SQL Server Data Warehouse Design

This section covers the principles and best practices for designing robust and scalable data warehouses using Microsoft SQL Server. A well-designed data warehouse is crucial for effective business intelligence, reporting, and analytical workloads.

Core Concepts

Understanding the fundamental concepts of data warehousing is the first step towards a successful design. Key areas include:

Dimensional Modeling: Learn about star schemas, snowflake schemas, facts, and dimensions.
ETL Processes: Explore Extract, Transform, and Load strategies for populating your data warehouse.
Data Marts: Understand how to create subject-oriented subsets of data for specific business needs.
Performance Considerations: Techniques for optimizing query performance and data loading.

Dimensional Modeling Best Practices

Dimensional modeling is central to data warehouse design. We focus on creating intuitive and query-efficient structures.

Fact Tables: Granularity, additive vs. semi-additive vs. non-additive measures.
Dimension Tables: Slowly Changing Dimensions (SCDs), degenerate dimensions, role-playing dimensions.
Schema Design: Choosing between star and snowflake schemas based on requirements.

ETL and Data Integration

Efficiently moving and transforming data is vital. SQL Server Integration Services (SSIS) is the primary tool.

Key SSIS components and patterns:

Data Flow Tasks
Control Flow Tasks
Package Design Patterns
Error Handling and Logging
Incremental Loads

Advanced Topics

Dive deeper into specialized areas for advanced data warehouse solutions.

Data Vault Modeling: An alternative modeling technique for enterprise data warehouses.
Data Lake Integration: Combining data warehousing with data lakes for big data analytics.
Performance Tuning: Indexing strategies, partitioning, and query optimization for large datasets.
Master Data Management (MDM): Ensuring data consistency and accuracy across the enterprise.

Example: Sales Data Warehouse Schema

Let's consider a simplified example of a sales data warehouse.

Fact Table: SalesFact

                    
CREATE TABLE SalesFact (
    DateKey INT,
    ProductKey INT,
    CustomerKey INT,
    StoreKey INT,
    SalesAmount DECIMAL(18, 2),
    Quantity INT,
    UnitPrice DECIMAL(18, 2)
);
                    
                

Dimension Tables

                    
CREATE TABLE DimDate (
    DateKey INT PRIMARY KEY,
    FullDate DATE,
    DayOfWeek VARCHAR(10),
    Month INT,
    MonthName VARCHAR(10),
    Year INT
);

CREATE TABLE DimProduct (
    ProductKey INT PRIMARY KEY,
    ProductName VARCHAR(100),
    Category VARCHAR(50),
    Brand VARCHAR(50)
);

CREATE TABLE DimCustomer (
    CustomerKey INT PRIMARY KEY,
    CustomerName VARCHAR(100),
    City VARCHAR(50),
    StateProvince VARCHAR(50)
);

CREATE TABLE DimStore (
    StoreKey INT PRIMARY KEY,
    StoreName VARCHAR(100),
    Region VARCHAR(50)
);
                    
                

MSDN Documentation