> ## 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.

# JDBC

export const m_runner = "Maia runner";

export const maia = "Maia";

export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => {
  const allWarehouses = [...warehouses.map(w => ({
    name: w,
    supported: true
  })), ...unsupportedWarehouses.map(w => ({
    name: w,
    supported: false
  }))];
  return <div style={{
    background: 'var(--colors-background-light, #f9fafb)',
    border: '1px solid var(--colors-border-default, #e5e7eb)',
    borderRadius: '12px',
    padding: '20px 28px',
    marginBottom: '28px',
    boxShadow: '0 1px 4px rgba(0,0,0,0.10)'
  }}>
      <table style={{
    width: '100%',
    borderCollapse: 'collapse'
  }}>
        <tbody>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle',
    width: '180px'
  }}>Project Availability</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>
              <div style={{
    display: 'flex',
    flexWrap: 'wrap',
    gap: '8px'
  }}>
                {allWarehouses.map((w, i) => <span key={i} style={{
    background: w.supported ? '#dcfce7' : '#fee2e2',
    color: w.supported ? '#15803d' : '#b91c1c',
    border: `1px solid ${w.supported ? '#bbf7d0' : '#fca5a5'}`,
    borderRadius: '9999px',
    padding: '3px 12px',
    fontSize: '0.85rem',
    fontWeight: '500',
    whiteSpace: 'nowrap'
  }}>
                    {w.name} {w.supported ? '✅' : '❌'}
                  </span>)}
              </div>
            </td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Component Type</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{componentType}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    paddingBottom: '14px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Inputs</td>
            <td style={{
    paddingBottom: '14px',
    verticalAlign: 'middle'
  }}>{connectionInputs}</td>
          </tr>
          <tr>
            <td style={{
    fontWeight: '600',
    paddingRight: '32px',
    whiteSpace: 'nowrap',
    verticalAlign: 'middle'
  }}>Connection Outputs</td>
            <td style={{
    verticalAlign: 'middle'
  }}>{connectionOutputs}</td>
          </tr>
        </tbody>
      </table>
    </div>;
};

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

<Warning>
  For Snowflake and Google BigQuery projects, this component is superseded by the [JDBC Load](/docs/components/jdbc-load) component, which offers both full and incremental loading.

  Existing pipelines in Snowflake projects using the JDBC component will continue to work as expected, but new pipelines in Snowflake and Google BigQuery projects must use the JDBC Load component instead. When opening a pipeline containing the JDBC component, it may appear grayed out. This indicates that you should replace the existing component with the new JDBC Load component.

  Databricks and Amazon Redshift projects should continue to use the JDBC component.
</Warning>

The JDBC orchestration component allows you to run queries on a JDBC-compatible data source that you have a driver for. You can upload your drivers to {maia}, and so connect to any sources that {maia} doesn't provide drivers for as standard.

<Note>
  * This component is only available for use with [Hybrid SaaS](/docs/guides/runner-overview#hybrid-saas) {m_runner}s.
  * When adding new drivers, a [{m_runner} restart](/docs/guides/restart-runner) is required to recognize the configuration. Once the {m_runner} has restarted, you can choose the driver from the **Database Type** property in the component.
</Note>

To use this component, you must upload a suitable JDBC driver for your data source, following the process given in [Uploading external drivers to the {m_runner}](/docs/guides/uploading-external-drivers). You can upload drivers for multiple different data sources, and select the required source from a drop-down when configuring the component.

You must also create a [manifest file](#manifest-file), which will contain configuration information for your data sources.

You *do not* need to use the Create Table component when using this connector, as the JDBC component will create a new table or replace an existing table for you using the Destination parameters you define.

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

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

***

## Properties

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

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

### Connect

<ResponseField name="Database Type" type="drop-down" required>
  Select the database type. The drop-down will contain all databases that you have included in the [manifest file](#manifest-file).
</ResponseField>

{/* <!-- param-start:[jdbc-input-v2.connectionUrl] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Connection URL" type="string" required>
  The URL for your chosen JDBC database. If the field displays a template URL for the database, replace any placeholder values with the actual values of your database's URL.

  Although many parameters and options can be added to the end of the URL, it's generally easier to add them in the **Connection Options** property, documented below.
</ResponseField>

{/* <!-- param-start:[jdbc-input-v2.connection.authType] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Authentication Type" type="drop-down" required>
  Currently only supports **Username & Password** or **None**.
</ResponseField>

{/* <!-- param-start:[jdbc-input-v2.connection.overrides.username] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Username" type="string" required>
  Your username for the JDBC data source.
</ResponseField>

{/* <!-- param-start:[jdbc-input-v2.connection.secretReferenceId] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Password" type="drop-down" required>
  Displays a drop-down list of secret definitions. Select the [secret definition](/docs/guides/secrets-and-secret-definitions) that references the password corresponding to the username.
</ResponseField>

{/* <!-- param-start:[jdbc-input-v2.connectionOptions] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Connection Options" type="column editor" required>
  * **Parameter:** A JDBC parameter supported by the database driver. The available parameters are explained in the data model. Manual setup is not usually required, since sensible defaults are assumed.
  * **Value:** A value for the given parameter.

