Silver to Gold

These are the basic best practices followed when moving data from Silver to Gold.

Business alignment

  • Tailor Gold datasets to specific business needs and use cases
  • Collaborate closely with business stakeholders to understand requirements

Aggregation and summarization

  • Create pre-aggregated tables for common metrics (e.g., daily sales totals)
  • Implement various levels of granularity to support different analysis needs

Dimensional modeling

  • Develop star or snowflake schemas for analytical queries
  • Create conformed dimensions for consistent reporting across the organization

Denormalization

  • Create wide, denormalized tables for specific reporting needs
  • Balance performance gains against data redundancy

Metric standardization

  • Implement agreed-upon business logic for key performance indicators (KPIs)
  • Ensure consistent calculation of metrics across different Gold datasets

Data mart creation

  • Develop subject-area specific data marts (e.g., Sales, HR, Finance)
  • Optimize each mart for its intended use case

Advanced transformations

  • Apply complex business rules and calculations
  • Implement time-based analyses (e.g., year-over-year comparisons)

Data quality assurance

  • Implement rigorous testing of Gold datasets
  • Set up automated data quality checks and alerts

Performance optimization

  • Use appropriate indexing and partitioning strategies
  • Implement materialized views for frequently accessed data

Metadata management

  • Maintain detailed business glossaries and data dictionaries
  • Document data lineage and transformation logic

Access control

  • Implement fine-grained access controls for sensitive data
  • Ensure compliance with data governance policies

Versioning and historization

  • Implement slowly changing dimensions (SCDs) where appropriate
  • Maintain historical versions of key business entities

Data freshness

  • Define and implement appropriate refresh schedules
  • Balance data currency against processing costs

Self-service enablement

  • Create views or semantic layers for business users
  • Provide clear documentation and training for end-users

Caching strategies

  • Implement intelligent caching for frequently accessed data
  • Balance cache freshness against query performance

Query optimization

  • Tune common queries for optimal performance
  • Create aggregated tables or materialized views for complex calculations

Data exploration support:

  • Provide sample queries or analysis templates
  • Create dashboards or reports showcasing the value of Gold datasets

Scalability considerations

  • Design Gold datasets to handle growing data volumes
  • Implement appropriate archiving strategies for historical data

Documentation

  • Maintain comprehensive documentation of Gold dataset structures and uses
  • Provide clear guidelines on how to use and interpret the data