Data type
A data type classifies values and specifies their representation or interpretation and the operations applicable to them.
ScyllaDB in Action
A data type specifies the kind of values that a database column can store and the operations applicable to those values.
- How it is used
- Assigns column representations after query-first design identifies the fields required by restaurant-review tables.
- How deep it goes
- Explains basic, temporal, identifier, collection, and user-defined types with selection trade-offs, without detailing their storage internals.
- Examples
- Uses a Docker-backed types keyspace, a basic CQL table, integer inserts, an out-of-range error, and a scaled restaurant-review score.
- What you can do after
- Apply it via CQL by choosing column types whose value semantics, bounds, and operation behavior fit application requirements.
Learn SQL in a Month of Lunches
A data type classifies values and determines the operations and rules applicable to them.
- How it is used
- Guides table design, filtering, query combination, and parameter declarations by identifying the kinds of values SQL handles.
- How deep it goes
- Explains practical type selection and matching through tables, variables, and procedure parameters, without detailing type internals.
- Examples
- Uses CategoryID as int, changing a MySQL variable from 1 to a title string, and promotion procedure parameters.
- What you can do after
- Apply it by choosing column types and matching variable or procedure parameter types to the columns they evaluate.
Read how Learn SQL in a Month of Lunches explains data type →
Building User-Friendly DSLs
A specification of the kind of value or relation that a property can store.
- How it is used
- Defines permissible property values and relations, then supports type checking that prevents invalid DSL expressions.
- How deep it goes
- Develops domain value types into computed expression types and checking constraints, briefly deferring compatibility beyond equality.
- Examples
- Uses car-rental amount, percentage, and date-range attributes, plus a Number projection rendered with $ or %.
- What you can do after
- Build it from scratch by representing types, computing expression types, and comparing them in checking constraints.
Elasticsearch in Action, Second Edition
A data type is a mapping specification that determines how a document field's values are represented, indexed, and queried in Elasticsearch.
- How it is used
- Defines field behavior within mappings so indexing and search apply appropriate rules.
- How deep it goes
- Explains mapping behavior, storage structures, type categories, dynamic inference, and coercion, while deferring a complete type catalog.
- Examples
- Uses movie fields, an electrical-products schema, geospatial fields, and a car age supplied as a string.
- What you can do after
- Apply it via Elasticsearch mappings to select field types that support intended indexing and query behavior.
Read how Elasticsearch in Action, Second Edition explains data type →
100 SQL Server Mistakes and How to Avoid Them
A data type defines the kind of value that a database column or expression can contain and how that value is interpreted.
- How it is used
- Guides column and expression design by matching stored values to suitable storage, interpretation, constraints, and workload requirements.
- How deep it goes
- Explains consequences, compares integer sizes, and demonstrates schema redesign, while deferring detailed treatment of most specialized types.
- Examples
- Uses an employee table, an oversized warehouse fact table, integer-size measurements, and revised T-SQL definitions.
- What you can do after
- Apply it via SQL Server schema design by selecting bounded, semantically appropriate types and reviewing oversized key columns.
Read how 100 SQL Server Mistakes and How to Avoid Them explains data type →
Build Python Web Apps with Streamlit
A data type specifies the kind of value that a database column can store and the operations applicable to that value.
- How it is used
- Defines the kinds of values columns can store when specifying a relational table schema.
- How deep it goes
- Explains string limits, automatic integer values, and timestamp defaults, without detailing type internals.
- Examples
- Uses user and haiku table columns, plus sales-data columns reported as object or float64.
- What you can do after
- Apply data types by defining PostgreSQL table columns with appropriate string, integer, or timestamp types.
Read how Build Python Web Apps with Streamlit explains data type →
Julia as a Second Language
A type is a classification of values that determines their representation and the operations applicable to them in a programming language.
- How it is used
- Uses types to distinguish object behavior, define application-specific objects, and guide valid field values.
- How deep it goes
- Works through type inspection, typed fields, conversion, and performance intuition; defers a fuller explanation of the parametric `Type` form.
- Examples
- Uses `typeof` REPL checks, numeric conversions, a typed screw catalog, and a vector-scaling performance sketch.
- What you can do after
- Build it from scratch: define application-specific objects with typed fields, and anticipate some conversion and performance consequences.
F# in Action: A Revised Edition of Get Programming with F#
A type is a language-level classification that specifies the kind of values an expression can represent and the operations applicable to them.
- How it is used
- Encodes domain rules and prevents incompatible application data from being combined.
- How deep it goes
- Explains domain-specific wrappers, validated types, discriminated unions, and practical type-inference diagnosis rather than type-system internals.
- Examples
- Uses City and Country wrappers, ValidatedEmail email sending, Instrument matching, and calculateGroup inference errors.
- What you can do after
- Apply it to model validated domain values, enforce prerequisites, and guide unexpected F# type inference.
Read how F# in Action: A Revised Edition of Get Programming with F# explains data type →
Starting Data Analytics with Generative AI and Python
A data type specifies the kind of value represented by a variable or column and the operations appropriate for that value.
- How it is used
- Uses data types to validate analytical data and prevent inconsistencies that could produce erroneous conclusions.
- How deep it goes
- Defines expected types and validation steps, but leaves acceptable values and thresholds to domain knowledge.
- Examples
- Uses informal date strings and a datetime64[ns] conversion as concrete validation examples.
- What you can do after
- Apply data-type checks, inspect deviations, and investigate or correct invalid values before analysis.
Read how Starting Data Analytics with Generative AI and Python explains data type →
Everyday Data Visualization
A data type is a classification that specifies the kind of value data can contain and the operations or representations appropriate for it.
