Data model
A data model is a structured representation of data that defines its entities, attributes, relationships, and applicable constraints.
The Design of Web APIs, Second Edition
A data model is a structured description of the data accepted and returned by an API operation.
- How it is used
- Shapes REST resource data and operation inputs, outputs, and errors after capabilities receive HTTP representations.
- How deep it goes
- Explains model derivation, model variants, consumer-fit checks, embedding choices, and JSON Schema description, while deferring later optimization concerns.
- Examples
- Uses Product and Bank Account models, derived CRUD schemas, embedded owners, balances, transactions, and reusable OpenAPI schemas.
- What you can do after
- Build it from scratch by deriving consumer-ready operation schemas from complete resource models and describing them with JSON Schema in OpenAPI.
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The Well-Grounded Data Analyst
A formal representation of the entities, attributes, relationships, and constraints in a data domain.
- How it is used
- Standardizes recurring business logic between raw data and analysis so downstream reporting and self-service use shared definitions.
- How deep it goes
- Works through cleaning, entity definitions, deduplication, reshaping, grain, schema, and completeness trade-offs rather than formal modeling theory.
- Examples
- Uses customer consolidation across three sources and football results represented in wide and long formats.
- What you can do after
- Build a reusable customer dataset by consolidating sources, selecting a grain, encoding definitions, and communicating uncertainty.
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Web Components in Action
A structured representation of the data and relationships an application manages.
- How it is used
- Organizes application state outside rendering components and coordinates shared data flow across Web Components.
- How deep it goes
- Defines models and demonstrates getters, mutations, persistence, derived values, and playback, without detailing storage or event-system internals.
- Examples
- Uses an exercise library, workout-plan object, localStorage-backed saved plans, and playback controls as concrete model examples.
- What you can do after
- Build it from scratch as a JavaScript module that centralizes application data, exposes operations, and coordinates component access.
Effective Data Analysis
A data model is a defined representation of data entities, attributes, relationships, and organization for a particular analytical or operational purpose.
- How it is used
- Organizes warehouse data around recurring analytical questions so users spend less time joining, filtering, and correcting sources.
- How deep it goes
- Explains collaborative model design and star-schema criteria, but does not provide a complete table-building procedure.
- Examples
- Uses transactions and page views as facts, plus weather joins, forecast history, and weekly dashboard aggregates.
- What you can do after
- Evaluate/understand whether an analytics model supports stakeholder questions with meaningful facts, useful attributes, agreed calculations, and manageable query work.
MongoDB 8.0 in Action, Third Edition
A data model is a representation of the data structures, fields, and relationships that an application stores and accesses.
- How it is used
- Organizes MongoDB documents around application access patterns to support efficient retrieval, scalability, and maintainability.
- How deep it goes
- Works through workload assessment, relationship mapping, pattern selection, indexes, and validation; defers index details.
- Examples
- Uses an airline route workload table and route and airport documents to illustrate subset and computed patterns.
- What you can do after
- Build it from scratch by recording a workload, mapping relationships, selecting patterns, and indexing important queries.
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Python How-To
A data model is a structured representation of the entities, attributes, and relationships in an application domain.
- How it is used
- Represents application data and its related operations, with the representation chosen to serve specific features.
- How deep it goes
- Compares container trade-offs and develops feature-first class design, then uses UML to inspect classes that have grown too complex.
- Examples
- Uses tasks with titles, descriptions, urgency, and status, plus places with names and coordinates.
- What you can do after
- Build it from scratch by identifying required features, selecting suitable containers or classes, and revisiting the design as needs change.
Rust Servers, Services, and Apps
A data model is a structured specification of the data entities, fields, and relationships represented by a software system.
- How it is used
- Shapes course and tutor data as it moves through HTTP handlers, Rust structs, and PostgreSQL tables.
- How deep it goes
- Works through field definitions, JSON conversion, optional values, row mapping, and model refactoring rather than a general modeling method.
- Examples
- Uses a seeded course table, a `Course` struct, and separate registration-form, tutor-response, and user structs.
- What you can do after
- Build it from scratch: define operation-specific Rust structs and align them with HTTP payloads, PostgreSQL columns, and query results.
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Everyday Data Visualization
A data model is an organizational structure that specifies the kinds of data to store and how each data item is described.
- How it is used
- Aligns data gathering, metric requirements, and visualization design before dashboard decisions harden.
- How deep it goes
- Defines data models and compares tidy, aggregate, and tall table shapes without detailing implementation internals.
- Examples
- Uses fruit logs and hotel dashboard metrics to contrast observation-level, aggregate, and metric-requirement tables.
