# ── STEP 1: Clone the repo to your laptop (do this only once) ─────
git clone https://github.com/your-company/cars-etl-pipeline.git
cd cars-etl-pipeline

# ── STEP 2: Always pull latest main before creating your branch ───
# This ensures you start from the most up-to-date code
git checkout main
git pull origin main

# ── STEP 3: Create your feature branch ───────────────────────────
# Naming convention: feature/{ADO-ticket-id}-{short-description}
# This links your branch directly to the ADO task
git checkout -b feature/1042-bronze-glue-job

# ── STEP 4: Write your code ───────────────────────────────────────
# Create your files, write the feature, run tests locally
# Example: create the bronze layer Glue job script

# ── STEP 5: Check what files you changed ─────────────────────────
git status
# Shows: modified files in red (not staged) and green (staged)

# ── STEP 6: Stage your changes ───────────────────────────────────
git add glue_jobs/bronze_layer.py      # stage specific file
git add tests/test_bronze_layer.py     # stage test file too
# OR stage everything at once:
git add .

# ── STEP 7: Commit with a meaningful message ──────────────────────
# Good commit message = what you did + why (not how)
git commit -m "feat: add Bronze Glue job for CSV to Parquet ingestion

- Reads raw cars CSV from S3 raw/ prefix
- Converts to Parquet and writes to S3 bronze/ prefix
- Adds data type casting for price and year columns
- Fixes: ADO#1042"

# ── STEP 8: Push your branch to GitHub ───────────────────────────
git push origin feature/1042-bronze-glue-job

# ── STEP 9: Raise a Pull Request on GitHub website ───────────────
# Go to GitHub → your repo → you'll see a banner:
# "feature/1042-bronze-glue-job had recent pushes — Compare & pull request"
# Click it, fill in the PR template, assign a reviewer, submit

## What does this PR do?
Adds the Bronze layer Glue job that ingests raw cars CSV from S3
and converts it to Parquet format in the bronze/ S3 prefix.

## ADO Ticket
Fixes AB#1042

## Changes Made
- Created `glue_jobs/bronze_layer.py` — PySpark script for ingestion
- Added `tests/test_bronze_layer.py` — unit tests (85% coverage)
- Updated `dags/cars_etl.py` — added bronze task to Airflow DAG

## How to Test
1. Run `pytest tests/test_bronze_layer.py` — all tests should pass
2. Check S3 bronze/ prefix for Parquet output after running

## Screenshots / Evidence
[Attach screenshot of successful local run or test output]

## Checklist
- [x] Code follows team style guide
- [x] Unit tests written and passing
- [x] No hardcoded credentials or secrets
- [x] Confluence page updated if requirements changed