Build a Flexible CSV Parser with Streaming, Validation, and Error Reporting

Create a production-ready CSV parser module in [PROGRAMMING_LANGUAGE] that can handle large CSV files efficiently with streaming, data validation, and comprehensive error reporting. The parser should be designed for processing [DATA_DESCRIPTION] data (e.g., customer records, product catalogs, financial transactions) and meet these specifications: 1. **Core Parser Configuration**: - Accept configuration options: delimiter (default: comma), quote character, escape character, encoding ([ENCODING], e.g., UTF-8), whether the first row is a header, and custom line terminators. - Support both file path input and readable stream/buffer input. - Use streaming/chunked processing to handle files of [EXPECTED_SIZE] (e.g., 500MB+) without loading the entire file into memory. 2. **Column Mapping & Schema Definition**: - Define a schema for the expected CSV structure with these columns: [COLUMN_DEFINITIONS] (e.g., "email: string, required, unique | age: integer, min=0, max=150 | signup_date: date, format=YYYY-MM-DD | status: enum(active, inactive, pending)"). - Map CSV column headers (or indices) to internal field names, supporting aliases for common header variations (e.g., 'Email Address' → 'email', 'E-mail' → 'email'). 3. **Row-Level Validation**: - Validate each row against the schema: check required fields, data types, format patterns (regex), value ranges, and enum values. - Support custom validation functions for complex business rules (e.g., "if [BUSINESS_RULE]"). - Collect all validation errors per row (don't stop at the first error). 4. **Error Handling & Reporting**: - Track and categorize errors: parsing errors (malformed CSV), validation errors (bad data), and warnings (e.g., trailing whitespace auto-trimmed). - Generate a structured error report including: row number, column name, provided value, error type, and human-readable message. - Support configurable behavior: skip invalid rows, abort after [MAX_ERRORS] errors, or collect all errors. 5. **Output & Transformation**: - Transform valid rows into [OUTPUT_FORMAT] (e.g., array of objects, JSON, database-ready insert statements, or another CSV). - Apply optional data transformations: trim whitespace, normalize case, parse dates, and convert number formats. - Provide a summary: total rows processed, valid rows, skipped rows, and error count by category. 6. **API Design**: Expose both a simple one-call function `parseCSV(source, schema, options)` and an event-driven/streaming interface with callbacks for `onRow`, `onError`, `onComplete`. Provide complete code with thorough comments, type definitions (if applicable), and a usage example that parses a sample CSV matching the [DATA_DESCRIPTION] schema. Include unit test cases for: valid data, missing required fields, type mismatches, malformed rows, and large file streaming.