> ## 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.

# Excel Load

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs, driverVersion, driverVersionUrl}) => {
  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',
    paddingBottom: driverVersion ? '14px' : '0',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    paddingBottom: driverVersion ? '14px' : '0',
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
          {driverVersion && <tr>
              <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Driver Version</td>
              <td style={{
    verticalAlign: 'middle'
  }}>
                {driverVersionUrl ? <a href={driverVersionUrl} target="_blank" rel="noopener noreferrer">{driverVersion}</a> : driverVersion}
              </td>
            </tr>}
        </tbody>
      </table>
    </div>;
};

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

<Note>
  For Snowflake projects, the Excel Load connector supersedes the [Excel Query](/docs/components/excel-query) connector, which is no longer available for new pipelines. Existing pipelines that use the Excel Query connector will continue to work as expected.

  Databricks and Amazon Redshift projects should continue to use the [Excel Query](/docs/components/excel-query) connector.
</Note>

Excel Load connector loads data stored in an Office Open XML Excel sheet into a table.

Each time the Excel Load connector runs, the target table is recreated, dropping any existing table of the same name. You *do not* need to use the Create Table component when using this component.

By default, data types are guessed by looking at the cell formatting, not cell contents. This is controlled using the Connection Option "type detection scheme", which can be set to **ColumnFormat** (the default, which examines the cell formatting), **RowScan** (which will scan 15 rows of data and guess the data type based on the data values), or **None** (treat everything as text). **None** is often a sensible choice if you intend to parse the values later, or the types in a single column are mixed. The Connection Option "row scan depth" controls how many rows to scan when **RowScan** is selected.

If the component requires access to a cloud provider (AWS, Azure, or Google Cloud), it will use credentials as follows:

* If using [Matillion Full SaaS](/docs/guides/runner-overview#matillion-full-saas): The component will use the [cloud credentials](/docs/guides/cloud-credentials) associated with your environment to access resources.
* If using [Hybrid SaaS](/docs/guides/runner-overview#hybrid-saas): By default the component will inherit the agent's execution role (service account role). However, if there are [cloud credentials](/docs/guides/cloud-credentials) associated with your environment, these will overwrite the role.

<Warning>
  You need valid AWS or Azure credentials to access the data. The bucket may be defined as public, but is secured by the credentials.
</Warning>

<Warning>
  This component is potentially destructive. If the target table undergoes a change in structure, it will be recreated. Otherwise, the target table is truncated. Setting the load option **Recreate Target Table** to **Off** will prevent both recreation and truncation. Do not modify the target table structure manually.
</Warning>

***

## Properties

Reference material is provided below for the Connect, Configure, Destination, and Advanced Settings properties.

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

### Connect

<ResponseField name="Storage Type" type="drop-down" required>
  Select the cloud storage location that hosts your Excel file. Currently, the following storage types are supported:

  * Amazon S3 Storage
  * Azure Blob Storage
  * Google Cloud Storage
</ResponseField>

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

<ResponseField name="Storage URL" type="string" required>
  The URL to the location of your .xlsx source file. Only Office Open XML (.xlsx) files are supported. Storage containers are explorable here if your credentials include access to resources of the selected **Storage Type**.

  Clicking this property will open the **Storage URL** dialog. This displays a list of all existing storage accounts. Select a storage account, then a container, and then a subfolder if required. This constructs a URL with the following format:

  ```
  DATATYPE://<account>/<container>/<path>
  ```

  You can also type the URL directly into the **Storage Accounts path** field, instead of selecting listed elements. This is particularly useful when using [variables](/docs/guides/variables) in the URL, for example:

  ```
  AZURE://${jv_blobStorageAccount}/${jv_containerName}
  ```

  Special characters used in this field *must* be URL-safe.
</ResponseField>

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

<ResponseField name="Connection Options" type="column editor">
  `Connection Options` = *column editor*

  * **Parameter:** A JDBC parameter supported by the database driver. The available parameters are explained in the data model. Manual setup is not usually required, since sensible defaults are assumed.
  * **Value:** A value for the given parameter.

