Chapter 1 — How Data Pipelines Work
- What Is a Data Pipeline?
A Simple Example
What If the API Fails?
What If the Data Is Wrong?
What If the Same Data Arrives Twice?
What If the Pipeline Stops Halfway?
The Basic Pipeline
- Source
- Ingestion
- Validation
- Processing
- Storage
The Pipeline Is Not Always a Straight Line
Raw Data and Staging
Batch Pipelines
Streaming Pipelines
Batch vs Streaming
What Makes a Pipeline Reliable?
A Simple Real-World Example
Think About Failure Before Writing Code
The Data Engineering Mindset
- Understand
- Investigate
- Design
- Implement
- Test
- Verify
- Observe
- Recover
- Improve
Common Beginner Mistakes
- Only Thinking About the Happy Path
- Adding Tools Before Understanding the Problem
- Ignoring Duplicate Data
- Trusting the Input
- Assuming a Successful Run Means Correct Data
Practical Checklist
What You Should Remember
Chapter Summary
