agentic-data-stack-community/data-core/tasks/build-pipeline.md

AI Agentic Data Stack Framework - Community Edition. Open source data engineering framework with 4 core agents, essential templates, and 3-dimensional quality validation.

# Task: Build Pipeline

## Overview
Implements comprehensive data pipelines following enterprise best practices for reliability, scalability, and maintainability. Incorporates quality checks, monitoring, error handling, and governance controls throughout the pipeline architecture.

## Prerequisites
- Approved data contract with technical specifications
- Data architecture design and infrastructure plan
- Source system access and connectivity established
- Target system specifications and requirements
- Quality framework and validation rules defined

## Dependencies
- Templates: `pipeline-tmpl.yaml`, `monitoring-tmpl.yaml`
- Tasks: `design-data-architecture.md`, `implement-quality-checks.md`
- Checklists: `pipeline-deployment-checklist.md`

## Steps

### 1. **Pipeline Architecture Design**
   - Design end-to-end pipeline architecture based on data contract
   - Define data flow patterns (batch, streaming, micro-batch)
   - Specify processing stages and transformation logic
   - Plan error handling and recovery mechanisms
   - **Validation**: Architecture reviewed and approved by Data Architect

### 2. **Source System Integration**
   - Implement data extraction from source systems
   - Handle authentication, authorization, and connection management
   - Design incremental and full load strategies
   - Implement rate limiting and throttling controls
   - **Quality Check**: Source connectivity tested and data extraction validated

### 3. **Data Transformation Implementation**
   - Build data cleaning and standardization logic
   - Implement business rule transformations
   - Create data enrichment and lookup processes
   - Design aggregation and summarization functions
   - **Validation**: Transformations tested against sample data and business rules

### 4. **Quality Integration and Validation**
   - Embed quality checks at each pipeline stage
   - Implement real-time quality monitoring
   - Create data quality scorecards and metrics
   - Design quality issue escalation and remediation
   - **Quality Check**: Quality framework integrated and tested end-to-end

### 5. **Error Handling and Recovery**
   - Implement comprehensive error handling strategies
   - Design retry logic and circuit breaker patterns
   - Create dead letter queues for failed records
   - Build automated recovery and restart capabilities
   - **Validation**: Error scenarios tested and recovery mechanisms verified

### 6. **Performance Optimization**
   - Optimize data processing performance and throughput
   - Implement parallel processing and resource scaling
   - Design efficient data storage and retrieval patterns
   - Optimize memory usage and computational efficiency
   - **Quality Check**: Performance benchmarks meet SLA requirements

### 7. **Monitoring and Observability**
   - Implement comprehensive monitoring and alerting
   - Create performance dashboards and operational views
   - Design audit trails and lineage tracking
   - Build health checks and status monitoring
   - **Final Validation**: Monitoring system operational and alerts tested

## Interactive Features

### Progressive Pipeline Development
- **MVP Pipeline**: Basic data flow with core transformations
- **Production Pipeline**: Full feature set with monitoring and quality
- **Enterprise Pipeline**: Advanced features with governance and optimization

### Real-Time Pipeline Monitoring
- **Performance Metrics**: Throughput, latency, resource utilization
- **Quality Metrics**: Data quality scores, validation results
- **Operational Metrics**: Success rates, error frequencies, SLA compliance
- **Business Metrics**: Data freshness, completeness, availability

### Multi-Environment Deployment
- **Development**: Full feature testing with sample data
- **Staging**: Production-like testing with validation data
- **Production**: Live deployment with full monitoring and controls

## Outputs

### Primary Deliverable
- **Data Pipeline Implementation** (`pipeline-implementation/`)
  - Complete pipeline codebase with documentation
  - Configuration files for all environments
  - Deployment scripts and automation
  - Monitoring and alerting configurations

### Supporting Artifacts
- **Pipeline Documentation** - Architecture, design decisions, operational procedures
- **Performance Benchmarks** - Baseline metrics and SLA validation
- **Quality Reports** - Data quality validation and scorecard results
- **Operational Runbooks** - Troubleshooting, maintenance, and recovery procedures

## Success Criteria

### Quality Gates
- **Functional Completeness**: All data contract requirements implemented
- **Performance Standards**: Meets or exceeds SLA requirements for throughput and latency
- **Quality Integration**: Quality framework operational with real-time monitoring
- **Reliability Standards**: Error handling and recovery mechanisms validated
- **Operational Readiness**: Monitoring, alerting, and maintenance procedures operational

### Validation Requirements
- [ ] Data contract requirements fully implemented and tested
- [ ] Performance benchmarks meet or exceed SLA targets
- [ ] Quality checks operational with real-time monitoring
- [ ] Error handling tested across failure scenarios
- [ ] Security controls implemented and validated
- [ ] Monitoring and alerting operational and tested

