> ## 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 ODP

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

The SAP ODP orchestration component enables you to connect directly through SAP ODP to access available data sources in SAPI and ABAP CDS views. This data 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 SAP ODP component will create a new table or replace an existing table for you using the Destination parameters you define.

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

<Warning>
  In SAP ODP V2, if there is no data in the selected data source the component will fail on execution with the following error:

  ```
  No sample data available for schema detection from Data Source
  ```

  This happens because the component requires data to understand the correct structure for table creation and data loading.
</Warning>

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 ODP 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 authentication method. Only **Username/Password** is supported currently.
</ResponseField>

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

<ResponseField name="Username" type="string" required>
  Your SAP username.
</ResponseField>

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

<ResponseField name="Password" type="drop-down" 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-odp-input-v1.serverConnection] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Server Connection" type="drop-down" required>
  The connection method. Choose either **Direct** or **Load Balancer**. The following connection parameters will change depending on the connection method.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.encryption] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Encryption" type="drop-down" required>
  Select either **None** or **TLS**. Only required if **Server Connection** is Direct. For TLS encryption, a **Web Socket Host**, **Web Socket Port**, and **TLS/SSL Certificate** are required. For no encryption, a **Host** and **System Number** are required.
</ResponseField>

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

<ResponseField name="Host" type="string" required>
  SAP ABAP application server host DNS. This is the host name of your target system. Only required for Direct server connections with no encryption.
</ResponseField>

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

<ResponseField name="System Number" type="integer" required>
  The system number of the SAP ABAP app server. Your system number should have been provided at the point of installation. For ECC, it is usually `10`. For S/4H, it is usually `00`. Only required for Direct server connections with no encryption.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.webSocketHost] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Web Socket Host" type="string" required>
  Enter the host name for the TLS encrypted WebSocket connection. Only required for Direct server connections with TLS encryption.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.webSocketPort] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Web Socket Port" type="string" required>
  Enter the port number used for the TLS encrypted WebSocket connection. Only required for Direct server connections with TLS encryption.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.certificate] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="TLS/SSL Certificate" type="string" required>
  The TLS encryption certificate. Paste the text content of the certificate into the **TLS/SSL Certificate** dialog. Only required for Direct server connections with TLS encryption.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.messageServerHost] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Message Server Host" type="string" required>
  Enter the DNS host name or IP address of the SAP message server. Only required for Load Balancer connections.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.messageServerService] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Message Server Service" type="string">
  Enter a SAP message server port number. This is optional. To resolve service names `sapmsXXX`, a lookup in `etc/services` is performed by the network layer of the operating system. If you're using port numbers instead of symbolic service names, no look-ups will be performed and no additional entries are needed. Only required for Load Balancer connections.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.groupServer] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Group Server" type="string">
  If using a group of SAP application servers, use this property to identify the application servers used (the "logon group"). This parameter is optional and is only required if used on your SAP system. The default value is a single `SPACE`. Only required for Load Balancer connections.
</ResponseField>

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

<ResponseField name="Client" type="integer" required>
  Enter the SAP client number. The default is 100. From SAP help: "A SAP client is defined as a self-contained commercial, organizational, and technical unit within an SAP system. All business data within a client are protected from other clients." Read more about this at [Customer Data and System Data](https://help.sap.com/saphelp_snc70/helpdata/EN/5d/a9463c288dbc5de10000000a11402f/content.htm?no_cache=true).
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.language] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Language" type="string" required>
  Defaults to `en`. If not defined, the SAP system returns to JCo the default user language, and JCo will use that default value. Language codes adhere to [ISO 639-1 codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes).
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.connectionOptions] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Connection Options" type="column editor">
  Additional connection options for SAP ODP. Add a new row with **+**; remove a row with **-**; or click **Add All**. For each connection option, select the **Parameter** from the drop-down list and enter its **Value** as a string.
</ResponseField>

### Configure

<ResponseField name="Context" type="drop-down" required>
  List of contexts available in the SAP system for the subscriber type `SAP_BW`. The context acts as a "data provider" for the data source. In SAP, a data provider permits configuration of data for extraction when targeting a specific use.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.search] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Data Source Search Term" type="string">
  Search string used to identify and reduce the quantity of data sources returned by SAP. This is optional. For example, enter `TEXT` to filter the list of data sources to those containing the string "text". The maximum search string length is 30 characters.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.dataSource] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Data Source" type="drop-down" required>
  Select the SAP ODP data source. Set the **Data Source Search Term** property as a filter to limit the items in the list, if required.

