data-engineering-governance.md

Data Engineering & Governance

Introduction

This document outlines standards and best practices for data engineering, management, and governance across Bayat projects. These guidelines ensure data consistency, quality, security, and compliance while enabling teams to effectively utilize data for business insights and operations.

Core Principles

Data as a Strategic Asset

• Treat data as a valuable organizational asset
• Align data initiatives with business objectives
• Establish data ownership and stewardship
• Apply appropriate controls and governance
• Implement data valuation frameworks

Data Quality by Design

• Build quality controls into data pipelines
• Establish clear data quality metrics and thresholds
• Implement validation at collection and processing
• Create automated monitoring for quality issues
• Define remediation processes for quality problems

Ethical Data Management

• Respect user privacy and data rights
• Ensure appropriate consent for data collection
• Implement data minimization practices
• Consider potential bias in data collection
• Practice responsible data stewardship

Scalable Architecture

• Design data systems for future growth
• Implement horizontally scalable solutions
• Consider performance at scale from the beginning
• Plan for evolving data volume and complexity
• Balance scalability with cost efficiency

Data Architecture

Logical Data Architecture

• Define clear data domains and boundaries
• Establish common data models and definitions
• Document entity relationships
• Implement consistent naming conventions
• Create data flow diagrams for key processes

Data Platform Components

Data Storage

• Define appropriate storage solutions for different data types
• Implement tiered storage strategies based on access patterns
• Establish data retention policies for different data classes
• Document storage encryption requirements
• Define backup and recovery strategies

Data Integration

• Establish standard patterns for data integration
• Define batch vs. real-time integration approaches
• Implement data validation during integration
• Create metadata capture during integration
• Document system integration points

Data Processing

• Define standard batch processing frameworks
• Establish stream processing patterns
• Implement job orchestration and scheduling
• Define error handling and retry strategies
• Create monitoring for processing jobs

Data Serving

• Implement appropriate serving layers for different use cases
• Define caching strategies for frequently accessed data
• Establish API standards for data access
• Document performance requirements for data serving
• Implement appropriate security controls

Reference Architecture

┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Data Sources   │────▶│  Data Ingestion │────▶│  Data Storage   │
└─────────────────┘     └─────────────────┘     └────────┬────────┘
                                                         │
                                                         ▼
┌─────────────────┐     ┌─────────────────┐     ┌─────────────────┐
│  Data Serving   │◀────│  Data Processing│◀────│   Data Quality  │
└─────────────────┘     └─────────────────┘     └─────────────────┘
        │
        ▼
┌─────────────────┐     ┌─────────────────┐
│ Data Consumption│     │  Data Governance│
└─────────────────┘     └─────────────────┘

Data Modeling

Data Modeling Approaches

• Define when to use normalized vs. denormalized models
• Establish dimensional modeling standards
• Document entity-relationship modeling practices
• Define standards for schema evolution
• Implement consistent naming conventions

Master Data Management

• Identify and document master data entities
• Establish single source of truth for master data
• Define master data governance processes
• Implement master data synchronization strategies
• Create master data quality metrics

Metadata Management

• Define metadata capture requirements
• Implement a centralized metadata repository
• Document technical and business metadata
• Create metadata discovery and search capabilities
• Establish metadata quality standards

Data Pipeline Development

Pipeline Design Principles

• Design for idempotency and repeatability
• Implement proper error handling and logging
• Create data lineage tracking
• Design for performance and scalability
• Support monitoring and observability

ETL/ELT Patterns

• Define standard patterns for extraction, transformation, and loading
• Document when to use ETL vs. ELT approaches
• Establish transformation logic documentation standards
• Implement source-to-target mappings
• Create data reconciliation processes

Workflow Orchestration

• Define workflow orchestration standards
• Implement dependency management
• Establish retry and failure handling mechanisms
• Document scheduling approaches
• Create alerting for workflow failures

Testing Strategies

• Implement unit testing for data transformations
• Create integration testing for data pipelines
• Establish data validation testing
• Define performance testing requirements
• Document test data management approaches

