# Generic The Generic load prefix allows for conversion of entity–attribute–value modeled data (EAV) into a traditional, normalized relational table structure. EAV modeling is alternatively referred to as "generic data modeling" or "open schema". Example of EAV modeled data and an equivalent denormalized relational table Example of EAV modeled data and an equivalent denormalized relational table Example of EAV modeled data and an equivalent denormalized relational table ![Diagram showing transformation of a table with three columns and five rows into a table with four columns and two rows. The initial table has an 'Attributes' column, within which each entry gets converted to its own column in the resulting table: 'Status', 'Colour', and 'Size'.](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Resources/Images/scripting-functions-generic-EAV-denormalized.png) Example of EAV modeled data and an equivalent set of normalized relational tables Example of EAV modeled data and an equivalent set of normalized relational tables Example of EAV modeled data and an equivalent set of normalized relational tables ![Diagram showing transformation of a table with three columns and five rows into three separate tables. The initial table has an 'Attributes' column, within which each entry ('Status', 'Colour', and 'Size') gets converted into its own table. The 'Product ID' field remains constant in all tables.](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Resources/Images/scripting-functions-generic-EAV-normalized.png) While it is technically possible to load and analyze EAV modeled data in Qlik, it is often easier to work with an equivalent traditional relational data structure. Syntax: Generic( loadstatement | selectstatement ) * For more information about using LOAD statements, see [Load](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptRegularStatements/Load.htm). * For more information about using SELECT statements, see [Select](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptRegularStatements/Select.htm). These topics may help you work with this function: Related topics | Topic | Description | | --- | --- | | [Crosstable](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptPrefixes/crosstable.htm) | The `Crosstable` load prefix transforms data that is horizontally-oriented into vertically-oriented data. From a purely functional perspective, it performs the opposite transformation to the `Generic` load prefix, although the prefixes typically serve entirely different use cases. | | [Generic databases](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/DataSource/generic-databases.htm) | EAV structured data models are further described here. | ## Example 1 – Transforming EAV structured data with the Generic load prefix [](https://help.qlik.com/en-US/sense/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptPrefixes/Generic.htm) #### Load script and chart expression Overview Open the Data load editor and add the load script below to a new tab. The load script contains a dataset which is loaded into a table named `Transactions`. The dataset includes a date field. The default `MonthNames` definition is used. Load script ``` Products: Generic Load * inline [ Product ID, Attribute, Value 13, Status, Discontinued 13, Color, Brown 20, Color, White 13, Size, 13-15 20, Size, 16-18 2, Status, Discontinued 5, Color, Brown 2, Color, White 44, Color, Brown 45, Size, 16-18 45, Color, Brown ]; ``` Results Load the data and open a sheet. Create a new table and add this field as a dimension: `Color`. Add this measure: `=Count([Product ID])` Now you can inspect the number of products by color. Results table | Color | =Count([Product ID]) | | --- | --- | | Brown | 4 | | White | 2 | Note the shape of the data model, where each attribute has been broken out into a separate table named according to the original target table tag `Product`.Each table has the attribute as a suffix. One example of this is `Product.Color`. The resulting Product Attribute output records are associated by the `Product ID`. Data model viewer representation of the results Data model viewer representation of the results Data model viewer representation of the results ![Representation of the results of this example as they appear in the data model viewer. The Product ID field in each of the three tables is associated throughout the data model.](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Resources/Images/scripting-functions-generic-results-data-model-viewer.png) Resulting table of records: Products.Status | Product ID | Status | | --- | --- | | 13 | Discontinued | | 2 | Discontinued | Resulting table of records: Products.Size | Product ID | Size | | --- | --- | | 13 | 13-15 | | 20 | 16-18 | | 45 | 16-18 | Resulting table of records: Products.Color | Product ID | Color | | --- | --- | | 13 | Brown | | 5 | Brown | | 44 | Brown | | 45 | Brown | | 20 | White | | 2 | White | ## Example 2 – Analyzing EAV structured data without the Generic load prefix [](https://help.qlik.com/en-US/sense/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptPrefixes/Generic.htm) #### Load script and chart expression Overview This example shows how to analyze EAV structured data in its original form. Open the Data load editor and add the load script below to a new tab. The load script contains a dataset which is loaded into a table named `Products` in an EAV structure. In this example, we are still counting products by color attribute. In order to analyze data structured in this way, you will need to apply expression-level filtering of products carrying the Attribute value `Color`. Furthermore, individual attributes are not available to select as dimensions or fields, making it harder to determine how to build effective visualizations. Load script ``` Products: Load * Inline [ Product ID, Attribute, Value 13, Status, Discontinued 13, Color, Brown 20, Color, White 13, Size, 13-15 20, Size, 16-18 2, Status, Discontinued 5, Color, Brown 2, Color, White 44, Color, Brown 45, Size, 16-18 45, Color, Brown ]; ``` Results Load the data and open a sheet. Create a new table and add this field as a dimension: `Value`. Create the following measure: `=Count({} [Product ID])` Now you can inspect the number of products by color. Resulting table of records: Products.Status | Value | =Count({<Attribute={'Color'}>} [Product ID]) | | --- | --- | | Brown | 4 | | White | 2 | ## Example 3 – Denormalizing the resulting output tables from a Generic load (advanced) [](https://help.qlik.com/en-US/sense/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptPrefixes/Generic.htm) #### Load script and chart expression Overview In this example, we show how the normalised data structure produced by the `Generic` load prefix can be denormalised back into a consolidated `Product` dimension table. This is an advanced modeling technique which can be employed as part of data model performance tuning. Open the Data load editor and add the load script below to a new tab. Load script ``` Products: Generic Load * inline [ Product ID, Attribute, Value 13, Status, Discontinued 13, Color, Brown 20, Color, White 13, Size, 13-15 20, Size, 16-18 2, Status, Discontinued 5, Color, Brown 2, Color, White 44, Color, Brown 45, Size, 16-18 45, Color, Brown ]; RENAME TABLE Products.Color TO Products; OUTER JOIN (Products) LOAD * RESIDENT Products.Size; OUTER JOIN (Products) LOAD * RESIDENT Products.Status; DROP TABLES Products.Size,Products.Status; ``` Results Open the Data model viewer and note the shape of the resulting data model. Only one denormalized table is present. It is a combination of the three intermediary output tables: `Products.Size`, `Products.Status`, and `Products.Color`. Resulting internal data model | Products | | --- | | Product ID | | Status | | Color | | Size | Resulting table of records: Products | Product ID | Status | Color | Size | | --- | --- | --- | --- | | 13 | Discontinued | Brown | 13-15 | | 20 | - | White | 16-18 | | 2 | Discontinued | White | - | | 5 | - | Brown | - | | 44 | - | Brown | - | | 45 | - | Brown | 16-18 | Load the data and open a sheet. Create a new table and add this field as a dimension: `Color`. Add this measure: `=Count([Product ID])` Results table | Color | =Count([Product ID]) | | --- | --- | | Brown | 4 | | White | 2 | ## Learn more * [Load](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptRegularStatements/Load.htm) * [Select](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptRegularStatements/Select.htm) * [Crosstable](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/Scripting/ScriptPrefixes/crosstable.htm) * [Generic databases](https://help.qlik.com/en-US/sense/November2024/Subsystems/Hub/Content/Sense_Hub/DataSource/generic-databases.htm)