⚡ Bolt: Pandas iterrows() optimization in ETL pipeline

[Vagarh]

💡 What:
Replaced pd.DataFrame(data) and df.iterrows() with a direct list comprehension iteration over the data list of dictionaries in load_data of e2e_open_data_pipeline/dags/public_data_etl.py.

🎯 Why:
Pandas iterrows() is a known performance bottleneck. It converts every single row into a Pandas Series object which adds massive overhead in terms of execution time and memory usage. Since we only need to extract values to create a tuple for insertion via execute_values, directly accessing the python dictionaries avoids all this overhead.

📊 Impact:
Significantly reduces execution time for the load_data task by avoiding the slow Series creation. It also uses less memory since the intermediate DataFrame representation is skipped entirely.

🔬 Measurement:
We can verify the improvement by looking at the execution time of the load_data task in Airflow UI before and after this change. It should be tangibly faster.

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PR created automatically by Jules for task 17656273586896663308 started by @Vagarh

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