  Click the **Text Mode** toggle at the bottom of the **Connection Options** dialog to open a multi-line editor that lets you add items in a single block. For more information, read [Text mode](/docs/guides/components-overview#component-properties).

  To use [grid variables](/docs/guides/grid-variables), select the **Use Grid Variable** checkbox at the bottom of the **Connection Options** dialog.
</ResponseField>

### Configure

<ResponseField name="Load Type" type="drop-down" required>
  * **Full Load:** Select this option to load your entire dataset.
  * **Incremental Load:** Select this option to only load new and updated records from your dataset.
</ResponseField>

<ResponseField name="Mode" type="drop-down" required>
  * **Basic:** This mode will build a query for you using settings from the **Schema**, **Data Source**, **Data Selection**, **Data Source Filter**, **Combine Filters**, and **Row Limit** parameters. In most cases, this mode will be sufficient.
  * **Advanced:** This mode will require you to write an SQL-like query to call data from the service you're connecting to. The available fields and their descriptions are documented in the data model.

      <Note>
        **Advanced** mode is currently not supported when **Incremental Load** is selected.
      </Note>

  There are some special pseudo columns that can form part of a query filter, but are not returned as data. This is fully described in the data model.

  <Note>
    While the query is exposed in an SQL-like language, the exact semantics can be surprising, for example, filtering on a column can return more data than not filtering on it. This is an impossible scenario with regular SQL.
  </Note>
</ResponseField>

<ResponseField name="SQL Query" type="code editor" required>
  Enter an SQL-like SELECT query to retrieve the data you want. This query must be written in the SQL accepted by your cloud data warehouse. Treat collections as table names, and fields as columns. Only available in **Advanced** mode.

  For more information, read the SELECT query documentation for your cloud data warehouse:

  * [Snowflake](https://docs.snowflake.com/en/sql-reference/sql/select)
  * [Google BigQuery](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax)
</ResponseField>

<ResponseField name="Contains Header Row" type="boolean">
  * **Yes:** The first row of data is the column names.
  * **No:** The first row of data is just data. Columns will be named A, B, C...
</ResponseField>

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

<ResponseField name="Cell Range" type="string">
  By default the whole worksheet is considered. However, you may optionally specify a range of cells instead. For example, A5:E100 would only consider rows 5-100 in columns A-E. Wildcards (*) are also supported, for example A5:E* would consider columns A-E and rows 5 onwards.
</ResponseField>

<ResponseField name="Data Source" type="drop-down" required>
  Select a single data source to be extracted from the source system and loaded into a table in the destination. The source system defines the data sources available. Use multiple components to load multiple data sources.
</ResponseField>

<ResponseField name="Data Selection" type="object selector" required>
  Select the columns to fetch from the selected data source. Only available in **Basic** mode.

  To use grid variables, toggle **Use Grid Variable** on. For more information, read [Grid variables](/docs/guides/grid-variables).
</ResponseField>

<ResponseField name="Data Source Filter" type="column editor">
  Define one or more filter conditions that each row of data must meet to be included in the load.

  * **Input Column:** Select an input column. The available input columns vary depending upon the data source.
  * **Qualifier:**
    * **Is:** Compares the column to the value using the comparator.
    * **Not:** Reverses the effect of the comparison, so "Equals" becomes "Not equals", "Less than" becomes "Greater than or equal to", etc.
  * **Comparator:** Choose a method of comparing the column to the value. Possible comparators include: "Equal to", "Greater than", "Less than", "Greater than or equal to", "Less than or equal to", "Like", "Null". Not all data sources support all comparators.
  * **Value:** The value to be compared.

  Toggle **Text mode** on at the bottom of the dialog to open a multi-line editor that lets you add items in a single block. For more information, read [Text mode](/docs/guides/components-overview#text-mode).