  In the context of SAP ODP, a data source is an "extractor". In SAP, an extractor is a data extraction through a specified context. A data source can be the result of aggregation of one or more tables or views in SAP. A semantic character is also provided. Additionally, a contextual description is given where a data source has been provided with description metadata in SAP.

  The data sources available show a concatenation of:

  * The data source technical name.
  * A semantic (the SAP type of data source).
  * The SAP data source description, in the language specified at login.

  Semantics glossary:

  H = Hierarchy
  F = Transaction Data/Facts
  P = Master Data/Attributes
  T = Texts
  V = View
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.dataSelection] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Data Selection" type="dual listbox">
  Columns to include in the extraction. Columns will also have a contextual description where applicable. This property is optional.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.dataSourceFilter] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Data Source Filter" type="column editor">
  * **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.

  This property is optional.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.loadType] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Load Type" type="drop-down" required>
  Select a load type to manage the quantity of data sent via the network. This is especially useful when dealing with large RFC table parameters. Available options depend on your chosen data source. Possible types are Full Load, Delta Load, and Recovery (available only if the data source supports deltas). The default is Full Load.

  When Full Load is selected, the whole table is sent back to the caller. When Delta is selected, only appended, deleted, and updated table rows are transferred back to the caller.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.deltaInitialization] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Delta Initialisation" type="string" required>
  Only used if **Load Type** is Delta. To only extract changes (creates, modifications, and deletes) from now onwards, enter `X`. Leave blank (the default) to also return historical changes.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.recoveryPointer] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Recovery Pointer" type="string" required>
  Only available if the data source supports deltas, and the **Load Type** is Recovery.

  SAP ODP allows you to repeat the extraction of data that may have been lost or corrupted due to an interruption of service, to restore and guarantee the data integrity of the extracted data. A recovery pointer is set by SAP ODP at a specific point-in-time in the delta queue, and you can use this to recover any data change recorded from that point onward. When the recovery pointer is set and the pipeline repeated, all the data changes that happened from that recovery pointer onward are extracted, while all data changes that happened before that recovery pointer are **not** extracted.

  You must obtain the value of the recovery pointer from the **Replication Pointers** table in SAP. After a data extraction that you believe to have been interrupted, refresh the SAP **Replication Pointers** table and make a note of the pointer. The pointer is a string of digits in the following format: `20240131082113.000021000`. This is a year-month-day-hour-minute-second timestamp, and must be in this exact format; any format other than this will lead to an error. This string should be entered in the **Recovery Pointer** property to tell the component where in the delta queue to begin the recovery run.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.subscriberName] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Subscriber Name" type="string" required>
  The subscriber is the consumer of the data. Subscriber name is assigned by the user to allow the identification of which app is consuming the data. The maximum string size is 32 characters.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.subscriberProcess] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Subscriber Process" type="string" required>
  The identifier for the extraction process of the subscriber. The maximum string size is 64 characters.
</ResponseField>

{/* <!-- param-start:[sap-odp-input-v1.subscriberRun] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Subscriber Run" type="string">
  The run ID of the subscriber. For example, `${dt.now()}` The maximum string size is 64 characters. This property is optional.
</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="Max Package Size" type="integer" required>
  Byte integer representing the package size of the data in SAP. The default setting is 5,000,000 bytes.

  Max Package Size is based on the size of the package in SAP, measured in compressed bytes. This size may need to be tweaked depending on the content of the package and the size constraint of the cloud data platform.
</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>

## 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).