Introduction to Data Engineering is your essential guide to understanding the foundations of modern data systems, pipelines, and architectures that power today’s data-driven world. This course provides the knowledge and skills needed to design, build, and manage robust data infrastructures that help organizations make intelligent, insight-driven decisions. Whether you’re a beginner or an aspiring data professional, this program delivers practical tools, techniques, and real-world applications to help you succeed.
 
Course Description
Introduction to Data Engineering takes you through the entire lifecycle of data engineering—from data collection and ingestion to transformation, storage, and analysis. You’ll explore how to use leading technologies such as Apache Spark, Hadoop, and Airflow to automate, streamline, and optimize data pipelines.
You’ll also gain hands-on experience with essential cloud data platforms, including AWS Redshift, Google BigQuery, and Azure Data Factory. Through practical projects and case studies, you will learn how to apply your skills to real-world data engineering challenges. Designed to bridge theory and practice, this course ensures you develop both conceptual understanding and technical proficiency.
 
What You’ll Learn
Understand the fundamentals of data engineering and its role in modern analytics.
Design and implement data pipelines for structured and unstructured data.
Work effectively with ETL (Extract, Transform, Load) processes.
Leverage SQL, Python, and cloud tools for data processing and workflow orchestration.
Use data warehousing and data lake architectures efficiently.
Optimize data systems for scalability, performance, and reliability.
 
Requirements
Basic understanding of programming (Python preferred).
Familiarity with SQL and databases is helpful but not required.
Access to a computer with an internet connection.