Data Quality Management

Data Quality Dimensions

• Define standards for accuracy, completeness, consistency, timeliness, validity, and uniqueness
• Establish measurement methodologies for each dimension
• Create data quality metrics and KPIs
• Document acceptable thresholds for quality dimensions
• Implement remediation procedures for quality issues

Data Quality Rules

• Define schema validation rules
• Establish business rule validation
• Document referential integrity checks
• Create format and pattern validation
• Implement range and constraint validation

Monitoring and Alerting

• Define real-time data quality monitoring
• Establish alerting thresholds and procedures
• Create data quality dashboards
• Document incident response procedures
• Implement trend analysis for quality metrics

Data Cleansing

• Define data cleansing methodologies
• Establish standardization rules
• Document deduplication strategies
• Create missing value handling approaches
• Implement outlier detection and handling

Data Governance

Governance Structure

• Define data governance roles and responsibilities
• Establish data stewardship model
• Create data governance committees
• Document escalation and decision-making processes
• Implement governance metrics and reporting

Policies and Standards

• Define data classification policies
• Establish data retention and archiving policies
• Create data quality policies
• Document data access and security policies
• Implement data privacy and protection standards

Regulatory Compliance

• Document applicable regulatory requirements
• Establish compliance verification processes
• Create audit trails and evidence collection
• Implement data subject rights management
• Define data breach response procedures

Data Lifecycle Management

• Define data lifecycle stages
• Establish processes for each lifecycle stage
• Document archiving and purging strategies
• Create data deprecation procedures
• Implement lifecycle transition approvals

Data Mesh Architecture

Domain-Oriented Ownership

• Define data domain boundaries
• Establish domain ownership model
• Document domain data product standards
• Create cross-domain integration patterns
• Implement domain-specific governance

Self-Serve Data Platform

• Define self-service capabilities
• Establish standardized tooling
• Document discovery mechanisms
• Create onboarding processes
• Implement usage monitoring

Data Products

• Define data product standards
• Establish product documentation requirements
• Document product quality guarantees
• Create product versioning standards
• Implement product lifecycle management

Federated Computational Governance

• Define federated governance model
• Establish global vs. local governance
• Document decision-making frameworks
• Create governance tooling requirements
• Implement governance metrics

Security and Privacy

Data Security

• Define data encryption standards
• Establish access control models
• Document authentication and authorization
• Create security monitoring requirements
• Implement security incident response

Data Privacy

• Define privacy-by-design principles
• Establish anonymization and pseudonymization standards
• Document consent management
• Create privacy impact assessment process
• Implement privacy controls audit procedures

Access Management

• Define role-based access control
• Establish access request and approval workflows
• Document privileged access management
• Create access certification processes
• Implement access monitoring and logging

Sensitive Data Handling

• Define sensitive data classification
• Establish discovery and scanning procedures
• Document masking and tokenization approaches
• Create secure processing requirements
• Implement special handling procedures

Data Tooling and Technology

Tool Selection Criteria

• Define evaluation criteria for data tools
• Establish proof of concept methodologies
• Document tool integration requirements
• Create tool adoption process
• Implement tool usage monitoring

Recommended Technologies

Data Storage

• Relational Databases: PostgreSQL, MySQL, Oracle, SQL Server
• NoSQL Databases: MongoDB, Cassandra, DynamoDB
• Data Warehouses: Snowflake, Redshift, BigQuery, Synapse
• Data Lakes: Delta Lake, Iceberg, Hudi, Cloud Storage
• Object Storage: S3, Azure Blob Storage, Google Cloud Storage

Data Processing

• Batch Processing: Spark, Hadoop, Flink
• Stream Processing: Kafka Streams, Flink, Spark Streaming
• ETL/ELT Tools: DBT, Airflow, Fivetran, Matillion
• Transformation: Spark, dbt, Dataflow, Databricks

