> ## Documentation Index
> Fetch the complete documentation index at: https://docs.open-metadata.org/llms.txt
> Use this file to discover all available pages before exploring further.

# DeltaLake Connector | OpenMetadata Data Lake Integration

> Connect OpenMetadata to Delta Lake with our comprehensive database connector guide. Step-by-step setup, configuration, and metadata extraction instructions.

export const MetadataIngestionUi = ({connector, selectServicePath, addNewServicePath, serviceConnectionPath}) => {
  return <>
    <p>
      To ingest metadata from your sources, you need to create a service connection.
      The service connects your source system with OpenMetadata. Once you create
      a service, you can use it to configure your ingestion workflows.<br />
      <br />
      To create a service connection and ingest your metadata, follow the steps below:
    </p>
      <Steps>
      <Step title="Select the Service">
        <ol>
          <li>
            On the left navigation bar, click <strong>Settings</strong>.
          </li>
          <li>
            On the next page, click <strong>Services</strong>, and then select the service.
            <img src="/public/images/connectors/visit-services-page.png" alt="Visit Services Page" />
          </li>
        </ol>
      </Step>

      <Step title="Create a New Service">
        To add a new service connection, click <strong>Add New Service</strong>.
        <img src="/public/images/connectors/create-new-service.png" alt="Create a new Service" />
      </Step>

      <Step title="Select the Connector">
        Select <strong>{connector}</strong> as the service type and click <strong>Next</strong>.

        {selectServicePath && <img src={selectServicePath} alt="Select Service" />}
      </Step>

      <Step title="Name and Describe the Service">
        Enter a unique <strong>Service Name</strong> and <strong>Description</strong>.
        <ul>
         <li><strong>Service Name</strong>: OpenMetadata identifies services by their service name. Enter a name that distinguishes this deployment from other services, including other {connector} services you are ingesting metadata from.</li>
        </ul>

        <Note>
          The service name cannot be changed after it is set.
       </Note>

        {addNewServicePath && <img src={addNewServicePath} alt="Add New Service" />}
      </Step>

      <Step title="Configure the Service Connection">
        Set up the connection settings required for {connector} to set up the service and start ingesting metadata from your sources. The right-hand panel displays help documentation for the selected connection type in the product UI.
        {serviceConnectionPath && <img src={serviceConnectionPath} alt="Configure Service connection" />}
      </Step>
    </Steps>
  </>;
};

export const ConnectorDetailsHeader = ({name, icon, stage, availableFeatures, unavailableFeatures = [], availableFeaturesCollate = []}) => {
  const showSubHeading = availableFeatures?.length > 0 || unavailableFeatures?.length > 0 || availableFeaturesCollate?.length > 0;
  const totalAvailableFeatures = [...availableFeatures || [], ...availableFeaturesCollate || []];
  return <div className="container">
      <div className="Heading">
        <div className="flex items-center gap-3">
          {icon && <div className="IconContainer">
              <img src={icon} alt={name} noZoom className="ConnectorIcon" />
            </div>}
          <h1 className="ConnectorName">{name}</h1>
          <span className={`StageBadge ${stage === 'PROD' ? 'prod' : 'beta'}`}>
            {stage}
          </span>
        </div>
      </div>
      {showSubHeading && <div className="SubHeading">
          <div className="FeaturesHeading">Feature List</div>
          <div className="FeaturesList">
            {totalAvailableFeatures.map(feature => <div className="FeatureTag AvailableFeature" key={feature}>
                ✓ {feature}
              </div>)}
            {unavailableFeatures.map(feature => <div className="FeatureTag UnavailableFeature" key={feature}>
                ✕ {feature}
              </div>)}
          </div>
        </div>}
    </div>;
};

<ConnectorDetailsHeader icon="/public/images/connectors/delta-lake.webp" name="Delta Lake" stage="PROD" availableFeatures={["Metadata", "dbt"]} unavailableFeatures={["Query Usage", "Data Profiler", "Data Quality", "Lineage", "Column-level Lineage", "Owners", "Tags", "Stored Procedures", "Sample Data", "Auto-Classification"]} />

In this section, we provide guides and references to use the Delta Lake connector.
Configure and schedule Delta Lake metadata and profiler workflows from the OpenMetadata UI:

* [Requirements](#requirements)
* [Metadata Ingestion](#metadata-ingestion)
* [dbt Integration](/v1.12.x/connectors/database/dbt)
* [Troubleshooting](/v1.12.x/connectors/database/deltalake/troubleshooting)

## Requirements

Delta Lake requires to run with Python 3.9, 3.10 or 3.11. We do not yet support the Delta connector
for Python 3.11
The Delta Lake connector is able to extract the information from a **metastore** or directly from the **storage**.
If extracting directly from the storage, some extra requirements are needed depending on the storage