- What you can do after
- Apply it via Tableau, Power BI, Python, R, or d3 by choosing a structure suited to required aggregation and visuals.
Secure APIs
A data model is a structured representation of entities, attributes, relationships, and constraints, potentially including validation rules for accepted data.
- How it is used
- Uses schemas to constrain API input and output, prevent corruption, and keep untrusted data from reaching application state.
- How deep it goes
- Explains required and optional properties, enumerations, composition, validation errors, and separate input-output models through focused API examples.
- Examples
- Demonstrates character statuses, printed and electronic books, and payments with identifiers, amounts, currencies, and statuses.
- What you can do after
- Builds API schemas that validate values, separate representations, reject unsafe fields, and expose failures for diagnosis.
Pro Angular 16
A data model is a representation of the entities, properties, and relationships in an application's domain.
- How it is used
- Starts Angular application development by defining the domain data that components and templates use.
- How deep it goes
- Works through descriptive classes, an in-memory data source, and repository operations; defers RESTful service integration in the store example.
- Examples
- Uses a to-do item and list, then a product catalog with sample data and repository methods.
- What you can do after
- Build it from scratch using TypeScript classes, a sample data source, and product repository operations.
Pro ASP.NET Core 7, Tenth Edition
A data model is a structured representation of the real-world objects, processes, rules, and data that constitute an application's domain.
- How it is used
- Provides domain objects for ASP.NET Core features that accept user data, compose responses, and access stored records.
- How deep it goes
- Works from a single class through related entities, context properties, and schema migrations; leaves detailed ORM query behavior to further investigation.
- Examples
- Uses an RSVP response, a product catalog, shipped orders, and calculations to illustrate models of increasing scope.
- What you can do after
- Build it from scratch with C# model classes and an Entity Framework Core context, then evolve database-backed models with migrations.
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ASP.NET Core in Action, 3e
A database model represents the application data stored in a database and the structure used to work with that data.
- How it is used
- Supports storing, querying, creating, and updating application data in ASP.NET Core applications.
- How deep it goes
- Explains entities, DbContext, providers, dependency injection, and migrations, but defers advanced EF Core customization.
- Examples
- Uses a recipe API with recipes, ingredients, RecipeService, migrations, and an Identity-enabled ApplicationUser.
- What you can do after
- Build a small EF Core data model, synchronize its database schema, and isolate data access in services.
First-Party Data Activation
A data model is a structured representation of entities, attributes, and relationships used to organize and work with data.
- How it is used
- Organizes first-party customer data for audience creation, segmentation, campaign analysis, and activation.
- How deep it goes
- Compares ER, dimensional, and data vault approaches and their trade-offs without providing a schema-building procedure.
- Examples
- Uses churn and recent-purchaser audiences, linked customer tables, and star-versus-snowflake schema trade-offs.
- What you can do after
- Evaluate/understand which modeling approach fits marketing queries and why initial use cases can precede a comprehensive model.
The Art of Code
A data model is a representation of the information, structure, relationships, and constraints that a software system manipulates.
- How it is used
- Guides code design by making domain data explicit before arranging behavior around it.
- How deep it goes
- Develops practical modeling and refactoring principles, but stops short of prescribing a rigid universal method.
- Examples
- Uses ambiguous user attributes, typed height and weight values, immutable carriers, and refactoring inspection prompts.
- What you can do after
- Apply it by replacing ambiguous structures with precise, proportionate data types and simpler behavior.
Automating API Delivery
A data model is a representation of the data entities, attributes, relationships, and constraints handled by a system or API.
- How it is used
- Aligns API designers and stakeholders on resources, relationships, terminology, and operations before API definitions are created.
- How deep it goes
- Defines conceptual entities and relationships, represents them with diagrams or tables, and stops short of implementation-level schemas.
- Examples
- Uses a Product Catalog API with products, reviews, categories, cardinalities, relationship diagrams, and an operations table.
- What you can do after
- Evaluate/understand API resource boundaries and relationships, then communicate them through diagrams, tables, and operation definitions.
Functional Design and Architecture: Examples in Haskell
A database model is a representation of how data is structured and related within a database's internal storage system.
- How it is used
- Separates storage representation from domain logic while supporting key–value and relational persistence.
- How deep it goes
- Defines database models, demonstrates serialization and conversion, and addresses database types and key design.
- Examples
- Uses meteor records, KVDBMeteor JSON rows, DBMeteor SQL fields, conversion functions, and primary-key lookup.
- What you can do after
- Apply it via explicit database entities and conversion functions that isolate persistence code from business logic.
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Delivering Machine Learning Projects: From design to deployment
A data model is a formal representation of the entities, attributes, relationships, and constraints represented by data.
