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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