Mandy knows something is missing. The logical model she approved is flawed. It seems to fit the business requirements, but Dave in development detected indexing issues. If left unresolved, they might affect database performance.
She discovers the index is bulky when bucketing transactions could have saved storage. Maybe it’s time for a team refresher on the best practices of logical modeling. Let’s first get the prototype out, she decides, as she sets up a team meeting in her calendar.
Compare Embedded Analytics Software Leaders
Key Points
- What Are Logical Data Models?
- Benefits
- Data Model Types
- ERD vs. UML
- How to Create an ERD
- How’s NoSQL Database Designing Different?
- Tools
- Challenges and Best Practices
- Next Steps
Database design is the linchpin for big data and embedded analytics, and enterprise reporting. Capturing every client requirement in data models is crucial. The design for how the database should store, move and use business information decides how your applications will work.
Let’s first define what logical models are and explore their benefits and best practices.
What Are Logical Data Models?
A logical model is a representation of the structure of your database. Describing the structure of each entity regarding its attributes and relationship with other items makes up logical modeling.
It’s a critical part of requirements gathering and the need-analysis process. Much like an architect’s building plan, a logical model is a schematic representation of how the database will link related information and serve it in response to queries.
A lot of hard work goes into database design, and industry experts swear by the three stages of structural modeling – conceptual, logical and physical.
- As a data architect, start with a basic map of what the data structure should look like — that’s the conceptual model.
- The logical model is a more detailed view of the items and their mutual relationships.
- Physical modeling involves coding and is a developer task.
If it’s a database for an eCommerce application, the product owner might say something like, “I want to see inventory and sales workflows within my CRM software.”
The data architect is thinking something like this.
- Customer, product, inventory and sales need linking based on how they mutually relate.
- How will we get buyer information into the database?
- The repository must integrate with the client’s inventory and sales management systems.
- The information must be in a proper structure and organized for fast access.
- Which query language will work? Are direct query methods part of the design requirements?
- Does the client want write-backs?
A data architect works with the business analyst to finalize the database structure. The two roles are so closely related that you might find yourself doing a bit of both.
More in-depth questions might crop up as you get to work.
- Where will the information be stored? In which table and column?
- How can I group it?
- What are the various product categories?
- How are the various columns related?
- Is the category code the same as prod_cat? (Redundancy is a beast.)
- Do columns have a key or a label?
Logical tables keep data compliant and abstract the technicality from you. C_fnam in the physical model might not make sense, but customer-first-name in the logical model does. Product owners aren’t as technical, and familiar references are easier for them to follow.
But that’s not the only reason data architects use logical models.
Primary Benefits
By defining the database structure, logical modeling supports business intelligence, analytics, metadata management and data governance.
- Logical models support decision-making with complete, consistent and reliable databases. They clearly show how the business functions and the information that supports it.
- A robust design convinces clients of a quality product, so they’re more likely to invest. It’s why car makers, device manufacturers and technology vendors provide product specifications. It’s good marketing — when you know what makes the product work, you’ll know if it’s a fit.
- The database design document serves as a blueprint for future maintenance and fixes.
- It promotes collaboration, helping teams align key business functions with the structure. A standardized information structure across your organization gives teams a common language. Employees can be more self-reliant and maximize value gains.
- You can spot errors and redundancies before coding starts.
- Enforcing object-level security becomes a reality when you bake in masking and encryption at the model level.
Data Model Types
A data model moves the conception stage to the logical and physical phases, with the data structure becoming more granular.
Conceptual Model
A conceptual model is a basic diagram that outlines the business requirements by showing the data types and their correlations. It has a business context and is easily readable with common business terms like product ID, customer name and area code.
Logical Model
The information becomes more technical as you move from concept to the logic stage. It involves defining the attributes or characteristics of each item that needs coding. For instance, the item product might have the attributes product code, name, category and manufacturing date. Customer attributes can be name, address, phone number and loyalty ID.
A logical model has the following characteristics.
- It’s not a database or a DBMS (database management system).
- It’s independent of database management technology.
- A logical model is independent of physical storage devices like file systems. Its attributes are precise and contain information on data types with exact lengths.
- Primary and secondary keys aren’t defined at this stage.
Physical Model
The logical model changes hands — it’s up to developers to interpret them and create the code. Based on their understanding, they define the physical structure. It’s a critical phase as experienced developers can identify if the logic is flawed, and then it’s back to the drawing board.
