Lesson 54 of 55
20 mins readProduction Capstone: Multi-Source ETL & Reporting Engine
In Plain English
Synthesize everything you have learned: construct an ETL (Extract, Transform, Load) reporting engine that parses raw sensor data, aggregates statistics, and outputs clean structured summaries.
Deep Dive: How It Works
Extract: Ingest raw delimited records or stream inputs.
Transform: Sanitize corrupted fields with regex substitution, calculate moving averages.
Load/Report: Format markdown table or JSON data report for downstream ingestion.
Core Rules to Remember
Full Pipeline Integration: Combines subroutines, references, regex, arrays, and hashes.
Robust Error Handling: Filters malformed records cleanly without script crashes.
Live Interactive Example
Hit Run Code to see it liveETL Pipeline Engine
Python 3.12
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Output Console
Click "Run Code" to view the rendered output.
How it works: ETL pipeline filtered out the invalid record and computed average temperatures accurately.
Your Turn: Micro Challenge
No pressure! Edit the starter code below and test your solution with instant feedback.
Micro Exercise
Run ETL Data Extraction
- Extract valid numbers from `@stream = ("val=10", "val=bad", "val=30");`
- Calculate total and print `"Valid Total: $sum\n"`.
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Sandbox Output
Click "Run & Check" to test your solution.
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