> ## 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.

# SAP NetWeaver

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

The SAP NetWeaver component uses the SAP Java Connector (SAP JCo) to connect to SAP systems via the SAP RFC protocol, letting you retrieve your SAP NetWeaver data to load into a table in your data warehouse (Snowflake, Databricks, or Amazon Redshift) or cloud storage solution (Amazon S3, Azure Blob Storage, or Google Cloud Storage).

Loading the data into your cloud data warehouse stages the data, so the table is reloaded each time. You can then use transformations to enrich and manage the data in permanent tables. You do not need to set up a Create Table component when using this component.

<Note>
  This component is only available for use with [Hybrid SaaS](/docs/guides/runner-overview#hybrid-saas) {m_runner}s.
</Note>

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).

***

## Uploading SAP drivers

Drivers for SAP NetWeaver are not natively included in [Hybrid SaaS](/docs/guides/runner-overview#hybrid-saas) {m_runner}s, but can be uploaded to your {m_runner} instance using the process in [Uploading external drivers to the {m_runner}](/docs/guides/uploading-external-drivers).

Two driver files are required:

* `sapjco3.jar`
* `libsapjco3.so`

You can obtain these drivers as a single ZIP file from [Download SAP Java Connector 3.1 SDK](https://support.sap.com/en/product/connectors/jco.html?anchorId=section_2129803369), selecting **Linux for Intel compatible processors**. Unzip the file and place the drivers in the storage location you specified as described in [Uploading external drivers to the {m_runner}](/docs/guides/uploading-external-drivers). Do **not** change the driver file names.

***

## 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 the method through which to authenticate to SAP. Currently supports Username and Password.
</ResponseField>

{/* <!-- param-start:[sap-netweaver-input-v1.connection.overrides.host] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Host" type="string">
  Input the host name or IP address of the SAP NetWeaver server.

  Read [Connecting to an SAP load balancer](#connecting-to-an-sap-load-balancer) if you wish to configure your SAP environment to use a load balancer. In such instances, keep the **Host** parameter empty.
</ResponseField>

{/* <!-- param-start:[sap-netweaver-input-v1.connection.overrides.username] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Username" type="string" required>
  A valid SAP NetWeaver username.
</ResponseField>

{/* <!-- param-start:[sap-netweaver-input-v1.connection.overrides.password] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Password" type="string" required>
  The [secret definition](/docs/guides/secrets-and-secret-definitions) holding your password tied to your username for your SAP account. Your password should be saved as a secret definition before using this component.
</ResponseField>

{/* <!-- param-start:[sap-netweaver-input-v1.connection.overrides.client] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Client" type="string" required>
  Specify the client authenticating to the SAP system.
</ResponseField>

{/* <!-- param-start:[sap-netweaver-input-v1.connection.overrides.systemNumber] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="System Number" type="integer" required>
  A two-digit identifier (ranging from 00 to 99) that uniquely identifies an instance of the SAP system in the host machine.
</ResponseField>

{/* <!-- param-start:[sap-netweaver-input-v1.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="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:[sap-netweaver-input-v1.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:[sap-netweaver-input-v1.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:[sap-netweaver-input-v1.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:[sap-netweaver-input-v1.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>
</Tabs>

### Advanced Settings

<ResponseField name="Page Size" type="integer">
  The default is `50000`.
</ResponseField>

## Connecting to SAP products

The RFC protocol is used to connect to the following SAP systems:

* SAP BW
* SAP ECC
* SAP ERP
* SAP R/3
* SAP S/4 HANA

***

## Connecting to an SAP Load Balancer

You can configure an SAP environment to use a load balancer, called a message server. This load balancer may have its own port, which must be specified for a connection to succeed. Read below to learn how to connect to an SAP load balancer.

1. The **Host** parameter must be left empty.
2. Set the **User** and **Password** parameters as normal with the correct credentials.
3. In **Connection Options**, set up three connection options as per the table below.

| Parameter            | Value           |
| -------------------- | --------------- |
| MessageServer        | example.8u98900 |
| MessageServerService | 3603            |
| Group                | PUBLIC          |

The **Host** parameter is not set because the `MessageServer` connection option takes its place.

***

## 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).