⚡ Bolt: [performance improvement] Optimize DataFrame iterrows in ETL pipeline

[Vagarh]

💡 What:
Replaced the creation of a pandas DataFrame and iteration using df.iterrows() with a direct iteration over the native Python list of dictionaries in the load_data task of e2e_open_data_pipeline/dags/public_data_etl.py.

🎯 Why:
The ETL task pulled a JSON list of dictionaries from XCom and converted it to a Pandas DataFrame purely to iterate over its rows using iterrows() for inserting data into Postgres. iterrows() is notoriously slow, and allocating a DataFrame just to iterate over it introduces unnecessary memory overhead and processing time compared to basic Python collections.

📊 Impact:
Significantly reduces memory footprint and CPU processing time during the data loading phase, particularly as the number of records pulled from the API increases. The loop operation is now near-instantaneous native dict traversal instead of costly Pandas Series conversions.

🔬 Measurement:
Run the ETL DAG (open_data_etl_accidentes) and observe the execution time and memory usage of the load_task operator in the Airflow UI compared to previous runs.

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PR created automatically by Jules for task 4263350885591088969 started by @Vagarh

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