> ## 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.

# SurveyMonkey

export const maia = "Maia";

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", "Amazon Redshift", "Google BigQuery"]} unsupportedWarehouses={[]} componentType="Connector, Orchestration" connectionInputs="One" connectionOutputs="Unlimited" />

The SurveyMonkey orchestration component uses the Connect and Configure parameters to create a table of SurveyMonkey data, which is then stored in your preferred storage location (Snowflake, Databricks, Amazon Redshift, or cloud storage). You *do not* need to use the Create Table component when using this connector, as the SurveyMonkey component will create a new table or replace an existing table for you using the Destination parameters you define.

If the component requires access to a cloud provider (AWS, Azure, or Google Cloud), it will use the [cloud credentials](/docs/guides/cloud-credentials) associated with your environment to access resources.

To stage data to Azure Blob Storage, the Azure credentials associated with your environment must be assigned the `Storage Blob Data Contributor` role. For more information, read [User assigned with the Storage Blob Data Contributor role](https://learn.microsoft.com/en-us/answers/questions/1155139/user-assigned-with-storage-blob-data-contributor-r).

***

## 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="Authentication Type" type="drop-down" required>
  Select **OAuth 2.0 Authorization Code** from the drop-down menu.
</ResponseField>

{/* <!-- param-start:[survey-monkey-input-v0.connection.secretReferenceId] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Authentication" type="drop-down" required>
  Choose your OAuth connection from the drop-down menu.

  Click **Manage** to navigate to the **OAuth connections** list to review OAuth connections and to add new connections. Read [OAuth](/docs/guides/oauth) to learn how to create an OAuth connection.

  To set up a new OAuth:

  1. Click **Manage** under the drop-down menu.
  2. Click **Add OAuth connection** above the **OAuth connections** list.
  3. Give an appropriate **OAuth name**.
  4. Use the drop-down menu to select the **SurveyMonkey** provider.
  5. Select **OAuth 2.0 Authorization Code Grant** from the **Authentication type** drop-down.
  6. Click **Authorize**.
  7. The website for SurveyMonkey will be displayed. Log in, and you will be redirected to {maia}, where your OAuth connection will be created.
</ResponseField>

{/* <!-- param-start:[survey-monkey-input-v0.connectionOptions] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Connection Options" type="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#text-mode).
</ResponseField>

### Configure

<ResponseField name="Mode" type="drop-down" required>
  * **Basic:** This mode will build a query for you using settings from the **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.

  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>

{/* <!-- param-start:[survey-monkey-input-v0.sqlQuery] | warehouses: [snowflake, databricks, redshift] --> */}

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

  * [Snowflake](https://docs.snowflake.com/en/sql-reference/sql/select)
  * [Databricks](https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-qry-select.html)
  * [Amazon Redshift](https://docs.aws.amazon.com/redshift/latest/dg/r_SELECT_synopsis.html)
</ResponseField>

{/* <!-- param-start:[survey-monkey-input-v0.dataSource] | warehouses: [snowflake, databricks, redshift] --> */}

<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>

{/* <!-- param-start:[survey-monkey-input-v0.dataSelection] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Data Selection" type="dual listbox" required>
  Choose one or more columns to return from the query. The columns available are dependent upon the data source selected. Move columns left-to-right to include in the query.

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

{/* <!-- param-start:[survey-monkey-input-v0.dataSourceFilter] | warehouses: [snowflake, databricks, redshift] --> */}

<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.

  Click the **Text Mode** toggle at the bottom of the dialog to open a multi-line editor. For more information, read [Text mode](/docs/guides/components-overview#component-properties).
</ResponseField>

{/* <!-- param-start:[survey-monkey-input-v0.combineFilters] | warehouses: [snowflake, databricks, redshift] --> */}

<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>

{/* <!-- param-start:[survey-monkey-input-v0.limit] | warehouses: [snowflake, databricks, redshift] --> */}

<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>

### Destination

Select your cloud data warehouse.

