> ## Documentation Index
> Fetch the complete documentation index at: https://docs.maia.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Pivot

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => {
  const allWarehouses = [...warehouses.map(w => ({
    name: w,
    supported: true
  })), ...unsupportedWarehouses.map(w => ({
    name: w,
    supported: false
  }))];
  return <div style={{
    background: 'var(--colors-background-light, #f9fafb)',
    border: '1px solid var(--colors-border-default, #e5e7eb)',
    borderRadius: '12px',
    padding: '20px 28px',
    marginBottom: '28px',
    boxShadow: '0 1px 4px rgba(0,0,0,0.10)'
  }}>
      <table style={{
    width: '100%',
    borderCollapse: 'collapse'
  }}>
        <tbody>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle',
    width: '180px'
  }}>Project Availability</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>
              <div style={{
    display: 'flex',
    flexWrap: 'wrap',
    gap: '8px'
  }}>
                {allWarehouses.map((w, i) => <span key={i} style={{
    background: w.supported ? '#dcfce7' : '#fee2e2',
    color: w.supported ? '#15803d' : '#b91c1c',
    border: `1px solid ${w.supported ? '#bbf7d0' : '#fca5a5'}`,
    borderRadius: '9999px',
    padding: '3px 12px',
    fontSize: '0.85rem',
    fontWeight: '500',
    whiteSpace: 'nowrap'
  }}>
                    {w.name} {w.supported ? '✅' : '❌'}
                  </span>)}
              </div>
            </td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Component Type</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{componentType}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Inputs</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{connectionInputs}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
        </tbody>
      </table>
    </div>;
};

<ComponentMetadata warehouses={["Snowflake", "Databricks"]} unsupportedWarehouses={["Amazon Redshift", "BigQuery"]} componentType="Transformation" connectionInputs="One" connectionOutputs="Unlimited" />

The **Pivot** transformation component lets you turn your rows of data into columns. This generally means reducing the number of rows and increasing the number of columns.

The Pivot column you choose contains the data that you want to aggregate. The Value column you choose contains labels for that data, which define the groups by which the data in the Pivot column can be aggregated. The Pivot Values you specify (values in the Value column) are used as the new column names in the output.

This component is equivalent to an SQL PIVOT function.

### Use case

This component can be used to analyze data by aggregating values from a single dataset in different categories. For example, you can use it to:

* Calculate total sales per product using a dataset containing data from multiple individual sales.
* Monitor average product downtime per day using a dataset containing downtime data for multiple products over a longer period of time.

***

## Properties

<ResponseField name="Name" type="string" required>
  A human-readable name for the component.
</ResponseField>

{/* <!-- param-start:[aggregateFunction] | warehouses: [snowflake, databricks] --> */}

<ResponseField name="Aggregate Function" type="drop-down" required>
  Select which aggregate function to call to combine the grouped values from the **Pivot Column**. For more information, read the following:

  * [Snowflake Aggregate Functions](https://docs.snowflake.com/en/sql-reference/functions-aggregation.html)
  * [Databricks Aggregate Functions](https://docs.databricks.com/en/sql/language-manual/sql-ref-functions-builtin.html#aggregate-functions)
</ResponseField>

{/* <!-- param-start:[pivotColumn] | warehouses: [snowflake, databricks] --> */}

<ResponseField name="Pivot Column" type="drop-down" required>
  Specify the column from the source table or sub-query that will be aggregated. This requires an input component before configuring and uses the column names from the input component.

  The data type expected in this column depends on the aggregate function you are depending to use. For example, you cannot average non-numeric values.
</ResponseField>

{/* <!-- param-start:[valueColumn] | warehouses: [snowflake, databricks] --> */}

<ResponseField name="Value Column" type="drop-down" required>
  Specify the column from the source table or sub-query that contains the values that will group data for aggregations and from which new column names will be generated. This requires an input component before configuring and uses the column names from the input component.

