Data Warehouse Construction Standards - What

3.What are the key components of data warehouse standards?

a. Data Modeling Standards:

These standards define the guidelines for designing and structuring the data models used in the data warehouse. They include naming conventions, entity-relationship diagrams, data type definitions, and relationships between tables.

b. Data Integration Standards:

These standards focus on the processes and methods used to extract, transform, and load (ETL) data into the data warehouse. They cover data extraction techniques, data cleansing procedures, transformation rules, and data loading strategies.

c. Data Quality Standards:

These standards ensure the accuracy, consistency, completeness, and validity of data within the data warehouse. They include data profiling, data validation rules, data cleansing methodologies, and data quality metrics.

d. Metadata Standards:

Metadata standards define the structure and format of metadata stored in the data warehouse. They cover metadata definitions, metadata repositories, metadata integration, and metadata management processes.

e. Security and Access Control Standards:

These standards focus on protecting the data warehouse from unauthorized access, ensuring data privacy, and enforcing data security policies. They include user authentication, authorization mechanisms, encryption techniques, and data masking methods.

f. Performance and Scalability Standards:

These standards address the performance optimization and scalability aspects of the data warehouse. They cover query optimization techniques, indexing strategies, partitioning schemes, and data archiving processes.

g. Data Governance Standards:

Data governance standards define the overall framework and processes for managing and governing data within the data warehouse. They include data stewardship, data ownership, data lifecycle management, and data governance policies.

h. Documentation Standards:

These standards ensure the documentation of all aspects of the data warehouse, including data models, ETL processes, data dictionaries, and data lineage. They promote understanding, maintainability, and ease of future enhancements.