<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>

    <Tabs>
      <Tab title="Snowflake">
        {/* <!-- param-start:[snowflake-output-connector-v0.warehouse, snowflake-output-connector-v1.warehouse] | warehouses: [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-end:[snowflake-output-connector-v0.warehouse, snowflake-output-connector-v1.warehouse] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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-end:[snowflake-output-connector-v0.database, snowflake-output-connector-v1.database] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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-end:[snowflake-output-connector-v0.schema, snowflake-output-connector-v1.schema] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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>

        {/* <!-- param-end:[snowflake-output-connector-v0.tableName, snowflake-output-connector-v1.tableName] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.createTableMode, snowflake-output-connector-v1.createTableMode] | warehouses: [snowflake] --> */}

        <ResponseField name="Load Strategy" type="drop-down" required>
          Define what happens if the table name already exists in the specified Snowflake database and schema.

          * **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>

        {/* <!-- param-end:[snowflake-output-connector-v0.createTableMode, snowflake-output-connector-v1.createTableMode] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.primaryKeys, snowflake-output-connector-v1.primaryKeys] | warehouses: [snowflake] --> */}

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

        {/* <!-- param-end:[snowflake-output-connector-v0.primaryKeys, snowflake-output-connector-v1.primaryKeys] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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-end:[snowflake-output-connector-v0.cleanStagedFiles, snowflake-output-connector-v1.cleanStagedFiles] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.stageAccessStrategyForS3, snowflake-output-connector-v1.stageAccessStrategyForS3] | warehouses: [snowflake] --> */}

        <ResponseField name="Stage Access Strategy" type="drop-down">
          Select the stage access strategy. The strategies available depend on the cloud platform you select in **Stage Platform**.

          * **Credentials:** Connects to the external stage (AWS, Azure) using your configured [cloud provider credentials](/docs/guides/cloud-credentials). Not available for Google Cloud Storage.
          * **Storage Integration:** Use a Snowflake storage integration to grant access to Snowflake to read data from and write to a cloud storage location. This will reveal the **Storage Integration** property, through which you can select any of your existing Snowflake storage integrations.
        </ResponseField>

        {/* <!-- param-end:[snowflake-output-connector-v0.stageAccessStrategyForS3, snowflake-output-connector-v1.stageAccessStrategyForS3] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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-end:[snowflake-output-connector-v0.stagePlatform, snowflake-output-connector-v1.stagePlatform] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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-end:[snowflake-output-connector-v0.snowflake#internalStageType, snowflake-output-connector-v1.snowflake#internalStageType] --> */}

        {/* <!-- param-start:[snowflake-output-connector-v0.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.

          <Warning>
            There is a known issue where named stages that include special characters or spaces are not supported.
          </Warning>
        </ResponseField>

        {/* <!-- param-end:[snowflake-output-connector-v0.snowflake#internalNamedStage, snowflake-output-connector-v1.snowflake#internalNamedStage] --> */}
      </Tab>

      <Tab title="Cloud Storage">
        {/* <!-- param-start:[storage-only-output-v0.prepareStageStrategy, storage-only-output-v1.prepareStageStrategy] | warehouses: [snowflake] --> */}

        <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-end:[storage-only-output-v0.prepareStageStrategy, storage-only-output-v1.prepareStageStrategy] --> */}

        {/* <!-- param-start:[storage-only-output-v0.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-end:[storage-only-output-v0.folderPath, storage-only-output-v1.folderPath] --> */}

        {/* <!-- param-start:[storage-only-output-v0.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-end:[storage-only-output-v0.filePrefix, storage-only-output-v1.filePrefix] --> */}

        {/* <!-- param-start:[storage-only-output-v0.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>

        {/* <!-- param-end:[storage-only-output-v0.storage, storage-only-output-v1.storage] --> */}
      </Tab>
    </Tabs>
  </Tab>

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

      * **Databricks:** Load your data into Databricks. You'll need to set a cloud storage location for temporary staging of the data.
      * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location.
    </ResponseField>

    <Tabs>
      <Tab title="Databricks">
        {/* <!-- param-start:[databricks-output-connector-v0.catalog, databricks-output-connector-v1.catalog] | warehouses: [databricks] --> */}

        <ResponseField name="Catalog" type="drop-down" required>
          Select a [Databricks Unity Catalog](https://docs.databricks.com/en/data-governance/unity-catalog/index.html). The special value `[Environment Default]` uses the catalog defined in the environment. Selecting a catalog will determine which databases are available in the next parameter.
        </ResponseField>

        {/* <!-- param-end:[databricks-output-connector-v0.catalog, databricks-output-connector-v1.catalog] --> */}

        {/* <!-- param-start:[databricks-output-connector-v0.schema, databricks-output-connector-v1.schema] | warehouses: [databricks] --> */}

        <ResponseField name="Schema" type="drop-down" required>
          The Databricks schema. The special value `[Environment Default]` uses the schema defined in the environment. Read [Create and manage schemas](https://docs.databricks.com/en/data-governance/unity-catalog/create-schemas.html) to learn more.
        </ResponseField>

        {/* <!-- param-end:[databricks-output-connector-v0.schema, databricks-output-connector-v1.schema] --> */}

        {/* <!-- param-start:[databricks-output-connector-v0.tableName, databricks-output-connector-v1.tableName] | warehouses: [databricks] --> */}

        <ResponseField name="Table Name" type="string" required>
          The name of the table to be created in your Databricks schema. 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>

        {/* <!-- param-end:[databricks-output-connector-v0.tableName, databricks-output-connector-v1.tableName] --> */}

        {/* <!-- param-start:[databricks-output-connector-v0.loadStrategy, databricks-output-connector-v1.loadStrategy] | warehouses: [databricks] --> */}

        <ResponseField name="Load Strategy" type="drop-down" required>
          Define what happens if the table name already exists in the specified Databricks schema.

          * **Fail if Exists:** If the specified table name already exists, this pipeline will fail to run.
          * **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.
          * **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>

        {/* <!-- param-end:[databricks-output-connector-v0.loadStrategy, databricks-output-connector-v1.loadStrategy] --> */}

        {/* <!-- param-start:[databricks-output-connector-v0.cleanStagedFiles, databricks-output-connector-v1.cleanStagedFiles] | warehouses: [databricks] --> */}

        <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-end:[databricks-output-connector-v0.cleanStagedFiles, databricks-output-connector-v1.cleanStagedFiles] --> */}

        {/* <!-- param-start:[databricks-output-connector-v0.stagePlatform, databricks-output-connector-v1.stagePlatform] | warehouses: [databricks] --> */}

        <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 Databricks table.

          * **Amazon S3:** Stage your data on an AWS S3 bucket.
          * **Azure Storage:** Stage your data in an Azure Blob Storage container.
        </ResponseField>

        {/* <!-- param-end:[databricks-output-connector-v0.stagePlatform, databricks-output-connector-v1.stagePlatform] --> */}
      </Tab>

      <Tab title="Cloud Storage">
        {/* <!-- param-start:[storage-only-output-v0.prepareStageStrategy, storage-only-output-v1.prepareStageStrategy] | warehouses: [databricks] --> */}

        <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-end:[storage-only-output-v0.prepareStageStrategy, storage-only-output-v1.prepareStageStrategy] --> */}

        {/* <!-- param-start:[storage-only-output-v0.folderPath, storage-only-output-v1.folderPath] | warehouses: [databricks] --> */}

        <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-end:[storage-only-output-v0.folderPath, storage-only-output-v1.folderPath] --> */}

        {/* <!-- param-start:[storage-only-output-v0.filePrefix, storage-only-output-v1.filePrefix] | warehouses: [databricks] --> */}

        <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-end:[storage-only-output-v0.filePrefix, storage-only-output-v1.filePrefix] --> */}

        {/* <!-- param-start:[storage-only-output-v0.storage, storage-only-output-v1.storage] | warehouses: [databricks] --> */}

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

        {/* <!-- param-end:[storage-only-output-v0.storage, storage-only-output-v1.storage] --> */}
      </Tab>
    </Tabs>
  </Tab>

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

      * **Redshift:** Load your data into Amazon Redshift. You'll need to set a cloud storage location for temporary staging of the data.
      * **Cloud Storage:** Load your data directly into files in your preferred cloud storage location.
    </ResponseField>

    <Tabs>
      <Tab title="Amazon Redshift">
        {/* <!-- param-start:[redshift-output-connector-v0.schema, redshift-output-connector-v1.schema] | warehouses: [redshift] --> */}

        <ResponseField name="Schema" type="drop-down" required>
          Select the Amazon Redshift schema that will contain your table. The special value `[Environment Default]` uses the schema defined in the environment. For information about using multiple schemas, read [Schemas](https://docs.aws.amazon.com/redshift/latest/dg/r_Schemas_and_tables.html).
        </ResponseField>

        {/* <!-- param-end:[redshift-output-connector-v0.schema, redshift-output-connector-v1.schema] --> */}

        {/* <!-- param-start:[redshift-output-connector-v0.table, redshift-output-connector-v1.table] | warehouses: [redshift] --> */}

        <ResponseField name="Table Name" type="string" required>
          The name of the table to be created in your Amazon Redshift 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>

        {/* <!-- param-end:[redshift-output-connector-v0.table, redshift-output-connector-v1.table] --> */}

        {/* <!-- param-start:[redshift-output-connector-v0.createTableMode, redshift-output-connector-v1.createTableMode] | warehouses: [redshift] --> */}

        <ResponseField name="Load Strategy" type="drop-down" required>
          Define what happens if the table name already exists in the specified Amazon Redshift database and schema.

          * **Replace:** If the specified table name already exists, that table will be destroyed and replaced by the table created during this pipeline run.
          * **Fail if Exists:** If the specified table name already exists, this pipeline will fail to 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.
          * **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>

        {/* <!-- param-end:[redshift-output-connector-v0.createTableMode, redshift-output-connector-v1.createTableMode] --> */}

        {/* <!-- param-start:[redshift-output-connector-v0.cleanStagedFiles, redshift-output-connector-v1.cleanStagedFiles] | warehouses: [redshift] --> */}

        <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-end:[redshift-output-connector-v0.cleanStagedFiles, redshift-output-connector-v1.cleanStagedFiles] --> */}

        {/* <!-- param-start:[redshift-output-connector-v0.s3#bucket, redshift-output-connector-v1.s3#bucket] | warehouses: [redshift] --> */}

        <ResponseField name="Amazon S3 Bucket" type="drop-down" required>
          An AWS S3 bucket to stage data into before it is loaded into your Amazon Redshift table. The drop-down menu will include buckets tied to the [cloud provider credentials](/docs/guides/cloud-credentials) that you have associated with your [environment](/docs/guides/environments).
        </ResponseField>

        {/* <!-- param-end:[redshift-output-connector-v0.s3#bucket, redshift-output-connector-v1.s3#bucket] --> */}
      </Tab>

      <Tab title="Cloud Storage">
        {/* <!-- param-start:[storage-only-output-v0.prepareStageStrategy, storage-only-output-v1.prepareStageStrategy] | warehouses: [redshift] --> */}

        <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-end:[storage-only-output-v0.prepareStageStrategy, storage-only-output-v1.prepareStageStrategy] --> */}

        {/* <!-- param-start:[storage-only-output-v0.folderPath, storage-only-output-v1.folderPath] | warehouses: [redshift] --> */}

        <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-end:[storage-only-output-v0.folderPath, storage-only-output-v1.folderPath] --> */}

