Data Science
Intermediate
Reliable Data Pipelines
Build data pipelines that are observable, repeatable, and safe to rerun.
Course Overview
Learn idempotent loads, data quality checks, backfills, and operational ownership through a small analytics pipeline. Every exercise addresses what happens when a step fails halfway through.
What You Will Learn
Master core concepts and practical workflows in Reliable Data Pipelines
Apply industry best practices to build production-ready Data Science solutions
Utilize modern tools including data pipelines, quality, scheduling effectively
Diagnose common execution failures, performance bottlenecks, and edge cases
Establish repeatable engineering habits for continuous improvement
Tools & Technologies Used
Data Pipelines
Quality
Scheduling
Etl
Python 3.11
VS Code
Structured Curriculum
2 Modules · 6 Lessons · 195 Minutes Total
Module 1
Module 1: Fundamentals of Reliable Data Pipelines
3 lessons
Module 1: Fundamentals of Reliable Data Pipelines
Explore core principles, setup, and foundational concepts of Reliable Data Pipelines.
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Introduction to Reliable Data Pipelines Principles
Deconstruct core building blocks and architecture of Reliable Data Pipelines.
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Core Workflows with Data Pipelines
Hands-on demonstration of primary tools and API patterns.
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Practical Implementation & Configuration
Build your first working module with error validation.
Module 2
Module 2: Advanced Patterns & Real-World Application
3 lessons
Module 2: Advanced Patterns & Real-World Application
Master practical engineering patterns and deploy resilient Data Science projects.
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Handling Edge Cases & Error Boundaries
Implement defensive programming and robust exception management.
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Performance Optimization & Benchmarking
Benchmark performance, identify bottlenecks, and apply efficiency gains.
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Production Integration & Review
Assemble the final project, run test suites, and review deployment steps.
Practical Project & Capstone Outcome
🚀 Capstone Project
Production Reliable Data Pipelines Capstone Project
Build and test a complete, real-world application demonstrating all core skills learned in Reliable Data Pipelines, featuring comprehensive tests and documentation.
Prerequisites
- Basic familiarity with Data Science concepts
- A working computer with command-line terminal access
Intended Audience
- Practitioners wanting to build expertise in Reliable Data Pipelines
- Engineers and Analysts working with Data Science tools
Instructor Information
M
Mateo Silva
Course Author & Industry Expert
Mateo Silva is a seasoned industry professional with over 8 years of hands-on experience in Data Science and technical education.
Frequently Asked Questions
What background knowledge is required for Reliable Data Pipelines?
A foundational understanding of Data Science is helpful, but all key concepts are explained step-by-step.
Is source code provided for all exercises?
Yes! All lessons include repository code samples and step-by-step solution guides.