ETL Pipeline Development

Course Overview

ETL Pipeline Development is a practical, engineering‑focused course that teaches learners how to design, build, automate, and optimize Extract‑Transform‑Load (ETL) and Extract‑Load‑Transform (ELT) pipelines. The course covers data ingestion, transformation logic, workflow orchestration, data quality, metadata management, and cloud‑native pipeline architectures. Learners work with real datasets and tools such as SQL, Python, Airflow, Spark, dbt, and cloud services to build production‑grade data pipelines.

Target Audience

This course is ideal for:

• Aspiring data engineers and analytics engineers

• Data analysts transitioning into engineering roles

• Python/SQL developers working with data workflows

• Students or career switchers entering data engineering or cloud roles

• Professionals building pipelines on AWS, Azure, or GCP

Course Outcomes

By the end of this course, learners will be able to:

• Understand ETL vs. ELT architectures and when to use each

• Build scalable data ingestion workflows from databases, APIs, and files

• Transform data using SQL, Python, and distributed engines like Spark

• Implement workflow orchestration using Airflow, Prefect, or cloud schedulers

• Apply data quality checks, validation rules, and error‑handling strategies

• Design data pipelines for batch and real‑time processing

• Work with data lakes, warehouses, and lakehouse architectures

• Optimize pipelines for performance, reliability, and cost

• Deploy and monitor pipelines in cloud environments