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URL: https://www.coursera.org/learn/automate-optimize-and-benchmark-data-pipelines

⇱ Automate, Optimize, and Benchmark Data Pipelines | Coursera

Automate, Optimize, and Benchmark Data Pipelines

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Automate, Optimize, and Benchmark Data Pipelines

This course is part of multiple programs.

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Advanced level

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2 hours to complete
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Gain insight into a topic and learn the fundamentals.
Advanced level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Performance measurement and evidence-based decisions rely on comparing execution metrics to improve data engineering efficiency.

  • Config-driven model generation cuts manual work, keeps projects consistent, and supports scalable data transformation.

  • Pipeline optimization uses repeated measurement and programmatic fixes to deliver lasting performance gains.

  • Modern data engineering succeeds by creating reusable, maintainable systems that adapt to changing needs while preserving performance.

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Recently updated!

February 2026

Assessments

4 assignments¹

AI Graded see disclaimer
Taught in English

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There are 2 modules in this course

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

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

What's included

4 videos1 reading2 assignments

4 videos•Total 26 minutes
  • The Performance Cost of Guessing Wrong •3 minutes
  • Fundamentals of Pipeline Performance Measurement •8 minutes
  • Tools and Techniques for Runtime Measurement •12 minutes
  • Hands-On Pipeline Performance Comparison Using SQL Profiling •4 minutes
1 reading•Total 8 minutes
  • Statistical Methods for Performance Analysis •8 minutes
2 assignments•Total 15 minutes
  • Performance Benchmarking Analysis Project •10 minutes
  • Pipeline Performance Evaluation Knowledge Check •5 minutes

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

What's included

3 videos2 readings2 assignments1 ungraded lab

3 videos•Total 19 minutes
  • From Manual Headaches to Automated Excellence•3 minutes
  • Building Configuration File Structures for Data Models •10 minutes
  • Creating an Automated Model Generation Script in Python•6 minutes
2 readings•Total 18 minutes
  • Configuration-Driven Development Principles •10 minutes
  • Script Development Patterns for Code Generation •8 minutes
2 assignments•Total 15 minutes
  • Automation Script Development Knowledge Check •5 minutes
  • Comprehensive Pipeline Automation Mastery Assessment•10 minutes
1 ungraded lab•Total 18 minutes
  • Automated Data Transformation Model Generator•18 minutes

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Frequently asked questions

In this course, data pipeline optimization means improving pipeline performance through systematic measurement, comparison of design choices, and automation of repeatable transformation work. The focus is on making evidence-based changes that improve how pipelines run and scale, rather than relying on intuition.

You would use it when multiple pipeline designs can perform the same task, but you need a clear way to decide which one runs better under real conditions. It is also useful when repetitive transformation work is creating inconsistency and you want a more reusable, configuration-driven approach.

It fits into the build-and-improve phase of data engineering, after a pipeline is working well enough to measure and before teams settle on a repeatable long-term pattern. In this course, optimization connects performance evaluation with automation so pipeline changes can be justified and applied more consistently.

One-off tweaks are isolated changes made because something seems slow, while pipeline optimization in this course is a structured process based on repeated measurement and controlled comparison. It also goes beyond a single fix by using automation to reduce manual transformation work and keep similar models consistent.

A basic understanding of SQL, data transformation, scripting, and pipeline architecture is helpful before taking this course. Because the course is advanced, it assumes you can follow technical pipeline logic and work with measured performance results.

The course uses SQL for runtime measurement and Python-based scripting for configuration-driven model generation. The main methods are performance benchmarking and automated generation of transformation models from configuration files.

You will practice setting up fair pipeline comparisons, collecting and interpreting runtime data, and judging which design is more efficient based on evidence. You will also create automation that reads configuration files, generates transformation models, and supports more repeatable pipeline development.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.