86 lines
3.1 KiB
Markdown
86 lines
3.1 KiB
Markdown
# 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
|