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docs/data-services/data-processor/README.md

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+# 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