Validating the logical model with a keen eye before pushing the design for coding is a great way to stay agile.
What Is Normalization?
Normalization aims to create a well-structured, scalable and flexible database schema optimized for efficient information access and maintenance. It includes preventing duplication, inconsistencies and inaccuracies in data.
As a data architect, it’s important to understand normalization and its various normal forms to apply them properly toward creating a robust and reliable database.
Normalization is achieved through a series of stages or normal forms, each building on the previous one.
- The first normal form (1NF) intends that information be atomic, meaning that each value in a field represents a single, indivisible piece of information.
- The second normal form (2NF) provides that each non-key attribute depends on the primary key.
- The third normal form (3NF) removes transitive dependencies where a non-key attribute depends on another non-key attribute.
Beyond 3NF, higher normal forms, such as Boyce-Codd normal form (BCNF) and the fourth normal form (4NF), address more complex structures and relationships.
As a data architect, it’s crucial to understand that database design significantly determines information access speed. So, employing efficient logical models that drive quick query responses is a good practice.
Two industry-standard techniques for designing information structures are Entity-Relationship diagrams (ERDs) and Unified Modeling Language (UML).
ERDs vs. UML
Entity-relationship diagrams have been around since the 1960s, and they provide a visual representation of database entities, attributes, relationships and keys.
- Entities represent the data objects within an organization, like customers, teams and products, and can be classified as strong, weak or associative.
- Relationships demonstrate how entities are related to each other. Read more ahead.
- Attributes define each entity’s properties and can be multivalued or derived.
- Keys are essential components of ER diagrams as they help to organize the diagram and maintain its integrity. Primary keys define a unique instance of an entity, while foreign keys link one entity to another in a one-to-one or one-to-many relationship.
As a data architect, it’s important to have a deep understanding of ER diagrams and UML to design efficient and effective structures.
Relationships
The relationships between database entities can be manifold: one-to-one, one-to-many and many-to-many.
In an inventory information store, a single product category can have multiple product details. It’s a one-to-many relationship.
Data architects use Chen, Crow’s foot and Barker notations to identify the strength of entities and their cardinality in relation to other linked entities.
A login ID is usually associated with a single email address which is a one-to-one relationship. As a data architect, enforcing relationships in the database maintains information integrity and prevents inconsistencies.
One of the most widely used tools for database designing is the Unified Modeling Language. It provides a standardized set of notations, shapes and symbols for establishing structure and correlations. UML diagrams allow developers to communicate complex software designs simply and intuitively using symbols.
Two of the most common UML diagrams are class and sequence schematics.
- A class diagram visually represents classes, interfaces and their relationships within a software system. It’s a critical building block of object-oriented modeling, showing how different components interact mutually.
- Sequence diagrams show the order in which different operations execute and how they relate.
As a data architect, it’s essential to be familiar with these UML diagrams and their applications in software design and development.
How To Create an ERD
Database design and software development are two distinct processes that follow different steps and principles. However, some steps may overlap.
- Gather Business Requirements: Identify and understand the information types to be stored and any specific requirements to keep in mind for storing and processing historical information.
- Analyze Processes: Study the business roadmap and how the organization expects to change and prepare for upcoming changes.
- Identify the Components: Break down the information types into individual entities and identify their relationships.
- Build an ER Diagram: Create a visual representation of the entities, attributes, primary keys and relationships.
- Create a Physical Model: Convert the ER diagram into a physical model that defines the database’s tables, columns and information types.
- Validate: Check that the ER diagram and physical model accurately represent the data and entity relationships.
- Generate the Data Definition Language (DDL): Use the physical model to create the DDL for deploying the database schema.
ERDs are great for designing relational databases, but what about non-relational information?
How’s NoSQL Database Modeling Different?
NoSQL (Not Only SQL) databases offer fast and efficient information access by moving away from traditional row-and-column storage models. They can have document, key-value, wide-column and graph-based structures. But, organizing non-SQL can be challenging.
NoSQL database design requires a logical model that evolves and adapts to meet changing business requirements and query patterns. But there are challenges.
As real-time information comes in, multiple update commands can slow down performance. Bucketing transactions can keep the index lightweight and improve performance, but it may require a different storage method and changes to the logical model.