  To use grid variables, toggle **Use Grid Variable** on. For more information, read [Grid variables](/docs/guides/grid-variables).
</ResponseField>

<ResponseField name="Combine Filters" type="drop-down">
  The data source filters you have defined can be combined using either **And** or **Or** logic. If **And**, then all filter conditions must be satisfied to load the data row. If **Or**, then only a single filter condition must be satisfied. The default is **And**.

  If you have only one filter, or no filters, this parameter is essentially ignored.
</ResponseField>

<ResponseField name="Row Limit" type="integer">
  Set a numeric value to limit the number of rows that are loaded. The default is an empty field, which will load all rows.
</ResponseField>

<ResponseField name="Use Caching" type="boolean">
  When enabled, Excel data is cached to a temporary directory on disk rather than being held entirely in memory. This significantly reduces memory usage and is recommended when processing large Excel files. The cache is automatically cleared once the query completes.
</ResponseField>

### Destination

<Tabs>
  <Tab title="Snowflake">
    <ResponseField name="Destination" type="drop-down" required>
      Select the destination for your data. This is either in Snowflake as a table or as files in cloud storage.

      * **Snowflake:** Load your data into a table in Snowflake. The data must first be staged via Snowflake or a cloud storage solution.
      * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension so we suggest inspecting the output to determine the format of the data.
    </ResponseField>

    <Note>
      When **Incremental Load** is selected, **Cloud Storage** is not supported as a destination. Only warehouse destinations (Snowflake or Google BigQuery) are available for incremental loads.
    </Note>

    <Tabs>
      <Tab title="Snowflake">
        <ResponseField name="Warehouse" type="drop-down" required>
          The Snowflake warehouse used to run the queries. The special value `[Environment Default]` uses the warehouse defined in the environment. Read [Overview of Warehouses](https://docs.snowflake.com/en/user-guide/warehouses-overview.html) to learn more.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.database, snowflake-output-connector-v1.database] | warehouses: [snowflake] --> */}

        <ResponseField name="Database" type="drop-down" required>
          The Snowflake database to access. The special value `[Environment Default]` uses the database defined in the environment. Read [Databases, Tables and Views - Overview](https://docs.snowflake.com/en/guides-overview-db) to learn more.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.schema, snowflake-output-connector-v1.schema] | warehouses: [snowflake] --> */}

        <ResponseField name="Schema" type="drop-down" required>
          The Snowflake schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Database, Schema, and Share DDL](https://docs.snowflake.com/en/sql-reference/ddl-database.html) to learn more.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.tableName, snowflake-output-connector-v1.tableName] | warehouses: [snowflake] --> */}

        <ResponseField name="Table Name" type="string" required>
          The name of the table to be created in your Snowflake database. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded.
        </ResponseField>

        <ResponseField name="Load Strategy" type="drop-down" required>
          Define what happens if the table name already exists in the specified destination. Only available when **Full Load** is selected.

          * **Replace:** If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
          * **Truncate and Insert:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated.
          * **Fail if Exists:** If the specified table name already exists, this pipeline will fail to run.
          * **Append:** If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It's appended onto the end of the existing data in the table. If the specified table name doesn't exist, then the table will be created, and your data will be inserted into the table.
        </ResponseField>

        <ResponseField name="Primary Keys" type="object selector">
          Select one or more columns to use as the table's primary key. If you select multiple columns, the component creates a composite primary key using the combination of the selected columns.

          Leave this blank to create the table without a primary key.

          When using **Incremental Load**, if you select a primary key, the loaded data will be merged with your existing data. If you don't select a primary key, the loaded data will be appended to your existing data.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.cleanStagedFiles, snowflake-output-connector-v1.cleanStagedFiles] | warehouses: [snowflake] --> */}

        <ResponseField name="Clean Staged files" type="boolean" required>
          * **Yes:** Staged files will be destroyed after data is loaded. This is the default setting.
          * **No:** Staged files are retained in the staging area after data is loaded.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.stagePlatform, snowflake-output-connector-v1.stagePlatform] | warehouses: [snowflake] --> */}

        <ResponseField name="Stage Platform" type="drop-down" required>
          Use the drop-down menu to choose where the data is staged before being loaded into your Snowflake table.