### S3 Permissions

To execute metadata extraction AWS account should have enough access to fetch required data. The <strong>Bucket Policy</strong> in AWS requires at least these permissions:

```json theme={null}
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::<my bucket>",
                "arn:aws:s3:::<my bucket>/*"
            ]
        }
    ]
}
```

## Metadata Ingestion

<MetadataIngestionUi connector={"DeltaLake"} selectServicePath={"/public/images/connectors/deltalake/select-service.png"} addNewServicePath={"/public/images/connectors/deltalake/add-new-service.png"} serviceConnectionPath={"/public/images/connectors/deltalake/service-connection.png"} />

## Connection Details

<Steps>
  <Step title="Connection Details For MetastoreConfig">
    * **Metastore Host Port**: Enter the Host & Port of Hive Metastore Service to configure the Spark Session. Either
      of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
    * **Metastore File Path**: Enter the file path to local Metastore in case Spark cluster is running locally. Either
      of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
    * **Metastore DB**: The JDBC connection to the underlying Hive metastore DB. Either
      of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
    * **appName (Optional)**: Enter the app name of spark session.
    * **Connection Arguments (Optional)**: Key-Value pairs that will be used to pass extra `config` elements to the Spark Session builder.
      We are internally running with `pyspark` 3.X and `delta-lake` 2.0.0. This means that we need to consider Spark configuration options for 3.X.
      **Metastore Host Port**
      When connecting to an External Metastore passing the parameter `Metastore Host Port`, we will be preparing a Spark Session with the configuration

    ```
    .config("hive.metastore.uris", "thrift://{connection.metastoreHostPort}")
    ```

    Then, we will be using the `catalog` functions from the Spark Session to pick up the metadata exposed by the Hive Metastore.
    **Metastore File Path**
    If instead we use a local file path that contains the metastore information (e.g., for local testing with the default `metastore_db` directory), we will set

    ```
    .config("spark.driver.extraJavaOptions", "-Dderby.system.home={connection.metastoreFilePath}")
    ```

    To update the `Derby` information. More information about this in a great [SO thread](https://stackoverflow.com/questions/38377188/how-to-get-rid-of-derby-log-metastore-db-from-spark-shell).

    * You can find all supported configurations [here](https://spark.apache.org/docs/latest/configuration.html)
    * If you need further information regarding the Hive metastore, you can find it [here](https://spark.apache.org/docs/latest/sql-data-sources-hive-tables.html),
      and in The Internals of Spark SQL [book](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-hive-metastore.html).
      **Metastore Database**
      You can also connect to the metastore by directly pointing to the Hive Metastore db, e.g., `jdbc:mysql://localhost:3306/demo_hive`.
      Here, we will need to inform all the common database settings (url, username, password), and the driver class name for JDBC metastore.
      You will need to provide the driver to the ingestion image, and pass the `classpath` which will be used in the Spark Configuration under `spark.driver.extraClassPath`.

    #### Connection Details for StorageConfig - S3

    * **AWS Access Key ID** & **AWS Secret Access Key**: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have
      permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and
      authorize your requests ([docs](https://docs.aws.amazon.com/IAM/latest/UserGuide/security-creds.html)).
      Access keys consist of two parts: An **access key ID** (for example, `AKIAIOSFODNN7EXAMPLE`), and a **secret access key** (for example, `wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY`).
      You must use both the access key ID and secret access key together to authenticate your requests.
      You can find further information on how to manage your access keys [here](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html).
    * **AWS Region**: Each AWS Region is a separate geographic area in which AWS clusters data centers ([docs](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.RegionsAndAvailabilityZones.html)).
      As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.
      Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the
      services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.
      You can find further information about configuring your credentials [here](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials).
    * **AWS Session Token (optional)**: If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID
      and AWS Secrets Access Key. Also, these will include an AWS Session Token.
      You can find more information on [Using temporary credentials with AWS resources](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_use-resources.html).
    * **Endpoint URL (optional)**: To connect programmatically to an AWS service, you use an endpoint. An *endpoint* is the URL of the
      entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the
      default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.
      Find more information on [AWS service endpoints](https://docs.aws.amazon.com/general/latest/gr/rande.html).
    * **Profile Name**: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command.
      When you specify a profile to run a command, the settings and credentials are used to run that command.
      Multiple named profiles can be stored in the config and credentials files.
      You can inform this field if you'd like to use a profile other than `default`.
      Find here more information about [Named profiles for the AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-profiles.html).
    * **Assume Role Arn**: Typically, you use `AssumeRole` within your account or for cross-account access. In this field you'll set the
      `ARN` (Amazon Resource Name) of the policy of the other account.
      A user who wants to access a role in a different account must also have permissions that are delegated from the account
      administrator. The administrator must attach a policy that allows the user to call `AssumeRole` for the `ARN` of the role in the other account.
      This is a required field if you'd like to `AssumeRole`.
      Find more information on [AssumeRole](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html).
    * **Assume Role Session Name**: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role
      is assumed by different principals or for different reasons.
      By default, we'll use the name `OpenMetadataSession`.
      Find more information about the [Role Session Name](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html#:~:text=An%20identifier%20for%20the%20assumed%20role%20session.).
    * **Assume Role Source Identity**: The source identity specified by the principal that is calling the `AssumeRole` operation. You can use source identity
      information in AWS CloudTrail logs to determine who took actions with a role.
      Find more information about [Source Identity](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html#:~:text=Required%3A%20No-,SourceIdentity,-The%20source%20identity).
  </Step>

