETL workflows using Apache NiFi, applying extract, transform, and load functions on structured datasets.
Evaluate and optimize ETL pipelines for performance, reliability, and integration with AI or analytics systems.
Details to know
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February 2026
1 assignment
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There are 3 modules in this course
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.
What's included
4 videos2 readings1 peer review
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.
What's included
3 videos1 reading1 peer review
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.
What's included
4 videos1 reading1 assignment2 peer reviews
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