Enhancing AML Solution Database Pipeline

Based on your screenshots, I can see you're working on an AML (Anti-Money Laundering) solution that generates staging tables with data violations. Now you need to add more violations and create target tables from the staging data according to the data lineage specifications.

Let's address both tasks:

Task 1: Adding More Violations

Looking at your current code, you already have these violation types:

• datatype: Incorrect data type violations
• out_of_range: Values outside acceptable ranges
• numeric_constraint: Numeric value constraint violations
• inconsistent_date: Date format inconsistencies

Let's add more violation types that would be relevant for AML monitoring:

Explanation of Enhancements

1. New Violation Types Added

I've added five new violation types to enhance your AML solution:

1. null_value: Introduces NULL values in fields that should not be NULL

   • Important for AML as missing data can hide suspicious activities

2. pattern_mismatch: Values not matching expected patterns/formats

   • Useful for detecting incorrectly formatted identifiers, account numbers, etc.

3. referential_integrity: Foreign key violations

   • Critical for ensuring relationships between entities are maintained

4. duplicate_value: Unexpected duplicate values

   • Important for detecting duplicate transactions or accounts

5. length_violation: String length violations

   • Helps identify truncated or padded identifiers

2. Documentation Improvements

• Added a comprehensive violation description system
• Created an enhanced Word document output with descriptions for each violation
• Added a pipeline documentation generator

3. Target Table Creation

I've implemented a target table creation process that:

1. Uses the data lineage information from your Excel sheets
2. Maps source columns to target columns
3. Handles column name differences and spaces
4. Creates target CSV files
5. Loads target tables to a separate SQLite database

How to Use the Enhanced Code

1. Replace your existing script with this enhanced version or save it as a new file

2. Run the script to:

   • Generate clean data
   • Apply both existing and new violations
   • Create target tables based on data lineage
   • Generate documentation

3. Output files:

   • Clean data: csv_no_vio/[table_name].csv
   • Violated data: csv_with_vio/[table_name].csv
   • Target tables: csv_target/[table_name].csv
   • SQLite databases: aml_database.db, aml_database_vio.db, aml_database_target.db
   • Documentation: csv_with_vio/violation_log.docx, csv_target/pipeline_documentation.docx

Data Lineage Implementation

The code implements the data lineage mappings shown in your Excel sheets, where:

• Target tables (like CUST_CREDIT_RTNG, CUST_MKT_SERVED, etc.) are created from source tables
• Column mappings are defined based on the relationships in your data lineage sheet
• The transformation process handles derived fields and different data types

This implementation ensures that your target tables accurately reflect the data lineage specifications while maintaining the data quality checks through the violation system.

To configure the generation, complete these steps:

Looking at your data documentation screenshot, I can see the detailed column descriptions and metadata. Let me create a more optimized and clean solution focused specifically on generating target tables from your staging data based on the data lineage specifications.

Key Improvements in This Optimized Solution

1. Clean Object-Oriented Design

• TargetTableGenerator class encapsulates all functionality
• Clear separation of concerns with dedicated methods
• Proper error handling and logging throughout

2. Robust Data Lineage Configuration

• Based on your Excel screenshots showing source-to-target mappings
• Handles data type conversions (VARCHAR, NUMBER, DATE, CHAR)
• Supports different transformation types (direct, sequence, date_format)

3. Smart Column Matching

• Handles column name variations and spaces
• Tries multiple matching strategies to find source columns
• Logs warnings for missing mappings

4. Data Type Handling

• Proper conversion between Oracle data types
• Length constraints for VARCHAR/CHAR fields
• Numeric precision handling
• Date format standardization

5. Comprehensive Output

• CSV files for each target table
• SQLite database creation
• JSON summary report with statistics
• Data validation and quality checks

6. Data Quality Validation

• Completeness rate calculation
• Null value analysis per column
• Overall data quality status reporting

How to Use

1. Ensure your staging data is in the csv_with_vio folder

2. Run the main script:

   python target_table_generator.py

3. Check outputs:

   • Target tables: csv_target/[table_name].csv
   • SQLite database: aml_target_tables.db
   • Summary report: csv_target/generation_summary.json
   • Validation report: csv_target/validation_report.json

Target Tables Generated

Based on your data lineage, this will generate:

• CUST_CREDIT_RTNG
• CUST_MKT_SERVED
• CUST_PRODUCT
• CUST_SUPPLEMENTAL_ATTR
• CUST_ACCT
• CUST_ACCT_ROLE
• CNTRL_CUST
• CORP_ACTN
• CUST

Each table will have proper data type conversions and transformations applied according to the specifications in your documentation.

To configure the generation, complete these steps: