> ## 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.

# RDS Query

export const m_runner = "Maia runner";

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

The RDS Query component runs SQL queries on an RDS database and then copies the results to a table via Amazon S3 (Amazon Simple Storage Service).

Each time the RDS Query component 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.

Once the component has run once, you can use transformation pipelines to transform your data to fit your business requirements.

## Authentication model

The RDS Query component uses two distinct sets of credentials for different purposes:

* **Database authentication:** Access to the RDS database itself is strictly managed via username and password (stored as a secret definition). IAM roles and AWS cloud credentials are **not** used for database authentication.
* **Cloud credentials:** These are used for cloud service interactions such as discovering RDS endpoints and staging data in cloud storage.
  * If your {m_runner} runs in AWS, the component can use the {m_runner}'s Task Role (IAM role) to access Amazon S3 and AWS APIs.
  * If the {m_runner} runs outside AWS, you must configure [AWS cloud credentials](/docs/guides/cloud-credentials#acquiring-aws-credentials) linked to an IAM user and [associate them with your environment](/docs/guides/cloud-credentials#associate-cloud-provider-credentials-with-an-environment).
  * If staging data to another cloud storage platform (such as Azure Blob Storage or Google Cloud Storage), you must configure the appropriate cloud credentials for that platform.

For this component, cloud provider credentials are primarily used for staging data in cloud storage and accessing AWS services.

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 to your environment, these will overwrite the role.

<Note>
  - If you're using a Matillion Full SaaS solution, you may need to [allow these IP address ranges](/docs/security/network-access-and-ip-allowlist-requirements#full-saas) from which Full SaaS {m_runner}s will call out to their source systems or to cloud data platforms.
  - Cloud credentials or IAM roles are used only for accessing cloud infrastructure services, **not** for authenticating to the RDS database.
</Note>

<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>

***

## Video example

<iframe width="560" height="315" src="https://www.youtube.com/embed/z8wIKtTLWQI?si=wzswhZs_2p2cIqjt&enablejsapi=1" title="YouTube video player" frameBorder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share; fullscreen" referrerPolicy="strict-origin-when-cross-origin" allowFullScreen />

***

## 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="Database Type" type="drop-down" required>
  Select the database type from:

  * Aurora
  * MariaDB
  * Microsoft SQL Server
  * MySQL
  * Oracle
  * PostgreSQL

  PostgreSQL is the default.
</ResponseField>

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

<ResponseField name="RDS Endpoint" type="drop-down" required>
  Select an RDS database endpoint from the drop-down menu. By default, this property offers the user a list of all the RDS instances available within the user's current region that are the same type as the selected database type. If the desired endpoint is located in a different region, or the user is not running on Amazon EC2 and therefore does not have a region, they can provide values manually. To acquire your database endpoint and provide it manually, follow these steps:

  1. Log in to the AWS Console.
  2. In the **Find Services** search bar, search for **RDS**.
  3. In the **Amazon RDS** navigation column on the left side of your screen, click **Databases**.
  4. Select a database.
  5. Locate the endpoint for that database in the **Connectivity & security** section.

  While this field is a drop-down menu, you can also manually edit a selected endpoint URL. Ensure the port number is included when manually typing the endpoint.
</ResponseField>

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

<ResponseField name="Database Name" type="string" required>
  Provide the name of the database within your RDS instance. In the AWS Console, this is the "DB identifier".
</ResponseField>

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

<ResponseField name="Username" type="string" required>
  The database-level username for the RDS instance.
</ResponseField>

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

<ResponseField name="Password" type="drop-down">
  Choose the secret definition that represents your credentials for this connector.

  If you have not already saved your credentials for this connector as a secret definition, click **Add secret** to create a secret definition representing these credentials. Read [Secrets and secret definitions](/docs/guides/secrets-and-secret-definitions) for details about creating a secret definition.

  <Note>
    RDS Query doesn't support IAM-based database authentication (for example, IAM authentication tokens). If the database requires password-based authentication and no password is provided, the component run will fail.
  </Note>
</ResponseField>

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

<ResponseField name="JDBC Options" type="column editor">
  * **Parameter:** A JDBC parameter supported by the database driver. Consult the specific database documentation for more details.
  * **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).
</ResponseField>

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

<ResponseField name="SSH Tunnel" type="drop-down">
  Select an SSH Tunnel from the list of [Network](/docs/guides/networks) items. For detailed usage instructions, read the [SSH Tunneling](/docs/security/ssh-tunnelling#how-to-use-it) documentation.

  <Note>
    If selected, the **RDS Endpoint** will be the data source that your secure tunnel connects to.
  </Note>
</ResponseField>

### Configure

<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 **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 documentation specific to the database product.

  <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:[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)

  For detailed information about tables and views for this connector, read the section about the data model, found below.
</ResponseField>

{/* <!-- param-start:[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.

  For detailed information about tables and views for this connector, read the section about the data model, found below.
</ResponseField>

{/* <!-- param-start:[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:[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". "Equal to" can match exact strings and numeric values, while other comparators, such as "Greater than" and "Less than", will work only with numerics. The "Like" operator allows the wildcard character `%` to be used at the start and end of a string value to match a column. The Null operator matches only null values, ignoring whatever the value is set to. Not all data sources support all comparators, meaning that it is likely that only a subset of the above comparators will be available to choose from.
  * **Value:** The value to be compared.

  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).
</ResponseField>

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

<ResponseField name="Combine Filters" type="drop-down" required>
  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:[limit] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Limit" type="integer">
  Set a numeric value to limit the number of rows that are loaded. The default is `100`. To load all rows from your data source, delete the default `100` and leave the field empty (i.e. do not set a limit).
</ResponseField>

### Destination

Select your cloud data warehouse.

<Tabs>
  <Tab title="Snowflake">
    <ResponseField name="Type" type="drop-down" required>
      * **Standard:** The data will be staged in your storage location before being loaded into a table. This is the only setting currently available.
    </ResponseField>

    {/* <!-- param-start:[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-start:[warehouse, warehouse1] | 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-start:[database, database1] | warehouses: [snowflake] --> */}

    <ResponseField name="Database" type="drop-down" required>
      The Snowflake database. 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:[schema, schema1] | 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:[targetTable] | warehouses: [snowflake] --> */}

    <ResponseField name="Target Table" type="string" required>
      The name of the table to be created in your Snowflake database. This table will be recreated and will drop any existing table of the same name.

      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-start:[stage, stage1] | warehouses: [snowflake] --> */}

    <ResponseField name="Stage" type="drop-down" required>
      Select a managed stage. The special value, \[Custom], will create a stage "on the fly" for use solely within this component.
    </ResponseField>

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

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

      * **Existing Amazon S3 Location:** Activates the **S3 Staging Area** property, allowing users to specify a custom staging area on Amazon S3. The **Stage Authentication** property is also activated, letting users select a method of authenticating the data staging.
      * **Existing Azure Blob Storage Location:** Activates the **Storage Account** and **Blob Container** properties, allowing users to specify a custom staging location on Azure. The **Stage Authentication** property is also activated, letting users select a method of authenticating the data staging.
      * **Existing Google Cloud Storage Location:** Activates the **Storage Integration** and **GCS Staging Area** properties, allowing users to specify a custom staging area within Google Cloud Storage.
      * **Snowflake Managed:** Create and use a temporary internal stage on Snowflake for staging the data. This stage, along with the staged data, will cease to exist after loading is complete. This is the default setting.
    </ResponseField>

    {/* <!-- param-start:[stageAuthentication, stageAuthentication1] | warehouses: [snowflake] --> */}

    <ResponseField name="Stage Authentication" type="drop-down" required>
      Select an authentication method for data staging. Only available when **Stage Platform** is set to either **Existing Amazon S3 Location** or **Existing Azure Blob Storage Location**.

      * **Credentials:** Uses the credentials configured in the environment. If no credentials have been configured, an error will occur.
      * **Storage Integration:** Use a Snowflake storage integration to authentication data staging.
    </ResponseField>

    {/* <!-- param-start:[storageIntegration, storageIntegration1] | warehouses: [snowflake] --> */}

    <ResponseField name="Storage Integration" type="drop-down" required>
      Select a Snowflake storage integration from the drop-down list. Storage integrations are required to permit Snowflake to read data from and write to your cloud storage location and must be [set up in advance of selection](https://docs.snowflake.com/en/sql-reference/sql/create-storage-integration).
    </ResponseField>

    {/* <!-- param-start:[s3StagingArea] | warehouses: [snowflake] --> */}

    <ResponseField name="S3 Staging Area" type="drop-down" required>
      Select an S3 bucket for temporary storage. Ensure your access credentials have S3 access and permission to write to the bucket. The temporary objects created in this bucket will be removed again after the load completes.
    </ResponseField>

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

    <ResponseField name="Storage Account" type="drop-down" required>
      Select a storage account with your desired blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview).
    </ResponseField>

