SSAS (SQL Server Analysis Services) is a Microsoft BI (Business Intelligence) technology used for online analytical processing (OLAP) and data mining. Here are some important SSAS terminology:
- Cube: A cube is a multidimensional structure that contains measures and dimensions. It organizes data into a structure that is optimized for querying and analysis.
- Dimension: Dimensions are the categories by which data is analyzed. They provide the context for measures in a cube. Examples of dimensions include Time, Geography, Product, etc.
- Measure: Measures are the numeric values that are being analyzed. Examples of measures include Sales Amount, Quantity Sold, Profit Margin, etc.
- Hierarchy: Hierarchies are logical structures that organize dimension attributes into levels of detail. For example, a Time dimension might have a hierarchy with levels such as Year, Quarter, Month, and Day.
- Attribute: Attributes are the individual members within a dimension hierarchy. For example, within a Time dimension, attributes might include Year, Quarter, Month, and Day.
- Fact Table: A fact table is a central table in a star schema or snowflake schema that contains the quantitative data for analysis. Measures in a cube are derived from the columns in the fact table.
- MDX (Multidimensional Expressions): MDX is the query language used to query multidimensional data stored in SSAS cubes. It is similar in syntax to SQL but is specifically designed for querying OLAP data.
- Aggregation: Aggregations are pre-calculated summary values stored within the cube to improve query performance. Aggregations are created based on the measures and dimensions defined in the cube.
- Partition: A partition is a physical storage container within a cube that holds a subset of data from the underlying data source. Partitions allow for parallel processing and efficient querying.
- Data Source View (DSV): A data source view defines the schema and relationships between the tables and views used as the data source for an SSAS project.
- KPI (Key Performance Indicator): KPIs are business metrics used to evaluate the performance of an organization. SSAS allows for the definition and analysis of KPIs within cubes.
- Mining Model: In SSAS, data mining models are used to identify patterns and relationships in data. These models can be created using algorithms such as Decision Trees, Clustering, Neural Networks, etc.