Have you written any SQL procedures? How would you ensure that your PL/SQL procedure processes financial data remains performant during migration to Python or vice versa?

When migrating financial data between PL/SQL and Python, performance hinges on how the data is extracted, transformed, and loaded. First, design the PL/SQL procedure to use bulk collect and FORALL to reduce context switches. Use efficient indexing on the columns involved in joins and filters, and avoid SELECT * by pruning columns. For large result sets, stream data in batches (e.g., 10,000 rows) rather than loading everything into memory. In Python, use generators or streaming APIs (e.g., pandas read_sql with chunksize) to process data incrementally. Leverage a columnar store or object store like S3 for intermediate storage, applying column pruning to read only needed columns. Finally, monitor execution plans, use explain plans, and tune SQL statements. By combining bulk operations in PL/SQL, batch streaming in Python, and efficient storage, you keep the migration performant and avoid memory bottlenecks.