  <Step title="Advanced Configuration">
    Database Services have an Advanced Configuration section, where you can pass extra arguments to the connector
    and, if needed, change the connection Scheme.

    This would only be required to handle advanced connectivity scenarios or customizations.

    * **Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to database during the connection. These details must be added as Key-Value pairs.
    * **Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent during the connection. These details must be added as Key-Value pairs.

          <img src="https://mintcdn.com/openmetadata/6gXrLELUGeLr2Cj6/public/images/connectors/advanced-configuration.png?fit=max&auto=format&n=6gXrLELUGeLr2Cj6&q=85&s=c8f582832bafdb22aec9a99e387d8b50" alt="Advanced Configuration" width="1398" height="534" data-path="public/images/connectors/advanced-configuration.png" />
  </Step>

  <Step title="Test the Connection">
    Once the credentials have been added, click on *Test Connection* and *Save* the changes.

    <img src="https://mintcdn.com/openmetadata/9G75p72jJKYgvFUQ/public/images/connectors/test-connection.png?fit=max&auto=format&n=9G75p72jJKYgvFUQ&q=85&s=4ac71a56e30fa3dd1be86f82c1f07068" alt="Test Connection" width="1494" height="310" data-path="public/images/connectors/test-connection.png" />
  </Step>

  <Step title="Configure Metadata Ingestion">
    In this step we will configure the metadata ingestion pipeline,
    Please follow the instructions below

    <img src="https://mintcdn.com/openmetadata/9SXjaLbGROaofLQU/public/images/connectors/configure-metadata-ingestion-database-1.png?fit=max&auto=format&n=9SXjaLbGROaofLQU&q=85&s=88d2b5053db43d64f42deb9c7f1482c9" alt="Configure Metadata Ingestion" width="1327" height="1271" data-path="public/images/connectors/configure-metadata-ingestion-database-1.png" />

    <img src="https://mintcdn.com/openmetadata/9SXjaLbGROaofLQU/public/images/connectors/configure-metadata-ingestion-database-2.png?fit=max&auto=format&n=9SXjaLbGROaofLQU&q=85&s=e71e7a62c5d9be44458c87508fed39e1" alt="Configure Metadata Ingestion" width="1327" height="1271" data-path="public/images/connectors/configure-metadata-ingestion-database-2.png" />

    #### Metadata Ingestion Options

    <Tip>
      If the owner's name is openmetadata, you need to enter `openmetadata@domain.com` in the name section of add team/user form, click [here](/connectors/database/dbt/ingest-dbt-owner#following-steps-shows-adding-a-user-to-openmetadata) for more info.
    </Tip>

    * **Name**: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.

    * **Database Filter Pattern (Optional)**: Use to database filter patterns to control whether or not to include database as part of metadata ingestion.
      * **Include**: Explicitly include databases by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all databases with names matching one or more of the supplied regular expressions. All other databases will be excluded.
      * **Exclude**: Explicitly exclude databases by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all databases with names matching one or more of the supplied regular expressions. All other databases will be included.

    * **Schema Filter Pattern (Optional)**: Use to schema filter patterns to control whether to include schemas as part of metadata ingestion.
      * **Include**: Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
      * **Exclude**: Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.

    * **Table Filter Pattern (Optional)**: Use to table filter patterns to control whether to include tables as part of metadata ingestion.
      * **Include**: Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded.
      * **Exclude**: Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included.

    * **Enable Debug Log (toggle)**: Set the Enable Debug Log toggle to set the default log level to debug.

    * **Mark Deleted Tables (toggle)**: Set the Mark Deleted Tables toggle to flag tables as soft-deleted if they are not present anymore in the source system.

    * **Mark Deleted Tables from Filter Only (toggle)**: Set the Mark Deleted Tables from Filter Only toggle to flag tables as soft-deleted if they are not present anymore within the filtered schema or database only. This flag is useful when you have more than one ingestion pipelines. For example if you have a schema

    * **includeTables (toggle)**: Optional configuration to turn off fetching metadata for tables.