          * **Amazon S3:** Stage your data on an AWS S3 bucket.
          * **Snowflake:** Stage your data on a Snowflake internal stage.
          * **Azure Storage:** Stage your data in an Azure Blob Storage container.
          * **Google Cloud Storage:** Stage your data in a Google Cloud Storage bucket.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.snowflake#internalStageType, snowflake-output-connector-v1.snowflake#internalStageType] | warehouses: [snowflake] --> */}

        <ResponseField name="Internal Stage Type" type="drop-down" required>
          Select the Snowflake internal stage type. Use the Snowflake links provided to learn more about each type of stage.

          * **User:** Each Snowflake user has a [user stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#user-stages) allocated to them by default for file storage. You may find the user stage convenient if your files will only be accessed by a single user, but need to be copied into multiple tables.
          * **Named:** A [named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#named-stages) provides high flexibility for data loading. Users with the appropriate privileges on the stage can load data into any table. Furthermore, because the stage is a database object, any security or access rules that apply to all objects will apply to the named stage.

          Named stages can be altered and dropped. User stages cannot.
        </ResponseField>

        {/* <!-- param-start:[snowflake-output-connector-v2.snowflake#internalNamedStage, snowflake-output-connector-v1.snowflake#internalNamedStage] | warehouses: [snowflake] --> */}

        <ResponseField name="Named Stage" type="drop-down" required>
          Select your named stage. Read [Creating a named stage](https://docs.snowflake.com/en/user-guide/data-load-local-file-system-create-stage#creating-a-named-stage) to learn how to create a new named stage.

          <Note>
            You can't select a named stage if its name includes special characters or spaces.
          </Note>
        </ResponseField>
      </Tab>

      <Tab title="Cloud Storage">
        <ResponseField name="Load Strategy" type="drop-down">
          * **Append Files in Folder:** Appends files to storage folder. This is the default setting.
          * **Overwrite Files in Folder:** Overwrite existing files with matching structure.
        </ResponseField>

        {/* <!-- param-start:[storage-only-output-v2.folderPath, storage-only-output-v1.folderPath] | warehouses: [snowflake] --> */}

        <ResponseField name="Folder Path" type="string">
          The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name.
        </ResponseField>

        {/* <!-- param-start:[storage-only-output-v2.filePrefix, storage-only-output-v1.filePrefix] | warehouses: [snowflake] --> */}

        <ResponseField name="File Prefix" type="string">
          A string of characters that precedes the name of the written files. This can be useful for organizing database objects.
        </ResponseField>

        {/* <!-- param-start:[storage-only-output-v2.storage, storage-only-output-v1.storage] | warehouses: [snowflake] --> */}

        <ResponseField name="Storage" type="drop-down" required>
          A cloud storage location to load your data into files for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
        </ResponseField>
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Google BigQuery">
    <ResponseField name="Destination" type="drop-down" required>
      Select the destination for your data:

      * **Google BigQuery:** Load your data into a table in Google BigQuery.
      * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location. The format of these files can differ between source systems and will not have a file extension. Check the output to determine the format of the data.
    </ResponseField>

    <Tabs>
      <Tab title="Google BigQuery">
        <ResponseField name="Project" type="drop-down" required>
          Select the Google BigQuery project to load data into. The special value `[Environment Default]` uses the project defined in the environment.
        </ResponseField>

        <ResponseField name="Dataset" type="drop-down" required>
          Select the Google BigQuery dataset to load data into. The special value `[Environment Default]` uses the dataset defined in the environment.
        </ResponseField>

        <ResponseField name="Table Name" type="string" required>
          The name of the table to be created in your Google BigQuery project. You can use a [Table Input](/docs/components/table-input) component in a transformation pipeline to access and transform this data after it has been loaded.
        </ResponseField>

        <ResponseField name="Load Strategy" type="drop-down" required>
          Define what happens if the table name already exists in the specified destination. Only available when **Full Load** is selected.