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

### Configure

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

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

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

{/* <!-- param-start:[jdbc-input-v2.schema] | warehouses: [snowflake, databricks, redshift] --> */}

<ResponseField name="Schema" type="drop-down">
  Select the database schema to load data from.
</ResponseField>

{/* <!-- param-start:[jdbc-input-v2.sqlQuery] | warehouses: [snowflake, databricks, redshift] --> */}

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

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

{/* <!-- param-start:[jdbc-input-v2.dataSource] | warehouses: [snowflake, databricks, redshift] --> */}

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

{/* <!-- param-start:[jdbc-input-v2.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:[jdbc-input-v2.dataSourceFilter] | warehouses: [snowflake, databricks, redshift] --> */}

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

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

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

{/* <!-- param-start:[jdbc-input-v2.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:[jdbc-input-v2.limit] | warehouses: [snowflake, databricks, redshift] --> */}

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

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

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

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

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

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

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

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

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

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

## Manifest file

The manifest file is a JSON format text file that includes the configuration information needed to connect to the data source. Every data source that you [upload an external driver](/docs/guides/uploading-external-drivers) for must have a corresponding entry in the manifest file.

The manifest file must be named `jdbc-driver-manifest.json` and saved to the storage location that also holds the uploaded external driver files.

A manifest file must be in valid JSON format and strictly follow this structure:

```json theme={null}
[
    {
      "name": "<database_type>",
      "driverClassName": "<driver_class_name>",
      "baseUrl": "<base_url>",
      "limitStrategy": "<limit_strategy>",
      "fetchSize": <fetch_size>,
      "defaultProperties": {
        "<property>": "<value>",
        ...
      },
      "blacklistedProperties": [
        "<property>",
        ...
      ],
      "propertyOverrides": {
        "<property>": "<value>",
        ...
      },
      "skipQueryValidation": "true"
    }
]
```

A single manifest file can include the configuration for several different database types by repeating this structure for each type.

Replace the parameters shown within `< >` above with the actual values required by the driver you are using, in accordance with the following notes:

* **name:** This should be a simple name for the driver that identifies the database type it's used with. For example `MySQL`, `Mailchimp`, or `PostgreSQL`. The name will appear in the component's **Database Type** drop-down.

* **driverClassName:** The fully qualified name of a class that implements the `java.sql.Driver` interface in Java. The fully qualified name of the driver class includes the package name followed by the class name, ensuring it's unique across all Java packages. For example, the driver class name for MySQL's Connector/J driver is `com.mysql.cj.jdbc.Driver`, and the class name for the PostgreSQL driver is `org.postgresql.Driver`.

* **baseUrl:** A template URL used to prompt the user to enter the correct format of URL in the component's **Connection URL** property. This should be the basic URL for the driver, such as `jdbc:postgresql://` or `jdbc:cdata:mailchimp:`.

* **limitStrategy:** Controls how the component gets metadata about the query. Different databases have different ways of supporting this. Enter the method required by the data source you are configuring:
  * `limit-inline`: The most straightforward and widely supported method for limiting query results, commonly used in MySQL, PostgreSQL, SQLite, and others.
  * `top-n`: Similar to `limit-inline`, but commonly used in SQL Server and Sybase.
  * `fetch-first-n`: Similar to `limit-inline`, but supported by newer versions of databases such as Db2, Oracle (12c and later), and PostgreSQL.
  * `rownum`: Uses a pseudocolumn in Oracle to limit the number of rows returned, and is particularly useful for complex queries or pagination.
  * `limit-outer`: A sophisticated technique to limit query results by wrapping the main query within an outer query where the limit is applied. This strategy is particularly useful for complex queries that require pre-processing steps before the application of the limit. Commonly used in databases that can use optimized nested queries.
  * `none`: No limit is applied to the query, either due to retrieving all records for processing or when the dataset is known to be within acceptable size limits. Commonly used in all SQL databases.

* **fetchSize:** Used in conjunction with the **limitStrategy**, this is the number of rows fetched at once, if supported by the database driver. Enter this as a JSON integer value, i.e. not within `" "`.

* **defaultProperties:** Define any required defaults for driver properties. These defaults can be overridden by the user when the component is configured (but see **blacklistedProperties**, below, for how to prevent overrides).

  List the name of each default property and its default value as a name:value pair inside the `defaultProperties` object. This is a JSON object requiring `{ }` delimiters. For example:

  ```json theme={null}
  "defaultProperties": {
      "connectTimeout": "20000",
      "allowLoadLocalInfile": "false",
      "allowUrlInLocalInfile": "false",
      "allowLocalInfile": "false"
  },
  ```

  If there are no default properties, this element is still required and should be an empty object:

  ```json theme={null}
  "defaultProperties": {},
  ```

* **blacklistedProperties:** List any properties that you don't want to be accessible from the UI. For example, if you have set a default property here in the manifest file that you don't want the pipeline creator to be able to override in the UI, you should list it here.