    * **includeViews (toggle)**: Set the Include views toggle to control whether to include views as part of metadata ingestion.

    * **includeTags (toggle)**: Set the 'Include Tags' toggle to control whether to include tags as part of metadata ingestion.

    * **includeOwners (toggle)**: Set the 'Include Owners' toggle to control whether to include owners to the ingested entity if the owner email matches with a user stored in the OM server as part of metadata ingestion. If the ingested entity already exists and has an owner, the owner will not be overwritten.

    * **includeStoredProcedures (toggle)**: Optional configuration to toggle the Stored Procedures ingestion.

    * **includeDDL (toggle)**: Optional configuration to toggle the DDL Statements ingestion.

    * **queryLogDuration (Optional)**: Configuration to tune how far we want to look back in query logs to process Stored Procedures results.

    * **queryParsingTimeoutLimit (Optional)**: Configuration to set the timeout for parsing the query in seconds.

    * **useFqnForFiltering (toggle)**: Regex will be applied on fully qualified name (e.g service\_name.db\_name.schema\_name.table\_name) instead of raw name (e.g. table\_name).

    * **Incremental (Beta)**: Use Incremental Metadata Extraction after the first execution. This is done by getting the changed tables instead of all of them. **Only Available for BigQuery, Redshift and Snowflake**
      * **Enabled**: If `True`, enables Metadata Extraction to be Incremental.
      * **lookback Days**: Number of days to search back for a successful pipeline run. The timestamp of the last found successful pipeline run will be used as a base to search for updated entities.
      * **Safety Margin Days**: Number of days to add to the last successful pipeline run timestamp to search for updated entities.

    * **Threads (Beta)**: Use a Multithread approach for Metadata Extraction. You can define here the number of threads you would like to run concurrently.

    Note that the right-hand side panel in the OpenMetadata UI will also share useful documentation when configuring the ingestion.
  </Step>

  <Step title="Schedule the Ingestion and Deploy">
    Scheduling can be set up at an hourly, daily, weekly, or manual cadence. The
    timezone is in UTC. Select a Start Date to schedule for ingestion. It is
    optional to add an End Date.

    Review your configuration settings. If they match what you intended,
    click Deploy to create the service and schedule metadata ingestion.

    If something doesn't look right, click the Back button to return to the
    appropriate step and change the settings as needed.

    After configuring the workflow, you can click on Deploy to create the
    pipeline.

    <img src="https://mintcdn.com/openmetadata/j50Bw6ZBiFbbFFnF/public/images/connectors/schedule.png?fit=max&auto=format&n=j50Bw6ZBiFbbFFnF&q=85&s=24b0c2f55f803efde5fb3b3bc24ed3ae" alt="Schedule the Workflow" width="2733" height="1083" data-path="public/images/connectors/schedule.png" />
  </Step>

  <Step title="View the Ingestion Pipeline">
    Once the workflow has been successfully deployed, you can view the
    Ingestion Pipeline running from the Service Page.

    <img src="https://mintcdn.com/openmetadata/9G75p72jJKYgvFUQ/public/images/connectors/view-ingestion-pipeline.png?fit=max&auto=format&n=9G75p72jJKYgvFUQ&q=85&s=7c4e411977371617cb1312efb9f9bfee" alt="View Ingestion Pipeline" width="2733" height="1271" data-path="public/images/connectors/view-ingestion-pipeline.png" />

    <Tip>
      If AutoPilot is enabled, workflows like usage tracking, data lineage, and similar tasks will be handled automatically. Users don’t need to set up or manage them - AutoPilot takes care of everything in the system.
    </Tip>
  </Step>
</Steps>

## Related

<Columns cols={2}>
  <Card title="Usage Workflow" href="/v1.12.x/connectors/ingestion/workflows/usage">
    Learn more about how to configure the Usage Workflow to ingest Query information from the UI.
  </Card>

  <Card title="Lineage Workflow" href="/v1.12.x/connectors/ingestion/workflows/lineage">
    Learn more about how to configure the Lineage from the UI.
  </Card>

  <Card title="Profiler Workflow" href="/v1.12.x/how-to-guides/data-quality-observability/profiler/profiler-workflow">
    Learn more about how to configure the Data Profiler from the UI.
  </Card>

  <Card title="Data Quality Workflow" href="/v1.12.x/how-to-guides/data-quality-observability/quality/configure">
    Learn more about how to configure the Data Quality tests from the UI.
  </Card>

  <Card title="dbt Integration" href="/v1.12.x/connectors/database/dbt">
    Learn more about how to ingest dbt models' definitions and their lineage.
  </Card>
</Columns>