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

    <ResponseField name="Blob Container" type="drop-down" required>
      Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction).
    </ResponseField>

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

    <ResponseField name="GCS Staging Area" type="drop-down" required>
      The URL and path of the target Google Cloud Storage bucket to be used for staging the queried data.
    </ResponseField>
  </Tab>

  <Tab title="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-start:[schema2, database2] | warehouses: [databricks] --> */}

    <ResponseField name="Schema (Database)" 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-start:[table] | warehouses: [databricks] --> */}

    <ResponseField name="Table" type="string" required>
      The name of the table to be created in your Databricks schema. This table will be recreated and will drop any existing table of the same name.

      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-start:[stagePlatform1] | warehouses: [databricks] --> */}

    <ResponseField name="Stage Platform" type="drop-down" required>
      Choose where the data is staged before being loaded into your Databricks table using the drop-down menu.

      * **AWS S3:** Lets users specify a custom staging area on Amazon S3.
      * **Azure Blob:** Lets users specify a custom staging area on Azure Blob storage.
      * **Personal Staging (deprecated):** Uses a Databricks personal staging location. This option has been deprecated by Databricks.
      * **Volume:** Use a pre-existing [Databricks volume](https://docs.databricks.com/en/sql/language-manual/sql-ref-volumes.html) to stage your data. Only external volumes are available.
    </ResponseField>

    {/* <!-- param-start:[s3StagingArea] | warehouses: [databricks] --> */}

    <ResponseField name="S3 Staging Area" type="drop-down" required>
      Select an S3 bucket for temporary storage. The temporary objects created in this bucket will be removed again after the load completes.
    </ResponseField>

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

    <ResponseField name="Storage Account" type="drop-down" required>
      Select a storage account with your desired Blob container to be used for staging the data. For more information, read [Storage account overview](https://learn.microsoft.com/en-us/azure/storage/common/storage-account-overview).
    </ResponseField>

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

    <ResponseField name="Blob Container" type="drop-down" required>
      Select a Blob container to be used for staging the data. For more information, read [Introduction to Azure Blob storage](https://learn.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction).
    </ResponseField>

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

    <ResponseField name="Volume" type="drop-down" required>
      Select a [Databricks volume](https://docs.databricks.com/en/sql/language-manual/sql-ref-volumes.html).
    </ResponseField>
  </Tab>

  <Tab title="Amazon Redshift">
    <ResponseField name="Type" type="drop-down" required>
      * **Standard:** The data will be staged in your storage location before being loaded into a table. This is the only setting currently available.
    </ResponseField>

    {/* <!-- param-start:[schema3] | warehouses: [redshift] --> */}

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

    {/* <!-- param-start:[targetTable1] | warehouses: [redshift] --> */}

    <ResponseField name="Target Table" type="string" required>
      The name of the table to be created in your Amazon Redshift schema. This table will be recreated and will drop any existing table of the same name.

      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-start:[s3StagingArea1] | warehouses: [redshift] --> */}

    <ResponseField name="S3 Staging Area" type="S3 bucket" required>
      Select an S3 bucket for temporary storage. The temporary objects created in this bucket will be removed again after the load completes.
    </ResponseField>

    {/* <!-- param-start:[distributionStyle] | warehouses: [redshift] --> */}

    <ResponseField name="Distribution Style" type="drop-down" required>
      Table distribution is critical to good performance. Read the [Distribution styles](https://docs.aws.amazon.com/redshift/latest/dg/c_choosing_dist_sort.html) documentation for more information.

      * **All:** Copy rows to all nodes in the Redshift cluster.
      * **Auto:** (Default) Allow Redshift to manage your distribution style.
      * **Even:** Distribute rows around the Redshift cluster evenly.
      * **Key:** Distribute rows around the Redshift cluster according to the value of a key column.
    </ResponseField>

    {/* <!-- param-start:[distributionKey] | warehouses: [redshift] --> */}

    <ResponseField name="Distribution Key" type="drop-down" required>
      This parameter is only available when **Distribution Style** is set to **Key**. Select a column from the input that should be set as the table's distribution key. This column should be chosen carefully, as it will determine how the data is distributed around the Redshift cluster.
    </ResponseField>

    {/* <!-- param-start:[sortKey] | warehouses: [redshift] --> */}

    <ResponseField name="Sort Key" type="dual listbox" required>
      Sort keys are critical to good performance. Read [Working with sort keys](https://docs.aws.amazon.com/redshift/latest/dg/t_Sorting_data.html) for more information.

