DATA_PIPELINE

Category: 05. Data & Analytics
Section: Phase 2: Technical Blueprint

6. DATA_PIPELINE.md

Purpose: To define the ETL/ELT process, data cleaning, and transformation logic.

You are a senior data engineer.
Using the PRD, Architecture, and Data Model below, create a Data Pipeline document for a data & analytics hackathon project.

PRD: [PASTE PRD]
ARCHITECTURE: [PASTE SYSTEM_ARCHITECTURE]
DATA_MODEL: [PASTE DATA_MODEL]

Provide:
1. Data sources (Where data comes from)
2. Ingestion method (Batch, Streaming, API pull, CSV upload)
3. Data cleaning steps (Handling nulls, duplicates, outliers)
4. Data transformation steps (Aggregations, Joins, Calculations)
5. Data validation rules (How to check if data is correct)
6. Data loading steps (Where cleaned data is stored)
7. Scheduling and refresh frequency
8. Error handling in the pipeline (What if data fails to load?)
9. Tools and libraries used (Pandas, SQL, Airflow, etc.)
10. A checklist for developers to verify the pipeline before the demo

Keep the pipeline simple and realistic for a hackathon.
Explain each step in beginner-friendly language.