What is aggregation and granularity?
Aggregation and granularity are complementary concepts. Aggregation is a mathematical operation that takes multiple values and returns a single value: operations like sum, average, count, or minimum. This changes the data to a lower granularity (aka a higher level of detail).
What is granularity of a dataset?
Granularity is the level of detail at which data are stored in a database. When the same data are represented in multiple databases, the granularity may differ.
What are the levels of granularity?
Granularity refers to “the level of detail or summarisation of the units of data in the data warehouse”. The low level of granularity contains high level of detail and the high level of granularity contains low level of detail.
What is an aggregated table?
Aggregate tables are tables that aggregate or “roll up” the data to one level higher than a base or derived table (and other functions can also be in the aggregate tables such as average, count, min, max, and others).
What is data aggregation?
Data aggregation is the process where data is collected and presented in a summarized format for statistical analysis and to effectively achieve business objectives. Data aggregation is vital to data warehousing as it helps to make decisions based on vast amounts of raw data.
What are aggregate results?
Aggregate data refers to individual data that are averaged by geographic area, by year, by service agency, or by other means. Individual data are disaggregated individual results and are used to conduct analyses for estimation of subgroup differences. Aggregate data are also used for medical and educational purposes.
What is granularity table?
In dimensional modeling, granularity refers to the level of detail stored in a table. For example, a dimension such as Date (with Year and Quarter hierarchies) has a granularity at the quarter level but does not have information for individual days or months.
What is granularity in statistics?
Data granularity is the level of detail considered in a model or decision making process or represented in an analysis report. The greater the granularity, the deeper the level of detail.
What is granularity of a fact table?
The granularity is the lowest level of information stored in the fact table. The depth of data level is known as granularity. In date dimension the level could be year, month, quarter, period, week, day of granularity.
What is meant by level of granularity?
/ˌɡrænjəˈlærəti/ a lot of small details included in information, making it possible for you to understand very clearly what is happening: degree/level of granularity In our market analysis we offer a whole new level of granularity.
Can the data in the fact table be aggregated?
Aggregates are the summarization of fact related data for the purpose of improved performance. Aggregates can be considered to be conformed fact tables since they must provide the same query results as the detailed fact table.
What is aggregate data analysis?
Aggregate data refers to numerical or non-numerical information that is (1) collected from multiple sources and/or on multiple measures, variables, or individuals and (2) compiled into data summaries or summary reports, typically for the purposes of public reporting or statistical analysis—i.e., examining trends.
How to determine the granularity of a fact table?
The first step in designing a fact table is to determine the granularity of the fact table. By granularity, we mean the lowest level of information that will be stored in the fact table. This constitutes two steps: Determine which dimensions will be included.
What is the difference between aggregation and granularity?
Aggregation and granularity are complementary concepts. Aggregation is a mathematical operation that takes multiple values and returns a single value: operations like sum, average, count, or minimum. This changes the data to a lower granularity (aka a higher level of detail).
How can I create an aggregate fact table?
You may create each aggregate fact table as a specific summarization across any number of dimensions. Let us begin by examining a sample STAR schema. Choose a simple STAR schema with the fact table at the lowest possible level of granularity. Assume there are four dimension tables surrounding this most granular fact table.
Which is the lowest granularity in the time dimension?
If so, it makes sense to use ‘hour’ as the lowest level of granularity in the time dimension. If daily analysis is sufficient, then ‘day’ can be used as the lowest level of granularity.