          * **Replace:** If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
          * **Truncate and Insert:** Each time the pipeline runs, two operations are performed: first, the table is truncated, meaning all existing rows are deleted. Then, your new rows are inserted. The table itself is never destroyed and recreated.
          * **Fail if Exists:** If the specified table name already exists, this pipeline will fail to run.
          * **Append:** If the specified table name already exists, then the data is inserted without altering or deleting the existing data in the table. It's appended onto the end of the existing data in the table. If the specified table name doesn't exist, then the table will be created, and your data will be inserted into the table.
        </ResponseField>

        <ResponseField name="Primary Keys" type="object selector">
          Select one or more columns to be designated as the table's primary key.

          When using **Incremental Load**, if you select a primary key, the loaded data will be merged with your existing data. If you don't select a primary key, the loaded data will be appended to your existing data.
        </ResponseField>

        <ResponseField name="Clean Staged Files" type="boolean" required>
          * **Yes:** Staged files will be destroyed after data is loaded. This is the default setting.
          * **No:** Staged files are retained in the staging area after data is loaded.
        </ResponseField>

        <ResponseField name="Partition Type" type="drop-down">
          Select the time period to use to partition the data loaded into your table, for example **Day** or **Month**.
        </ResponseField>

        <ResponseField name="GCS Bucket" type="drop-down" required>
          Select the Google Cloud Storage (GCS) bucket linked to your Google Cloud Platform account.
        </ResponseField>

        <ResponseField name="Overwrite" type="boolean" required>
          Select whether to overwrite files of the same name when this pipeline runs. Default is **Yes**.
        </ResponseField>
      </Tab>

      <Tab title="Cloud Storage">
        <ResponseField name="Load Strategy" type="drop-down">
          * **Append Files in Folder:** Appends files to storage folder. This is the default setting.
          * **Overwrite Files in Folder:** Overwrite existing files with matching structure.
        </ResponseField>

        <ResponseField name="Folder Path" type="string">
          The folder path for the files to be written to. Note that this path follows, but does not include, the bucket or container name.
        </ResponseField>

        <ResponseField name="File Prefix" type="string">
          A string of characters that precedes the name of the written files. This can be useful for organizing database objects.
        </ResponseField>

        <ResponseField name="Storage" type="drop-down" required>
          A cloud storage location to load your data into files for storage. Choose either Amazon S3, Azure Storage, or Google Cloud Storage.
        </ResponseField>
      </Tab>
    </Tabs>
  </Tab>
</Tabs>

### Advanced Settings

<ResponseField name="Auto Debug" type="boolean" required>
  Choose whether to automatically log debug information about your load. These logs can be found in the task history and should be included in support requests concerning the component. This property is set to **No** by default. Turning this on will override any debugging **Connection Options** you may have set.
</ResponseField>

{/* <!-- param-start:[jira-input-v1.debugLevel] | warehouses: [snowflake] --> */}

<ResponseField name="Debug Level" type="drop-down" required>
  The level of detail you want to include in your debug logs. Select a level between **1** and **4**:

  1. Will log the query, the number of rows returned by it, the start of execution, the time taken, and any errors.
  2. Will log everything included in Level 1, plus cache queries and additional information about the request, if applicable.
  3. Will log everything included in Levels 1 and 2, and additionally log the body of the request and the response. This is the default logging level when debug logging is activated.
  4. Will log everything included in Levels 1, 2, and 3, and additionally log transport-level communication with the data source. This includes SSL negotiation.

  Levels above 1 can log huge amounts of data and result in slower query execution.
</ResponseField>

***

## Data model

The JDBC driver for this component models Excel APIs as relational databases and stored procedures, which are documented in the data model. You'll also find API limitations and requirements.

* [Connection options](https://cdn.cdata.com/help/RXM/jdbc/Connection.htm)

This connector also allows you to query system tables in Advanced mode. To see the available system tables in the data model, read the [System Tables](https://cdn.cdata.com/help/RXM/jdbc/pg_allsystables.htm) section of the data model. For more information about using system tables, read our [System tables](/docs/guides/system-tables-example) guide.