      This property is optional, and lets you specify one or more columns from the input that should be set as the table's sort key.
    </ResponseField>

    {/* <!-- param-start:[sortKeyOptions] | warehouses: [redshift] --> */}

    <ResponseField name="Sort Key Options" type="drop-down" required>
      Decide whether the sort key is of a compound or interleaved variety.
    </ResponseField>

    {/* <!-- param-start:[primaryKey] | warehouses: [redshift] --> */}

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

### Advanced Settings

<Tabs>
  <Tab title="Snowflake">
    <ResponseField name="Load Options" type="multiple drop-downs" required>
      * **Clean Staged Files:** Destroy staged files after loading data. Default is **On**.
      * **String Null is Null:** Converts any strings equal to null into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is **Off**.
      * **Recreate Target Table:** Choose whether the component recreates its target table before the data load. If **Off**, the existing table will be used. Default is **On**.
      * **File Prefix:** Give staged file names a prefix of your choice. Default is empty (no prefix).
      * **Trim String Columns:** Remove leading and trailing characters from a string column. Default is **On**.
      * **Compression Type:** Set the compression type to either **gzip** (default) or **None**.
    </ResponseField>
  </Tab>

  <Tab title="Databricks">
    <ResponseField name="Load Options" type="multiple drop-downs" required>
      * **Clean Staged Files:** Destroy staged files after loading data. Default is **On**.
      * **String Null is Null:** Converts any strings equal to null into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is **Off**.
      * **Recreate Target Table:** Choose whether the component recreates its target table before the data load. If **Off**, the existing table will be used. Default is **On**.
      * **File Prefix:** Give staged file names a prefix of your choice. Default is empty (no prefix).
      * **Compression Type:** Set the compression type to either **gzip** (default) or **None**.
    </ResponseField>
  </Tab>

  <Tab title="Amazon Redshift">
    <ResponseField name="Load Options" type="multiple drop-downs" required>
      * [Comp Update:](https://docs.aws.amazon.com/redshift/latest/dg/copy-parameters-data-load.html#copy-compupdate) Apply automatic compression to the target table. Default is On.
      * [Stat Update:](https://docs.aws.amazon.com/redshift/latest/dg/copy-parameters-data-load.html#copy-statupdate) Automatically update statistics when filling a table. Default is On.
      * **Clean S3 Objects:** Automatically remove UUID-based objects on the S3 bucket. Default is On.
      * **String Null is Null:** Converts any strings equal to "null" into a null value. This is case-sensitive and only works with entirely lower-case strings. Default is On.
      * **Recreate Target Table:** Choose whether the component recreates its target table before the data load. If Off, the existing table will be used. Default is On.
      * **File Prefix:** Give staged file names a prefix of your choice.
      * **Compression Type:** Set the compression type to either gzip (default) or None.
    </ResponseField>
  </Tab>
</Tabs>

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

<ResponseField name="Encryption" type="drop-down" required>
  Decide how the files are encrypted inside the S3 bucket. This property is available when using an existing Amazon S3 location for staging.

  * **None:** No encryption.
  * **SSE KMS:** Encrypt the data according to a key stored on KMS. Read [AWS Key Management Service (AWS KMS)](https://aws.amazon.com/kms/) to learn more.
  * **SSE S3:** Encrypt the data according to a key stored on an S3 bucket. Read [Using server-side encryption with Amazon S3-managed encryption keys (SSE-S3)](https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingServerSideEncryption.html) to learn more.
</ResponseField>

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

<ResponseField name="KMS Key ID" type="drop-down" required>
  The ID of the KMS encryption key you have chosen to use in the **Encryption** property.
</ResponseField>

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

<ResponseField name="Auto Debug" type="drop-down" 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. Turning this on will override any debugging connection options.
</ResponseField>

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

<ResponseField name="Debug Level" type="drop-down" required>
  The level of verbosity with which your debug information is logged. Levels above 1 can log huge amounts of data and result in slower execution. These logs can be found in the **Message** field of the [task details](/docs/guides/designer-ui-basics#task-history-tab) after pipeline execution and should be included in support requests concerning the component.

  1. Will log the query, the number of rows returned by it, the start of execution and 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 additionally log the body of the request and the response.
  4. Will additionally log transport-level communication with the data source. This includes SSL negotiation.
  5. Will additionally log communication with the data source, as well as additional details that may be helpful in troubleshooting problems. This includes interface commands.
</ResponseField>