- How it is used
- Uses data-model understanding to establish ML readiness and reduce uncertainty before project delivery begins.
- How deep it goes
- Requires documentation and inspection of real data characteristics, but stops short of describing modeling or implementation internals.
- Examples
- Uses a short ML-readiness project, documented data-model work, extraction and inspection, and contingency estimates as practical artifacts.
- What you can do after
- Evaluate data readiness and plan an ML project with informed expectations, governance provisions, and explicit contingency.
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Data-Oriented Programming: Reduce software complexity
A data model is a structured representation of the data entities, fields, values, and relationships in a system.
- How it is used
- Separates data design from code while organizing entities, fields, values, and relationships for generic manipulation.
- How deep it goes
- Descends from entity diagrams to record structures, relationship representation, information paths, and runtime field changes.
- Examples
- Uses a Library Management System with Book–Author associations, identifier collections, positional collections, and indexed records.
- What you can do after
- Apply it via generic functions such as Lodash get, has, merge, values, filter, and map.
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Essential TypeScript 5, Third Edition
A data model is a structured representation of an application's data and the operations that can be performed on it.
- How it is used
- Structures to-do items and product orders so application code can store data and perform operations on it.
- How deep it goes
- Builds typed entities and collection methods, then demonstrates a compiler error; defers fuller explanation of classes and JavaScript types.
- Examples
- Uses a to-do list with add and completion operations and a product order with quantities and calculated line totals.
- What you can do after
- Build it from scratch as typed TypeScript entities with operations, and recognize a mismatched method argument through compiler feedback.
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Microsoft Azure in Action
A data model is a conceptual or structural representation that defines how data and its relationships are organized.
- How it is used
- Guides the organization, distribution, and retrieval of large, changing datasets in Azure Cosmos DB.
- How deep it goes
- Explains schema flexibility, denormalization, and partitioning with trade-offs, while stopping short of detailed partition-key design.
- Examples
- Uses product documents containing supplier, availability, sizes, colors, ratings, and images to illustrate flexible and embedded data.
- What you can do after
- Apply it via Azure Cosmos DB by designing flexible documents, embedding related data, and selecting partitioning strategies for efficient access.
Graph Neural Networks in Action
A data model is a formal structure that specifies the entities, attributes, relationships, and constraints of a dataset or information system.
- How it is used
- Guides the transformation of domain data into graph structures that downstream analytical systems can query reliably.
- How deep it goes
- Explains a practical design, testing, and refactoring workflow, while deferring detailed implementation of individual graph storage structures.
- Examples
- Uses recruitment referrals, JSON representations, candidate datasets, graph datasets, and published semantic models as concrete artifacts.
- What you can do after
- Build it from scratch by defining, testing, and refactoring a graph model for a domain-specific analytical workflow.
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Digital Twins in Action
A data model is an abstract structure that defines how data entities, relationships, and constraints are represented and organized.
- How it is used
- Structures digital-twin entities and relationships so storage choices and later analysis can be planned.
- How deep it goes
- Explains the consequences of schema choices and inconsistent representations, but does not specify a complete schema.
- Examples
- Uses mismatched bank customer records and a Python fuzzy-matching listing to illustrate the problem of linking representations.
- What you can do after
- Evaluate/understand it by checking whether proposed entities, relationships, and granularity support the intended queries and analysis.
Build Python Web Apps with Streamlit
A data model is a structured representation of an application's entities, attributes, relationships, and storage organization.
- How it is used
- Designs an application's entities, relationships, attributes, and relational database schema before implementing CRUD operations.
- How deep it goes
- Explains ER diagrams and table conversion, including primary and foreign keys, without detailing more complex real-world models.
- Examples
- Uses users and haikus with usernames, haiku text, authors, numeric IDs, creation times, and a foreign-key relationship.
- What you can do after
- Apply data modeling to design tables and relationships for CRUD applications using relational databases such as PostgreSQL.
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Related concepts
- schemaproduces · A data schema is a formal specification of the structure, fields, types, relationships, and constraints of data.
- MVC modelhas part · An MVC model is the application component that represents application data and associated domain logic, making relevant state and behavior available to other components such as views.
- Pydanticused by · Pydantic is a Python library for defining typed data models and schemas, validating and parsing data against them, and serializing validated values.
- Model–view–controllerpart of · Model–view–controller is an architectural pattern that separates application data and behavior, presentation, and interaction handling into model, view, and controller responsibilities.
- datasetkind of · A dataset is an organized collection of related data items, records, or observations treated as a unit for storage, processing, analysis, or computation.
- repository patternhas part · The repository pattern is a software design pattern that abstracts access to and persistence of application or domain data, hiding storage-specific operations.
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