### Evidence Collection
- Test results demonstrating functional completeness
- Performance benchmark reports with SLA validation
- Quality validation reports with scorecard results
- Error handling test documentation and results
- Security assessment and penetration test results
- Monitoring and alerting verification documentation

## Pipeline Architecture Patterns

### Batch Processing Patterns
- **ETL (Extract, Transform, Load)**: Traditional batch processing approach
- **ELT (Extract, Load, Transform)**: Modern cloud-native approach
- **Lambda Architecture**: Batch and speed layer combination
- **Kappa Architecture**: Streaming-first with batch capabilities

### Streaming Processing Patterns
- **Event Streaming**: Real-time event processing and routing
- **Change Data Capture**: Database change monitoring and propagation
- **Stream Processing**: Continuous data transformation and analytics
- **Micro-batch**: Small batch processing for near real-time results

### Integration Patterns
- **API Integration**: RESTful and GraphQL API consumption
- **Message Queue Integration**: Asynchronous message processing
- **File-based Integration**: Batch file processing and monitoring
- **Database Integration**: Direct database connectivity and replication

## Technology Stack Integration

### Orchestration Platforms
- **Apache Airflow**: Workflow orchestration and scheduling
- **Prefect**: Modern workflow management with dynamic DAGs
- **Dagster**: Asset-centric orchestration with data quality
- **Mage**: Pipeline development with built-in monitoring

### Processing Engines
- **Apache Spark**: Distributed data processing and analytics
- **dbt**: SQL-based transformation and modeling
- **Pandas**: Python-based data manipulation and analysis
- **Apache Beam**: Unified batch and streaming processing

### Storage Solutions
- **Cloud Data Warehouses**: Snowflake, BigQuery, Redshift
- **Data Lakes**: S3, Azure Data Lake, Google Cloud Storage
- **Databases**: PostgreSQL, MongoDB, Cassandra
- **Streaming Stores**: Apache Kafka, Amazon Kinesis

### Quality and Monitoring
- **Great Expectations**: Data quality testing and validation
- **Monte Carlo**: Data observability and monitoring
- **Soda**: Data quality checks and monitoring
- **dbt Test**: SQL-based data testing framework

## Implementation Best Practices

### Code Quality
- Modular design with reusable components
- Comprehensive testing with unit and integration tests
- Version control with proper branching strategies
- Code review processes and quality gates

### Configuration Management
- Environment-specific configuration files
- Secret management and security controls
- Infrastructure as code for reproducible deployments
- Configuration validation and testing

### Error Handling
- Graceful degradation and failure recovery
- Comprehensive logging and error tracking
- Automated alerting and escalation procedures
- Recovery testing and disaster recovery planning

### Performance Optimization
- Resource scaling and auto-scaling configurations
- Caching strategies for frequently accessed data
- Parallel processing and distributed computing
- Performance monitoring and optimization feedback loops

## Validation Framework

### Multi-Stage Testing
1. **Unit Testing**: Individual component functionality validation
2. **Integration Testing**: End-to-end data flow validation
3. **Performance Testing**: Throughput and latency validation
4. **Quality Testing**: Data quality framework validation
5. **Production Testing**: Live environment validation with monitoring

### Continuous Validation
- Automated testing in CI/CD pipelines
- Regular performance and quality assessments
- Monitoring-driven validation and alerting
- Feedback loops for continuous improvement

## Risk Mitigation

### Common Pitfalls
- **Performance Bottlenecks**: Design for scale from the beginning
- **Data Quality Issues**: Integrate quality checks throughout pipeline
- **Security Vulnerabilities**: Implement security best practices
- **Operational Complexity**: Design for maintainability and observability

### Success Factors
- Clear architecture design with stakeholder approval
- Comprehensive testing across all pipeline stages
- Robust error handling and recovery mechanisms
- Operational monitoring and alerting systems
- Documentation and knowledge transfer procedures

## Operational Procedures

### Deployment Process
- Automated deployment with rollback capabilities
- Environment promotion with validation gates
- Blue-green deployment for zero-downtime updates
- Configuration management and drift detection

### Monitoring and Maintenance
- Real-time monitoring with alerting thresholds
- Regular performance and capacity planning reviews
- Preventive maintenance and optimization procedures
- Incident response and escalation protocols

### Change Management
- Version control for all pipeline components
- Impact assessment for changes and updates
- Testing and validation procedures for changes
- Rollback procedures and contingency planning

## Notes
Pipeline implementation is the cornerstone of data infrastructure - invest in robust architecture, comprehensive testing, and operational excellence from the start. Focus on reliability, scalability, and maintainability to ensure long-term success and stakeholder confidence.