- How it is used
- Guides how dimensions and measures are interpreted and visualized.
- How deep it goes
- Defines strings, numbers, dates, Booleans, null, arrays, and objects, but defers fine-grained numeric distinctions such as float32 and BigInt.
- Examples
- Uses fruit fields, date and fruit-type dimensions, and a million-number field containing one stray string.
- What you can do after
- Evaluate/understand field types before assigning data to a visualization.
Grokking Relational Database Design
An SQL data type specifies the kind of values that a database column can store and the operations supported for those values.
- How it is used
- Guides entity-and-attribute design, query filtering, and database implementation.
- How deep it goes
- Explains type groups and selection from sample data, but defers RDBMS-specific equivalents and exhaustive details.
- Examples
- Uses product prices and manufacturers, SQL WHERE filters, and serial_number column alterations to demonstrate typed values.
- What you can do after
- Build it from scratch by assigning suitable types to entity attributes from requirements and sample data.
Read how Grokking Relational Database Design explains data type →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A classification that specifies the kind of values a data field can contain.
- How it is used
- Classifies table columns so numeric, categorical, text, date, and binary values can be organized and processed appropriately.
- How deep it goes
- Defines common field types and illustrates mixed-type DataFrame construction, but stops short of detailed type conversion or validation procedures.
- Examples
- Uses a currency table and a four-person pandas dataset containing gender, age, education level, and income.
- What you can do after
- Apply data types via pandas by creating, labeling, selecting, and managing mixed-type tabular DataFrames.
Read how Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI explains data type →
Effective Data Analysis
A data type specifies the kind of value a field can contain and the operations or representation associated with that value.
- How it is used
- Uses data types to clean, integrate, and query organizational data reliably.
- How deep it goes
- Explains types through cleaning and ETL examples, but stops short of cataloging type systems or implementation details.
- Examples
- Uses weather API records, zip codes, date fields, and a most_recent Boolean indicator.
- What you can do after
- Apply data types when correcting mismatched fields and preserving usable join keys across integrated datasets.
D3.js in Action, Third Edition
A data type is a classification describing the kind of values represented by data and the operations or interpretations appropriate for them.
- How it is used
- Guides data loading, cleaning decisions, scale selection, and conversion before binding values to visual elements.
- How deep it goes
- Explains nominal and ordinal categories, string-to-number conversion, and automatic typing, but stops short of comprehensive data cleaning.
- Examples
- Uses nominal and ordinal data, CSV and JSON files, and a weekly temperature dataset containing dates and temperatures.
- What you can do after
- Apply it via D3 by converting loaded values, inspecting inferred types, and supplying correctly typed data to a chart.
Read how D3.js in Action, Third Edition explains data type →
Essential TypeScript 5, Third Edition
A data type is a classification that determines the kinds of values a program can represent and the operations applicable to them.
- How it is used
- Uses data types to explain JavaScript behavior and the TypeScript checks that make value assumptions explicit.
- How deep it goes
- Explains built-in types, runtime inspection, dynamic coercion, and TypeScript controls, but does not detail advanced type-system internals.
- Examples
- Demonstrates reassignment, typeof output, built-in type descriptions, and a calculateTax function receiving numbers, strings, and booleans.
- What you can do after
- Diagnose unexpected results by inspecting runtime types and apply TypeScript features to restrict or narrow permitted values.
Read how Essential TypeScript 5, Third Edition explains data type →
Practical Automation with PowerShell: Effective scripting from the console to the cloud
A data type classifies the representation and permitted values of data, such as an integer, string, or binary value.
- How it is used
- Uses types to generate registry-resolution values and define SQL table schemas for operational automation.
- How deep it goes
- Explains practical type selection, string sizing, and nullability, then applies them to JSON properties and SQL column maps.
- Examples
- Uses DWORD and test values for registry remediation, plus an nvarchar-based Servers table with identity and nullable settings.
- What you can do after
- Apply it by generating typed JSON metadata and configuring SQL columns with type, length, nullability, and identity settings.
The Art of Code
A type is a specification of the values a program entity can represent and the operations permitted on those values.
- How it is used
- Guides domain-aware representation choices and makes program intent visible through appropriately cast variables.
- How deep it goes
- Connects type choice to precision, performance, memory usage, and concurrency, but stops short of explaining type-system internals.
- Examples
- Uses BigDecimal versus float for prices and typed Java-style variables in a promotional-email method.
- What you can do after
- Evaluate and choose types that represent domain values accurately and clarify each variable’s responsibility.
Go in Action, Second Edition
A type is a specification of the values and operations permitted for a piece of data in a programming language.
- How it is used
- Models real-world domains by combining primitive values into named, checked, and reusable structures.
- How deep it goes
- Explains named structs, composition, compile-time checking, runtime type checks, and array identity, but defers detailed type declarations.
- Examples
- Demonstrates a person struct, named person values, field access, incompatible array lengths, and len() calls.
- What you can do after
- Build it from scratch by defining named structs and recognizing how Go’s type rules constrain assignments and collections.
Related concepts
- classincludes · A class is a programming-language construct that defines a type’s structure or behavior and provides a basis for creating or representing objects.
- contractincludes · An API contract is a specification of the operations, data, and behavioral expectations that govern interactions between software components.
- stringincludes · A string is a programming-language value representing an ordered sequence of characters or character units as text.
- integerincludes · An integer is a whole-number value with no fractional part, including negative numbers, zero, and positive numbers.
- schemapart of · A data schema is a formal specification of the structure, fields, types, relationships, and constraints of data.
- nominal dataincludes · Nominal data consists of categorical values whose labels have no inherent or meaningful ordering.
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