  List the name of each blacklisted property inside the `blacklistedProperties` array. This is a JSON array requiring `[ ]` delimiters. For example:

  ```json theme={null}
  "blacklistedProperties": [
      "allowLoadLocalInfile",
      "allowUrlInLocalInfile",
      "allowLocalInfile"
  ],
  ```

  If there are no blacklisted properties, this element is still required and should be an empty array:

  ```json theme={null}
  "blacklistedProperties": [],
  ```

* **propertyOverrides:** If the driver is expecting property names that are different from those used by the component, you can map the expected names here. For example, MySQL expects "user" instead of "username" as used by the component, so in a manifest file for MySQL you would specify: `"username": "user"`.

  List each required override inside the `propertyOverrides` object. This is a JSON object requiring `{ }` delimiters. For example:

  ```json theme={null}
  "propertyOverrides": {
      "username": "user",
      "password": "pass"
  }
  ```

  If there are no required overrides, this element is still required and should be an empty object:

  ```json theme={null}
  "propertyOverrides": {}
  ```

* **skipQueryValidation:** The component's default behavior is to validate a query written in **Advanced** mode by fetching metadata from the data source. In some cases, returning metadata takes a considerable time, resulting in slow validation of the component. To avoid such delays, include this property in your manifest file and set it to `true`. This property is optional, and should be omitted if not needed.

The following sections include some examples of suitable manifest files for specific database types that {maia} has tested with this component. For other database types not covered here, you will need to refer to the database documentation, along with the guidance given above, to determine which properties and values are required in your manifest file.

### MySQL

The following manifest can be used to configure MySQL as a data source.

```json theme={null}
[
    {
      "name": "MySQL",
      "driverClassName": "com.mysql.jdbc.Driver",
      "baseUrl": "jdbc:mysql://",
      "limitStrategy": "limit-inline",
      "fetchSize": 500,
      "defaultProperties": {
        "connectTimeout": "20000",
        "allowLoadLocalInfile": "false",
        "allowUrlInLocalInfile": "false",
        "allowLocalInfile": "false"
      },
      "blacklistedProperties": [
      ],
      "propertyOverrides": {
        "username": "user",
        "password": "password"
      }
    }
]
```

You can download an example of this file [here](https://matillion-docs.s3.eu-west-1.amazonaws.com/Attachments/dpc-jdbc/jdbc-driver-manifest.json) to use as a template for creating your own manifest file.

### SAP Hana

The following manifest can be used to configure SAP Hana as a data source.

```json theme={null}
[
    {
      "name" : "SAPHana",
      "driverClassName" : "com.sap.db.jdbc.Driver",
      "baseUrl" : "jdbc:sap://",
      "fetchSize" : 500,
      "limitStrategy" : "top-n",
      "defaultProperties": {
      },
      "blacklistedProperties": [
      ],
      "propertyOverrides": {
        "username": "user",
        "password": "password"
      }
    }
]
```

### InterSystems Caché

The following manifest can be used to configure InterSystems Caché as a data source.

```json theme={null}
[
   {
    "name": "Intersystems Cache",
    "driverClassName": "com.intersys.jdbc.CacheDriver",
    "baseUrl": "jdbc:Cache://",
    "limitStrategy": "top-n",
    "fetchSize": 500,
    "defaultProperties": {
      "connectTimeout": "20000"
    },
    "blacklistedProperties": [],
    "propertyOverrides": {
      "username": "user",
      "password": "password"
    }
  }
]
```

### Mailchimp

The following manifest can be used to configure Mailchimp as a data source.

```json theme={null}
{
    "name": "mailchimp",
    "baseUrl": "jdbc:cdata:mailchimp:",
    "limitStrategy": "limit-inline",
    "driverClassName": "cdata.jdbc.mailchimp.MailChimpDriver",
    "blacklistedProperties": [
        "logfile",
        "verbosity"
    ],
    "defaultProperties": {
        "rtk": "434D524A5641535552425641454E54503156474A3138323200000000000000000000000000000000384A36435347324400005239584145445A39344D545A0000"
    }
}
```

### Informix

The following manifest can be used to configure Informix as a data source.

```json theme={null}
{
    "name": "Informix",
    "driverClassName": "com.informix.jdbc.IfxDriver",
    "baseUrl": "jdbc:informix-sqli://",
    "limitStrategy": "limit-inline",
    "fetchSize": 500,
    "defaultProperties": {},
    "blacklistedProperties": [],
    "propertyOverrides": {
    	"username": "user"
    }
}
```

***

## 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).

***

## Additional MySQL information

When working with MySQL databases, specific driver requirements apply based on your deployment model:

* **Licensing restrictions:** Due to licensing constraints, {maia} cannot include the MySQL driver.
* **For Full SaaS customers:** Use the MariaDB driver with the following components:
  * [Database Query](/docs/components/database-query)
  * [JDBC Table Metadata to Grid](/docs/components/jdbc-table-metadata-to-grid)
* **For Hybrid SaaS customers:** In addition to the MariaDB driver, you can configure and use the native MySQL driver via the JDBC component for enhanced compatibility.