Automate, Optimize, and Benchmark Data Pipelines

Overview

Did you know that two pipelines performing the same task can differ in run time by over 10x depending on design choices? Benchmarking and automation are essential for building fast, scalable, and cost-efficient data systems. This Short Course was created to help data engineers and pipeline architects optimize data processing systems through performance benchmarking and automation scripting to enhance efficiency and scalability in enterprise environments. By completing this course, you will be able to compare competing pipeline designs using run-time metrics, justify the most efficient approach, and automate the creation of transformation models using configuration-driven scripts—skills that help you build smarter, faster, and more reliable data pipelines. By the end of this course, you will be able to: Evaluate competing pipeline designs by comparing run-time statistics to justify the faster option. Create an automated script to generate data transformation models from configuration files. This course is unique because it blends performance engineering with automation, giving you practical experience in benchmarking real pipelines and generating transformation workflows programmatically to support large-scale data operations. To be successful in this project, you should have: SQL experience Data transformation knowledge Basic scripting skills Familiarity with pipeline architecture

Syllabus

Module 1: Pipeline Performance Evaluation and Benchmarking

Learners will master evidence-based pipeline performance evaluation by systematically measuring execution metrics, analyzing runtime statistics, and making data-driven optimization decisions.

Module 2: Automated Pipeline Optimization

Learners will develop automation skills to create scripts that read configuration specifications and generate complete data transformation models, enabling scalable and consistent pipeline development.