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⇱ Rust DataOps: CI/CD and Containers for Data Pipelines | Coursera


Rust DataOps: CI/CD and Containers for Data Pipelines

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Rust DataOps: CI/CD and Containers for Data Pipelines

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

6 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Build production-grade CI/CD pipelines for Rust applications using GitHub Actions, Makefiles, and Docker.

  • Manage advanced workflow patterns including matrix builds, interdependent jobs, and conditional logic.

  • Package, lint, and release containerized Rust data applications through an end-to-end deployment pipeline.

Details to know

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Recently updated!

May 2026

Assessments

1 assignment

Taught in English

Build your subject-matter expertise

This course is part of the Rust for Data Engineering Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 3 modules in this course

e.g. This is primarily aimed at first- and second-year undergraduates interested in engineering or science, along with high school students and professionals with an interest in programming.Master modern DataOps practices by building robust, automated CI/CD pipelines for data applications using Rust. This course teaches you how to design, implement, and maintain end-to-end deployment pipelines that combine the safety and performance of Rust with industry-standard DevOps tooling.

You will start with the fundamentals of Continuous Integration and Continuous Deployment, then progress to advanced topics including matrix builds, interdependent jobs, Dockerfile linting, and container packaging. Hands-on labs use GitHub Actions, Makefiles, and Docker to give you practical experience automating the full software delivery lifecycle for data-driven Rust applications. By the end of the course, you will be able to: write production-grade GitHub Actions workflows; lint, test, and release containerized Rust applications; manage complex job dependencies and matrix configurations; and ship a complete end-to-end deployment pipeline. Whether you are a data engineer modernizing your pipelines or a Rust developer adopting DataOps, this course gives you the patterns and tools to deliver reliable, automated, container-based data systems.

Covers Alfredo Deza, what you build, and parallel AI track.

What's included

1 reading

1 readingβ€’Total 10 minutes
  • About This Courseβ€’10 minutes

Covers DevOps as culture, automation as default, shared metrics, CALMS framework, and lean flow.

What's included

22 videos6 readings

22 videosβ€’Total 135 minutes
  • 1.1.0 Meet Your Instructorβ€’3 minutes
  • 1.1.1 Devops Principlesβ€’2 minutes
  • 1.1.2 Core Devops Principlesβ€’6 minutes
  • 1.1.2 What Are Llms And How Do They Workβ€’6 minutes
  • 1.1.3 What Is Automationβ€’8 minutes
  • 1.1.3 What Is Retrieval Augmented Generationβ€’4 minutes
  • 1.1.4 Foundations Of Logging And Monitoringβ€’9 minutes
  • 1.1.5 Visibility And Accountabilityβ€’5 minutes
  • 1.1.6 Real World Examples Of Applied Devopsβ€’6 minutes
  • 1.2.1 Devops Considerations For Applicationsβ€’2 minutes
  • 1.2.2 Version Control And Source Code Managementβ€’8 minutes
  • 1.2.2 What Is A Multi Model Applicationβ€’7 minutes
  • 1.2.3 Testing And Validationβ€’8 minutes
  • 1.2.4 Packaging And Versioningβ€’8 minutes
  • 1.2.5 Linting And Formattingβ€’5 minutes
  • 1.2.6 Rust Vs Pythonβ€’9 minutes
  • 1.3.1 Basics Of Containerizationβ€’2 minutes
  • 1.3.2 Exploring The Conceps Of Containerizationβ€’9 minutes
  • 1.3.3 Benefits Of Using Containers For Devopsβ€’5 minutes
  • 1.3.4 What Are Container Registriesβ€’6 minutes
  • 1.3.5 Distroless Containers With Rustβ€’9 minutes
  • 1.3.6 Cloud Scaling And Elasticity With Containersβ€’6 minutes
6 readingsβ€’Total 60 minutes
  • Key Terms: DevOps Principlesβ€’10 minutes
  • Reflection: DevOps Principlesβ€’10 minutes
  • Key Terms: DevOps Considerations for Applicationsβ€’10 minutes
  • Reflection: DevOps Considerations for Applicationsβ€’10 minutes
  • Key Terms: Basics of Containerizationβ€’10 minutes
  • Reflection: Basics of Containerizationβ€’10 minutes

Covers CI = continuous integration, CD = continuous delivery, value to learners, SageMaker Canvas, and where it fits.

What's included

17 videos8 readings1 assignment

17 videosβ€’Total 86 minutes
  • 2.1.1 Introduction To Ci Cd For Devopsβ€’2 minutes
  • 2.1.2 Overview Of Canvas Uiβ€’4 minutes
  • 2.1.2 What Is Ci Cdβ€’6 minutes
  • 2.1.3 Components Of A Jobβ€’5 minutes
  • 2.1.4 Overview Of Jenkinsβ€’5 minutes
  • 2.1.5 Introduction To Github Actionsβ€’6 minutes
  • 4.2.1 Introduction Advanced Ci Cd Conceptsβ€’2 minutes
  • 4.2.2 Working With Datasetβ€’6 minutes
  • 4.2.2 Automating Common Tasksβ€’7 minutes
  • 4.2.3 Managing Interdependent Jobsβ€’5 minutes
  • 4.2.4 Building A Matrix Jobβ€’6 minutes
  • 4.2.5 Handling Logic In Workflowsβ€’7 minutes
  • 4.3.1 Introduction Building And End To End Deployment Pipelineβ€’2 minutes
  • 4.3.2 Identifying Project Needsβ€’4 minutes
  • 4.3.3 Using Makefile And Lintingβ€’5 minutes
  • 4.3.4 Setting Up Dockerfile Linting For Pull Requestsβ€’7 minutes
  • 4.3.5 Packaging And Releasing A Container Appβ€’7 minutes
8 readingsβ€’Total 80 minutes
  • Key Terms: Introduction to CI/CD for DevOpsβ€’10 minutes
  • Reflection: Introduction to CI/CD for DevOpsβ€’10 minutes
  • Key Terms: Advanced CI/CD Conceptsβ€’10 minutes
  • Reflection: Advanced CI/CD Conceptsβ€’10 minutes
  • Key Terms: Building an End-to-End Deployment Pipelineβ€’10 minutes
  • Reflection: Building an End-to-End Deployment Pipelineβ€’10 minutes
  • Before You Goβ€’10 minutes
  • Next Stepsβ€’10 minutes
1 assignmentβ€’Total 5 minutes
  • Final Graded Quizβ€’5 minutes

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Instructors

Pragmatic AI Labs
61 Coursesβ€’6,684 learners

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