Data Warehousing Best Practices

Here are some notes¹ on DW Best Practices. Good points to keep in mind but admittedly warrants more concrete examples and unpacking (more in the future, so this could be an evolving note).

Table Design Best Practices

• Normalization and Denormalization
  • Use normalization to reduce data redundancy and improve data integrity.
  • Balance it with denormalization for query performance in read-heavy scenarios.
• Partitioning
  • Improves query performance and manageability.
  • Choose appropriate partitioning keys based on query patterns.
• Indexing
  • Speeds up query performance.
  • Create indexes on columns that are frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses.
  • See Sargable SQL queries.
• Primary and Foreign Keys
  • Define primary keys to uniquely identify each row
  • Foreign keys to maintain referential integrity between tables.
• Surrogate Keys
  • Consider using surrogate keys (e.g., auto-increment integers, hash keys) instead of natural keys for primary keys
  • Avoid issues with changing natural key values.
• Column Data Types
  • Choose appropriate data types for each column to optimize storage and performance.
  • Avoid using large data types unless necessary.
• Data Compression
  • Implement data compression techniques to save storage space and improve I/O performance.
• Audit Columns
  • Include audit columns (e.g., created_at, updated_at) to track record creation and modification times.
• Naming Conventions
  • Follow consistent naming conventions for tables, columns, and other database objects to improve readability and maintainability.
  • See SQL Style Guide by Simon Holywell
• Avoid Nulls
  • Minimize the use of NULL values as they can complicate queries and data processing
  • Use default values where applicable.

Data Warehouse Design Guidelines

• Understand Business Goals
  • Clearly define the business objectives and requirements that the data warehouse aims to address
• Data Governance
  • Establish strong data governance policies to ensure data quality, security, and compliance
  • A Medium article about securing PII Secure PII in Data Warehouse using BigQuery, Dataplex, MS Entra ID
  • Using Iceberg data format in Athena to implement GDPR use-cases
• Metadata Management
  • Properly manage metadata to organize and document data assets, making them easily accessible
• Scalability
  • Design the data warehouse with scalability in mind to accommodate future growth and increasing data volumes
• Performance Optimization
  • Regularly monitor and tune query performance using techniques like partitioning and indexing
• Security Measures
  • Implement advanced security measures such as encryption, access control, and authentication to protect sensitive information
• Automation
  • Automate processes like data transformation and model creation to reduce manual errors and improve efficiency
• Agile Approach
  • Adopt an agile methodology to allow for rapid adjustments and continuous improvement
• Data Quality Assurance
  • Implement robust data cleansing and validation processes to ensure high data quality
• Data Lifecycle Management
  • Define strategies for data archival and purging to keep the data warehouse lean and efficient

Footnotes

1. Written with CoPilot, I’ve added more notes and edited the text. ↩︎