  The chosen Value Column will be missing from the output, replaced by a new column for each of the Pivot Values specified.
</ResponseField>

{/* <!-- param-start:[pivotValues] | warehouses: [snowflake, databricks] --> */}

<ResponseField name="Pivot Values" type="multiple strings" required>
  A list of values that exist in the Value Column in the input data that are used to group data for aggregation. These values become new columns in the output, with data from the Pivot Column being aggregated into these new columns.
</ResponseField>

{/* <!-- param-start:[quotePivotValueColumns] | warehouses: [snowflake, databricks] --> */}

<ResponseField name="Quote Pivot Value Columns" type="drop-down" required>
  Choose whether to enclose the new column names (the selected pivot values) in quote marks. This can avoid problems with the component if your pivot values contain spaces or special characters, if your pivot values are numeric data, or if your data is in mixed case.

  If you use Snowflake, we recommend selecting `Yes` because Snowflake has specific rules for column identifiers. Enclosing your pivot values in quotes helps to meet these rules.
</ResponseField>

## Examples

<Tabs>
  <Tab title="Example 1">
    **Goal:** Calculate total monthly sales for each product

    We have the following table, containing data from individual sales of three different products (caps, T-shirts, and hoodies) in January, February, and March. Let's use the **Pivot** component to group the sales data by month and calculate the total sales for each month.

    **Input data**

    | Product | Month | Sales |
    | ------- | ----- | ----- |
    | T-Shirt | Jan   | 100   |
    | T-Shirt | Jan   | 50    |
    | T-Shirt | Feb   | 150   |
    | T-Shirt | Mar   | 200   |
    | Hoodie  | Jan   | 200   |
    | Hoodie  | Jan   | 100   |
    | Hoodie  | Mar   | 250   |
    | Hoodie  | Mar   | 180   |
    | Cap     | Jan   | 60    |
    | Cap     | Feb   | 90    |
    | Cap     | Mar   | 120   |

    **Component configuration**

    * **Aggregate Function:** Sum
    * **Pivot Column:** Month
    * **Value Column:** Sales
    * **Pivot Values:** Jan, Feb, Mar

    **Output data**

    | Product | Jan | Feb | Mar |
    | ------- | --- | --- | --- |
    | T-Shirt | 150 | 150 | 200 |
    | Hoodie  | 300 |     | 430 |
    | Cap     | 60  | 90  | 120 |

    **Result:** The pivot values `Jan`, `Feb`, and `Mar` are used as the new column names. The values in the value column `Sales` are aggregated using the `Sum` function and grouped by the values in the pivot column `Month`. There was no sales data for hoodies in February, so this value is null.
  </Tab>

  <Tab title="Example 2">
    **Goal:** Calculate average student exam results by subject

    We have the following table, containing different students' exam results for a range of subjects. Let's use the **Pivot** component to calculate each student's average score in each subject. The students are identified by their student ID number.

    **Input data**

    | Student\_ID | Subject   | Score |
    | ----------- | --------- | ----- |
    | S001        | Science   | 85    |
    | S001        | Science   | 90    |
    | S001        | History   | 78    |
    | S002        | Geography | 88    |
    | S002        | History   | 92    |
    | S002        | History   | 86    |

    **Component configuration**

    * **Aggregate Function:** Average
    * **Pivot Column:** Subject
    * **Value Column:** Score
    * **Pivot Values:** Science, History, Geography

    **Output data**

    | Student\_ID | Science | History | Geography |
    | ----------- | ------- | ------- | --------- |
    | S001        | 87.5    | 78      |           |
    | S002        |         | 89      | 88        |

    **Result:** The pivot values `Science`, `History`, and `Geography` are used as the new column names. The values in the value column `Score` are aggregated using the `Average` function and grouped by the values in the pivot column `Subject`. There are two null values here, as student S001 has no score for Geography and S002 has no score for Science.
  </Tab>
</Tabs>