# Data Processor

## Overview
The Data Processor service provides robust data transformation, cleaning, and enrichment capabilities for the stock-bot platform. It serves as the ETL (Extract, Transform, Load) backbone, handling both batch and streaming data processing needs to prepare raw data for consumption by downstream services.

## Key Features

### Data Transformation
- **Format Conversion**: Transforms data between different formats (JSON, CSV, Parquet, etc.)
- **Schema Mapping**: Maps between different data schemas
- **Normalization**: Standardizes data values and formats
- **Aggregation**: Creates summary data at different time intervals

### Data Quality Management
- **Validation Rules**: Enforces data quality rules and constraints
- **Cleansing**: Removes or corrects invalid data
- **Missing Data Handling**: Strategies for handling incomplete data
- **Anomaly Detection**: Identifies and flags unusual data patterns

### Pipeline Orchestration
- **Workflow Definition**: Configurable data processing workflows
- **Scheduling**: Time-based and event-based pipeline execution
- **Dependency Management**: Handles dependencies between processing steps
- **Error Handling**: Graceful error recovery and retry mechanisms

### Data Enrichment
- **Reference Data Integration**: Enhances data with reference sources
- **Feature Engineering**: Creates derived features for analysis
- **Cross-source Joins**: Combines data from multiple sources
- **Temporal Enrichment**: Adds time-based context and features

## Integration Points

### Upstream Connections
- Market Data Gateway (for raw market data)
- External Data Connectors (for alternative data)
- Data Lake/Storage (for historical data)

### Downstream Consumers
- Feature Store (for processed features)
- Data Catalog (for processed datasets)
- Intelligence Services (for analysis input)
- Data Warehouse (for reporting data)

## Technical Implementation

### Technology Stack
- **Runtime**: Node.js with TypeScript
- **Processing Frameworks**: Apache Spark for batch, Kafka Streams for streaming
- **Storage**: Object storage for intermediate data
- **Orchestration**: Airflow for pipeline management
- **Configuration**: YAML-based pipeline definitions

### Architecture Pattern
- Data pipeline architecture
- Pluggable transformation components
- Separation of pipeline definition from execution
- Idempotent processing for reliability

## Development Guidelines

### Pipeline Development
- Modular transformation development
- Testing requirements for transformations
- Performance optimization techniques
- Documentation requirements

### Data Quality Controls
- Quality rule definition standards
- Error handling and reporting
- Data quality metric collection
- Threshold-based alerting

### Operational Considerations
- Monitoring requirements
- Resource utilization guidelines
- Scaling recommendations
- Failure recovery procedures

## Future Enhancements
- Machine learning-based data cleaning
- Advanced schema evolution handling
- Visual pipeline builder
- Enhanced pipeline monitoring dashboard
- Automated data quality remediation
- Real-time processing optimizations