SQL Server Schema Generator
Design T-SQL tables, generate production-ready DDL for MSSQL and Azure SQL.
Columns ({count})
SQL Server Schema Design for Enterprise Applications
SQL Server is the database of choice for enterprise applications that demand reliability, security, and performance at scale. When designing SQL Server schemas, you have access to powerful T-SQL features including computed columns for derived values, identity specifications for auto-increment behavior, and advanced constraints for maintaining data integrity. This generator helps you create robust schemas optimized for SQL Server 2016+ and Azure SQL Database.
SQL Server Data Types & Best Practices
SQL Server’s data type system is designed for business applications. Integer Types: INT (32-bit), BIGINT (64-bit), SMALLINT (16-bit), TINYINT (8-bit) with IDENTITY for auto-increment. Numeric Types: NUMERIC and DECIMAL for precise financial calculations with configurable scale and precision. String Types: VARCHAR for ASCII up to 8KB, NVARCHAR for Unicode (best for international), TEXT for >8KB (deprecated, use VARCHAR(MAX) instead). Temporal Types: DATE for calendar dates, DATETIME for timestamp precision (to 3ms), DATETIME2 for higher precision (to 100ns). Binary Type: VARBINARY for binary data, IMAGE for large binary (deprecated). Specialized Types: UNIQUEIDENTIFIER (GUID) for distributed applications, BIT for boolean, JSON for document data, MONEY/SMALLMONEY for currency.
IDENTITY vs. UNIQUEIDENTIFIER Patterns
IDENTITY Pattern: Use INT or BIGINT with IDENTITY(1,1) for simple auto-increment primary keys. Fast, compact, but requires coordination in distributed systems. Ideal for single-server applications and data warehouses. UNIQUEIDENTIFIER (GUID) Pattern: Use UNIQUEIDENTIFIER DEFAULT NEWID() for globally unique keys without database coordination. Necessary for replicated databases, distributed systems, and multi-region deployments. Larger storage footprint but guarantees uniqueness. Hybrid Pattern: Use IDENTITY for clustering, UNIQUEIDENTIFIER for global identification. Example: Use INT IDENTITY as primary key, UNIQUEIDENTIFIER as application key for APIs.
Real-World Enterprise Scenarios
Financial Systems: Design schemas with DECIMAL(19,4) for currency, CHECK constraints for positive amounts, DATETIME2 for transaction precision, and audit tables with INSERTED/DELETED triggers. Healthcare Applications: HIPAA-compliant schemas with VARBINARY for encrypted PII, foreign keys for referential integrity, and temporal tables for audit compliance. Multi-Tenant SaaS: Design with TenantId columns, row-level security (RLS) for isolation, and computed columns for tenant-specific calculations. E-Commerce Platform: Product tables with JSON for variants, Orders with DATETIME2, Inventory with computed columns for stock levels, and proper foreign keys. BI & Data Warehouse: Fact tables with BIGINT for measures, Dimension tables with surrogate keys, and temporal columns for slowly changing dimensions.
Instead of recalculating in application code, use computed columns:
FullName AS (FirstName + ' ' + LastName)
Make them PERSISTED for indexing. This centralizes business logic at the database layer.SQL Server Performance Optimization
1. Choose Correct Data Types: Use SMALLINT/TINYINT for small values, INT for normal ranges, BIGINT only when needed. VARCHAR length impacts storage – use appropriate limits. 2. Clustering Strategy: Clustered index should be IDENTITY or sequential DATETIME2 to avoid page splits. 3. Non-Clustered Indexes: Create on frequently filtered/joined columns. INCLUDE columns for covering indexes to avoid lookups. 4. Partitioning: For tables >1GB, consider range partitioning by date or category for maintenance and performance. 5. Statistics: SQL Server auto-creates statistics; enable AUTO_CREATE_STATISTICS and AUTO_UPDATE_STATISTICS for optimal query plans.
Azure SQL Database Compatibility
This tool generates DDL that works seamlessly with Azure SQL Database. SQL Server schemas migrate to Azure with minimal changes – connection strings differ, but schema syntax is identical. Leverage Azure features like automatic failover, read replicas, and elastic pools without schema changes. Temporal tables for time-travel queries, JSON support for semi-structured data, and dynamic data masking for security all work out-of-the-box.
SQL Server Schema FAQ
Use DATETIME for legacy compatibility (3ms precision, 8 bytes). Use DATETIME2 (7) for new applications (100ns precision, 8 bytes, same storage). DATETIME2 is more precise and recommended for modern SQL Server.
VARCHAR stores ASCII (1 byte/char), NVARCHAR stores Unicode (2 bytes/char). Use NVARCHAR for international support, VARCHAR for ASCII-only. NVARCHAR is standard for global applications.
Computed columns are virtual columns calculated from other columns. Use AS expression syntax. Can be PERSISTED (stored) for indexing. Great for derived values without application logic.
No, you cannot change IDENTITY properties directly. You must drop and recreate the column. Use ALTER TABLE ALTER COLUMN for other properties. Plan IDENTITY carefully at creation.
Sparse columns optimize storage for columns with many NULLs. Transparent data encryption (TDE) encrypts entire database. SQL Server supports Always Encrypted for column-level encryption at application layer.
Store JSON in VARCHAR(MAX) or NVARCHAR(MAX) columns, then use JSON_VALUE, JSON_QUERY for extraction. Use JSON_MODIFY for updates. Create indexes on JSON properties for performance.