What Are Physical Data Models and Why Do They Matter?

Hey there! As a fellow data geek, I know you‘ll appreciate this comprehensive guide I‘ve put together on physical data modeling. Proper database design has fascinated me for years, so I‘m excited to share everything I‘ve learned about building efficient physical models.

In this post, we‘ll dig into what physical data models are, why they‘re critical for database success, and how to design effective ones. I‘ll also compare physical models to conceptual and logical models, and provide tons of learning resources to further your skills. So whether you‘re an aspiring data pro or seasoned database developer, you‘ll find this guide helpful. Let‘s get started!

Physical data models describe how data is stored in database management systems. They capture the actual database schema used to build databases.

As you can imagine, physical models contain really nitty-gritty technical details about database internals—like tables, columns, data types, keys, indexes, constraints, and more. It‘s the blueprint for developers to take a logical model and implement it in an actual database.

So why take the time to build physical data models? There are several key reasons:

Transforms Logical Models into Database Schemas

Physical models bridge the gap between conceptual thinking and physical database construction. They take a logical model as input and generate database schemas as output.

Enables Database Performance Optimization

With physical models, developers can specify storage parameters, partitions, clustering, indexing, and other optimizations to improve speed and efficiency.

Allows Collaborative Database Development

Physical models provide a shared specification for developers, DBAs, architects, and others to implement databases together.

Serves as Database Documentation

The diagrams and metadata in physical models record the final database design for future reference.

Facilitates Change Management

When modifying databases, models help teams understand the impact of schema changes.

Minimizes Costly Re-work

Solid modeling minimizes redesign work caused by poor planning. The time invested in modeling pays off exponentially.

In short, physical data modeling is mandatory for building production-ready databases that perform well. It‘s a core discipline of database development.

Now that you know why physical models matter, let‘s look at what comprises them. Here are the core components:

Tables

This includes physical tables, names, columns, relationships, constraints, keys, etc. It‘s the core schema.

Columns

The attributes of each table, specifying data types, nullable options, lengths, precision, scales, and default values.

Primary Keys

The primary key columns that uniquely identify each row in a table.

Foreign Keys

Foreign key columns that reference primary keys in other tables.

Indexes

Indexes to improve query performance like B-trees and hashes.

Partitions

Horizontal partitioning specifications to divide tables across storage.

Clusters

Instructions for cluster storage to collocate related data.

Tablespaces

Tablespaces that group related tables and indexes logically.

Triggers

Triggers that fire on data modification events.

Stored Procedures

Routines to encapsulate business logic in the database.

Views

Virtual tables representing queries and simplifying access.

Owners

User accounts that own the database objects.

Permissions

Access permissions granted to users and roles.

Storage

Physical storage parameters like files, filegroups, tablespaces, etc.

This comprehensive set of metadata is everything needed to construct databases and make them scream.

So how do skilled data modelers methodically build physical models? Here‘s a step-by-step process:

1. Gather Requirements

Interview business and technical teams to understand functionality, scalability, security, and storage requirements. Document findings in a modeling workspace.

2. Review Logical Models

Examine associated logical models, business rules, expected usage patterns and data volumes. Start mapping these to physical constructs.

3. Map Entities to Tables

Convert logical entities and attributes into physical tables, columns, relationships, keys, indexes, etc.

4. Specify Column Attributes

Define precise data types, length, precision/scale, nullable options, default values, collation, and other column attributes based on usage.

5. Optimize Table Design

Analyze access patterns to optimize tables via partitioning, compression, containment, row vs. page storage, etc.

6. Review Indexing Needs

Recommend selective indexes to improve common query and join performance based on access criteria.

7. Design Supporting Objects

Model supporting objects like sequences, stored procedures, functions, triggers, views, etc. based on requirements.

8. Determine Storage and Security

Define physical storage locations, encryption schemes, access permissions, etc. per security and compliance needs.

9. Generate DDL Statements

Use tools to automatically generate DDL statements from the model to build the physical database schema.

10. Review and Refine

Conduct exhaustive model reviews with stakeholders to confirm accuracy and completeness. Refine iteratively.

This methodical approach helps produce high-quality, production-ready physical models.

It‘s helpful to contrast physical models with logical and conceptual models:

Model Type	Focus	Details Level	Audience
Conceptual	Business needs	High-level	Business users
Logical	Structure and relationships	Technical	Data architects
Physical	Database schema and objects	Full implementation	DB developers

As you can see, each model serves a different purpose and level of abstraction. Conceptual models focus on business needs. Logical models add technical details. Physical models include full database implementation details.

Different audiences utilize each model type during the database design process too. business users validate conceptual models. Data architects leverage logical models. And physical models are the realm of database developers/administrators.

Nobody starts out an expert data modeler. It takes years of experience to master. Here are some best practices I‘ve found that really help:

Use Standard Notations

Stick to notations like IDEF1X or UML to leverage proven, understandable designs.

Take a Top-Down Approach

Follow the conceptual, logical, physical modeling sequence for completeness.

Apply Normalization Judiciously

Normalize to avoid redundancy but don‘t over-normalize causing performance issues.

Record Metadata and Business Rules

Capture column meanings, data rules, stewards, etc. in the model.

Leverage Design Patterns

Reuse standard modeling techniques where suitable.

Model Supporting Constructs Too

Don‘t just model tables but also other database objects like indexes, procedures, etc.

Peer Review Models Extensively

Collaborate on model reviews to improve quality and accuracy.

Use Version Control

Maintain models in version control for team collaboration.

Automate What You Can

Use tools to auto-generate DDL statements from models.

Refine Models Iteratively

Data modeling is a continuous, iterative refinement process.

If you adopt these best practices, you‘ll avoid common data modeling mistakes and accelerate your learning.

Looking to improve your physical modeling skills? Here are some of my favorite learning resources:

Books

• The Data Model Resource Book (Vol 1, 2 & 3) by Len Silverston: The data modeling bible—1,500 pages of comprehensive modeling knowledge.

• Database Modeling and Design: Logical Design by Toby Teorey: Great textbook introducing conceptual, logical, and physical modeling.

• Physical Database Design by Sam Lightstone: Expert techniques for database performance tuning via physical design.

Online Courses

• Data Modeling Masterclass (Udemy): One of the highest-rated data modeling courses covering all model types with demos.

• Physical Database Design for Performance (Pluralsight): Focuses on SQL Server performance optimization via partitions, indexes, compression, etc.

• Data Modeling in MongoDB (MongoDB University): Intro to modeling document and relationship data in MongoDB NoSQL databases.

Data Modeling Tools

• Erwin Data Modeler: The industry-leading (but expensive) data modeling tool. Has extensive features for enterprise-level modeling.

• SQL Power Architect: Feature-rich open source data modeling tool that supports modeling for multiple databases.

• Visio: Provides data modeling diagramming shapes and templates. A basic but handy tool if you already use Visio.

• Lucidchart: Cloud-based visual modeling and diagramming tool with database design features and real-time collaboration.

With the right tools and dedication to continuous learning, anyone can master physical data modeling over time. The investment will pay off with impactful contributions to building well-architected databases.

I hope you found this guide helpful! Let me know if you have any other physical data modeling questions.