## MODIFIED Requirements
### Requirement: PyArrow Dataset Deduplication Performance
The PyArrow dataset deduplication SHALL use vectorized operations instead of Python loops, providing significant performance improvements for large datasets.

#### Scenario: Large dataset deduplication with vectorized operations
- **GIVEN** a dataset with 1 million rows and duplicate records
- **WHEN** deduplicate_parquet_dataset_pyarrow is called with key_columns=["id"]
- **THEN** the operation SHALL complete in under 10 seconds
- **AND** memory usage SHALL not exceed 2GB during operation
- **AND** results SHALL be identical to the previous implementation

#### Scenario: Chunked processing for very large datasets
- **GIVEN** a dataset with 10 million rows
- **WHEN** deduplication is performed
- **THEN** data SHALL be processed in chunks to avoid memory overflow
- **AND** progress SHALL be trackable for long operations

### Requirement: PyArrow Merge Operations Vectorization
PyArrow merge operations SHALL use vectorized operations instead of converting tables to Python lists.

#### Scenario: Vectorized merge operations
- **GIVEN** large source and target tables for merge operations
- **WHEN** merge operations are performed
- **THEN** operations SHALL use PyArrow's built-in vectorized functions
- **AND** no table.to_pylist() conversions SHALL be used in merge logic
- **AND** performance SHALL be significantly improved over Python loop approach

## ADDED Requirements
### Requirement: Streaming Processing for Large Datasets
The PyArrow backend SHALL support streaming processing for datasets that exceed available memory.

#### Scenario: Memory-efficient processing
- **GIVEN** a dataset larger than available system memory
- **WHEN** dataset operations are performed
- **THEN** operations SHALL process data in streaming chunks
- **AND** peak memory usage SHALL be bounded regardless of dataset size
- **AND** correctness SHALL be maintained throughout streaming operations

### Requirement: Performance Monitoring and Metrics
Dataset operations SHALL provide performance metrics to help users understand operation characteristics.

#### Scenario: Performance metrics collection
- **GIVEN** dataset operations are performed
- **WHEN** operation completes
- **THEN** metrics SHALL be available including:
  - Processing time
  - Memory usage peaks
  - Data throughput
  - Operation-specific metrics (rows processed, files handled)