Here are some best practices.
- Reviewing actual user queries, their responses and turnaround times help guide the modeling process by identifying NoSQL patterns.
- It’s essential to consider business requirements such as the frequency of record updation and index size.
- Understanding the query languages associated with each type of NoSQL database is crucial. For instance, key-value databases respond to direct queries rather than a query language.
Designing a logical model for a NoSQL database requires a dynamic approach that considers both current and future business requirements.
Tools
A data architect should be familiar with the various platforms that support logical modeling, coding and prototyping for software development. These platforms streamline the development process with design wizards and various symbols and tools for database design.
A screenshot of MySQL Workbench. Source
These software systems go by various names and support information modeling, directly or indirectly.
- Diagramming tools, including Lucidchart, MindManager
- Data modeling software like erwin Data Modeler
- Change management solutions such as FreshService
- Web development platforms, including Shopify, WordPress, Wix
- Document management systems like Connecteam
- Code generator software like Polycoder, Cogram, Tabnine, OpenAI Codex
- Flowcharting tools, including Kissflow, Asana, Jira, GoodDay
- Prototyping and mockup tools like Mockplus, Balsamiq
- Quality Assurance tools such as Bugzilla, Jira
- Database design tools, including dbForge Studio, MySQL Workbench
As a data architect, it’s essential to have a working knowledge of industry-standard design software solutions and their features.
Refer to our Jumpstart Platform for readymade scorecards for the leading database design systems.
Challenges and Best Practices
It takes time to build great things. Rome wasn’t built in a day. Database design requires patience, a keen eye for detail and thoroughness. Neglecting even one aspect of the design process can cause longer delivery cycles, frayed tempers and team-wide frustration.
Prevent heartburn by avoiding the following mistakes and adhering to some example best practices.
- Ignoring Developer Feedback: Failure to heed developer inputs can result in a flawed database design.
Best Practice: Track the design through the coding phase, and don’t hesitate to invite responses from developers. It can tell you volumes about your database design. Fresh eyes bring new perspectives and uncover insights you might not see.
- Neglecting Use Cases: Ignoring common use cases about storing and using information can lead to a suboptimal database design.
Best Practice: Pay attention to how the database will ingest information and at what rate. What are the maximum information volumes to be moved or processed at any given time? How much throughput is acceptable before it starts impacting performance?
- Inefficient Normalization: Not applying the rules of normalization can lead to chaos when adding or updating records. Inaccuracies and inconsistencies can creep in, resulting in inefficient storage and slower queries.
Best Practice: Applying normalization helps avoid common information-related issues like duplication, selective updating and inadvertent deletion.
- Allowing Redundancy: Keeping duplicate or unnecessary fields and tables can increase the size of the database, slowing down performance.
Best Practice: Keep a strict watch on extra fields and tables to avoid unnecessary overheads. Follow the agreed-upon business requirements and technical specifications documents to avoid losing critical tables and fields.
- Ignoring Constraints: Adding few to no constraints can make the model susceptible to human error. Constraints play a vital role in information integrity.
Best Practice: Include constraints to your priority checklist when building logical models.
- Ignoring Database Engine Features: Not taking advantage of the database engine features — indexes, aggregate functions, constraints, functions and triggers — can result in a subpar design.
Best Practice: Work with what you have — assess your resources. You already have a database engine that gives you efficient views, automatically denormalizing data for queries.
- Poor Indexing: Improper indexing is the bane of databases, making information difficult to trace and resulting in sluggish responses. Keeping indexes synchronized with the tables they point to can be tricky.
The larger your index, the more processing power the system must use. It’s bad design, and Mandy’s team realizes it only too well now.
Best Practice: Be strict about what you include in the index by bucketing transactions. Remove clutter and keep it lightweight.
Also, index efficiency depends on the table’s information type. If your table stores numerical data, it’s bound to be more efficient than tables that store characters, dates or decimals. Be discerning when deciding on the information type and model accordingly.
Next Steps
Logical data models provide a structured approach to designing a database that meets the client’s needs. Identifying relationships between entities helps organize information, prevent redundancies and improve its quality.
If you’re involved in database development, developing logical modeling skills with the right software is essential. Get our free comparison report for database design software to learn which platforms are the best fit.
Which data modeling software do you use? How has it worked out for you? Let us know in the comments!