Data Governance

• Metadata Management: Collibra, Alation, Atlan, Amundsen
• Data Quality: Great Expectations, Deequ, Monte Carlo, Soda
• Data Catalogs: Alation, Collibra, Atlan, DataHub
• Lineage Tools: OpenLineage, Marquez, Atlas

Technology Standards

• Define technology standardization approach
• Establish approved technology list
• Document technology retirement process
• Create technology evaluation framework
• Implement technology lifecycle management

Implementation Guidelines

Project Setup

• Define standard project structure
• Establish documentation requirements
• Document environment setup
• Create code organization standards
• Implement version control practices

Development Standards

• Define code style and standards
• Establish testing requirements
• Document CI/CD integration
• Create code review process
• Implement logging standards

Production Deployment

• Define deployment validation requirements
• Establish rollback procedures
• Document performance testing
• Create monitoring setup
• Implement incident response process

Maintenance and Evolution

• Define maintenance responsibilities
• Establish change management process
• Document upgrade procedures
• Create technical debt management
• Implement continuous improvement

Measurement and Metrics

Data Platform Metrics

• Define reliability metrics
• Establish performance metrics
• Document cost metrics
• Create usage and adoption metrics
• Implement operational metrics

Data Quality Metrics

• Define accuracy measurement
• Establish completeness metrics
• Document timeliness tracking
• Create consistency monitoring
• Implement trend analysis

Team Performance Metrics

• Define delivery metrics
• Establish quality metrics
• Document efficiency metrics
• Create collaboration indicators
• Implement continuous improvement metrics

Case Studies

Data Quality Implementation

[Include a case study of successful data quality implementation with concrete metrics and outcomes]

Data Platform Modernization

[Include a case study of platform modernization with lessons learned and benefits]

Data Governance Program

[Include a case study of implementing a governance program with organizational impact]

Appendix

Templates and Checklists

Data Pipeline Requirements Template

# Data Pipeline Requirements

## Business Context
- Purpose and business value
- Key stakeholders
- Success criteria

## Source Data
- Data sources and formats
- Expected volume and frequency
- Schema and sample data
- Quality characteristics

## Processing Requirements
- Transformations needed
- Business rules
- Performance requirements
- Error handling approach

## Target Data
- Target systems
- Schema design
- Access patterns
- Retention requirements

## Operational Considerations
- SLAs and timing
- Dependencies
- Monitoring requirements
- Alerting thresholds

Data Quality Assessment Checklist

# Data Quality Assessment Checklist

## Completeness
- [ ] Missing values analysis
- [ ] Required field validation
- [ ] Coverage analysis

## Accuracy
- [ ] Reference data validation
- [ ] Business rule validation
- [ ] Historical trend analysis

## Consistency
- [ ] Cross-field validation
- [ ] Cross-system consistency
- [ ] Temporal consistency

## Timeliness
- [ ] Data freshness measurement
- [ ] Processing time analysis
- [ ] SLA compliance check

## Uniqueness
- [ ] Duplicate detection
- [ ] Entity resolution check
- [ ] Key integrity verification

## Documentation
- [ ] Quality metrics documented
- [ ] Remediation actions defined
- [ ] Ownership assigned

Glossary of Terms

• Data Lineage: Documentation of data's origins, movements, characteristics, and quality
• Data Catalog: Inventory of available data assets with metadata
• Data Lake: Storage repository for raw structured and unstructured data
• Data Warehouse: Repository for structured, filtered data optimized for analysis
• Data Mart: Subset of a data warehouse focused on a specific business area
• Data Mesh: Decentralized sociotechnical approach to data management
• ETL: Extract, Transform, Load - process of combining data from multiple sources
• ELT: Extract, Load, Transform - variation where transformation happens after loading
• Master Data: Core business data representing key business entities
• Metadata: Data that provides information about other data
• Data Governance: Framework for data asset management
• Data Steward: Person responsible for data quality in a specific domain
• Data Pipeline: Series of data processing steps
• Data Product: Packaged data with its accessing code and documentation
• Schema: Structure that defines how data is organized
• Data Drift: Unexpected changes in data structure or semantics over time