        {/* <!-- param-start:[storage-only-output-v0.filePrefix, storage-only-output-v1.filePrefix] | warehouses: [redshift] --> */}

        <ResponseField name="File Prefix" type="string">
          A string of characters to include at the beginning of the written files. Often used for organizing database objects.
        </ResponseField>

        {/* <!-- param-end:[storage-only-output-v0.filePrefix, storage-only-output-v1.filePrefix] --> */}

        {/* <!-- param-start:[storage-only-output-v0.storage, storage-only-output-v1.storage] | warehouses: [redshift] --> */}

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

        {/* <!-- param-end:[storage-only-output-v0.storage, storage-only-output-v1.storage] --> */}
      </Tab>
    </Tabs>
  </Tab>

  <Tab title="Google BigQuery">
    <BigqueryDestination />

    <Tabs>
      <Tab title="Google BigQuery">
        <BigqueryProject />

        <BigqueryDataset />

        <BigqueryTableName />

        <LoadStrategy />

        <PrimaryKeys />

        <CleanStagedFiles />

        <BigqueryPartitionType />

        <BigqueryGcsBucket />

        <Overwrite />
      </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="Output Encoding" type="drop-down">
  Set the file format used to stage extracted records from this component before loading. The default is `CSV`. `CSV` is compact and widely compatible, while `JSON Lines` preserves the difference between NULL and empty-string values.
</ResponseField>

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

<ResponseField name="Key Sanitization Strategy" type="drop-down">
  Choose how to change the column names in your data so that they conform to the destination warehouse's identifier rules:

  * `None`: (Default) Do not change the source column names.
  * `Alphanumeric (lower case)`: Replace non-alphanumeric characters with underscores, change the name to lowercase, and add an underscore at the start of the name if it begins with a digit.
  * `Alphanumeric (upper case)`: Replace non-alphanumeric characters with underscores, change the name to uppercase, and add an underscore at the start of the name if it begins with a digit.

  If two source column names sanitize to the same result, the load fails with an error rather than dropping data.
</ResponseField>

{/* <!-- param-start:[survey-monkey-input-v0.autoDebug] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Auto Debug" type="boolean">
  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:[survey-monkey-input-v0.debugLevel] | warehouses: [snowflake, databricks, redshift] --> */}

<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>

{/* <!-- param-start:[snowflake-output-connector-v0.stringNullIsNull, snowflake-output-connector-v1.stringNullIsNull, snowflake-output-connector-v2.stringNullIsNull] | warehouses: [snowflake] --> */}

<ResponseField name="Parse 'Null' & Empty Strings as NULL" type="boolean" required>
  Converts common strings that represent null into a null value. This is case-sensitive and works with the following strings: "", "NULL", "NUL", "Null", "null". The default is **No**.

  <Note>
    Currently, this property is only applicable when using Snowflake as your destination.
  </Note>
</ResponseField>

{/* <!-- param-start:[snowflake-output-connector-v0.trimStringColumns, snowflake-output-connector-v1.trimStringColumns, snowflake-output-connector-v2.trimStringColumns] | warehouses: [snowflake] --> */}

<ResponseField name="Trim String Columns" type="boolean" required>
  When **Yes**, remove leading and trailing characters from a string column. The default is **No**.
</ResponseField>

## Data model

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

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

***

## Deactivate soft delete for Azure blobs (Databricks)

If you intend to set your destination as Databricks *and* your stage platform as Azure Storage, you must turn off the "Enable soft delete for blobs" setting in your Azure account for your pipeline to run successfully. To do this:

1. In the [Azure portal](https://portal.azure.com/), navigate to your storage account.
2. In the menu, under **Data management**, click **Data protection**.
3. Clear the **Enable soft delete for blobs** checkbox. For more information, read [Soft delete for blobs](https://learn.microsoft.com/en-gb/azure/storage/blobs/soft-delete-blob-overview).