Granularity
Granularity is the level of detail or fineness at which information, behavior, or a system is represented, grouped, or divided.
The Well-Grounded Data Analyst
The level of detail at which data or an analysis is represented, such as cities, boroughs, or broader regions.
- How it is used
- Guides data-model structure and transformations so analytical outputs match stakeholder questions.
- How deep it goes
- Works through row-unit definition, uniqueness checks, aggregation, data-quality checks, and forecasting trade-offs.
- Examples
- Uses customer-level models, product purchase events, football tables, city spending, traffic counts, and street-level property comparisons.
- What you can do after
- Apply it to choose row units, aggregate data, validate record uniqueness, and judge whether available detail supports a question.
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Effective Data Analysis
Granularity, or grain, is the level of detail represented by a record, observation, or dataset.
- How it is used
- Guides the choice of record and metric grouping so measurements reflect the behavior and decisions at issue.
- How deep it goes
- Defines row-level detail and examines organizational, geographic, and temporal aggregation tradeoffs; does not develop a formal calculation method.
- Examples
- Uses patient visits, B2B users and customers, geographic groupings, and time periods for workflows and A/B tests.
- What you can do after
- Practice/perform it by selecting a measurement and time grain, checking existing aggregation, and judging what a metric might obscure.
Architecture Modernization: Socio-technical alignment of software, strategy, and structure
Granularity is the level of detail at which a domain story represents a process or business area.
- How it is used
- Sets a single level of detail when scoping Domain Storytelling workshops and domain-process representations.
- How deep it goes
- Distinguishes coarse, medium, and fine levels, and requires each story to remain at one level.
- Examples
- Uses broad business stories, ten-step activity breakdowns, and intention-focused domain events as contrasting levels of detail.
- What you can do after
- Apply it via Domain Storytelling or EventStorming to choose useful detail and keep workshop discussions focused.
Everyday Data Visualization
Grain is the level of detail defined by what one row in a data table represents.
- How it is used
- Defines the first data question to answer before interpreting, joining, aggregating, or visualizing a table.
- How deep it goes
- Explains grain through banking, fruit, and reservation examples, then stops short of detailed join or aggregation procedures.
- Examples
- Uses banking transactions, daily summaries, account snapshots, fruit observations, fruit-type scores, and hotel reservations.
- What you can do after
- Evaluate/understand what each row represents before aggregating data or trusting calculated totals.
Machine Learning System Design
The level of detail at which data is represented, measured, or modeled.
- How it is used
- Guides dataset design and demand-forecasting tradeoffs between computational efficiency, model performance, and product use cases.
- How deep it goes
- Contrasts daily and weekly aggregation, quantifies a sevenfold computation reduction, and stops short of prescribing one universal resolution.
- Examples
- Uses day-store-item and week-store-item demand records as the central granularity comparison.
- What you can do after
- Evaluate data-resolution choices against computation, validation performance, service speed, and stakeholder needs.
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Grokking Concurrency
Granularity is the degree to which a computation is divided into tasks, characterized by the number and size of those tasks.
- How it is used
- Guides the decomposition of concurrent applications into task sizes and counts that use processing resources effectively.
- How deep it goes
- Defines coarse and fine granularity, explains their trade-offs, and stops short of implementation-specific scheduling details.
- Examples
- Contrasts four and eight threads on an eight-core system, including how a scheduler handles fewer available cores.
- What you can do after
- Apply granularity choices when decomposing work, balancing parallelism and runtime coordination overhead.
The Design of Web APIs, Second Edition
Rule granularity is the degree to which a linting rule limits its verification to a narrowly defined aspect of an API.
- How it is used
- Guides API modeling, operation design, and lint-rule construction toward focused, usable, interoperable decisions.
- How deep it goes
- Explains relevance, scope, operation boundaries, and rule decomposition through practical guidance, without detailing linter implementation.
- Examples
- Uses account reads, transaction search, dashboards, customer-address updates, and decomposed linting needs as examples.
- What you can do after
- Evaluate API and lint-rule scope, then split broad verification needs into focused checks with clearer consumer feedback.
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Functional Design and Architecture: Examples in Haskell
Granularity is the size of the independently mutable units into which data or computation is divided.
- How it is used
- Uses granularity to choose a concurrent data design for a stateful simulator.
- How deep it goes
- Explains nested mutable layers and contrasts update costs, while deferring deeper STM treatment.
- Examples
- Compares nested MVars with a coarse-grained map in a simulator runtime.
- What you can do after
- Evaluate concurrent data structures by their mutation boundaries and repeated-update costs.
Read how Functional Design and Architecture: Examples in Haskell explains granularity →
Related concepts
- data aggregationconfigures · Data aggregation is the combination of multiple values, records, observations, or results into a consolidated or summarized output.
- decompositionpart of · Decomposition is the process of dividing a complex whole into smaller constituent parts that can be handled or analyzed separately.
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