> ## 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.

# Run the GCS Datalake Connector Externally

> Configure YAML ingestion for GCS Datalake to extract metadata from structured and unstructured file-based storage.

export const CodePanel = ({children, fileName = 'config.yaml', showLineNumbers = false}) => {
  const codePanelRef = useRef(null);
  const codeContentRef = useRef(null);
  const isProgrammaticScroll = useRef(false);
  const hoverTimeout = useRef(null);
  useEffect(() => {
    let tries = 0;
    const wrapLines = () => {
      const root = codeContentRef.current;
      if (!root) return;
      const pres = Array.from(root.querySelectorAll('pre'));
      if (!pres.length) {
        if (tries++ < 20) requestAnimationFrame(wrapLines);
        return;
      }
      let globalLine = 1;
      pres.forEach(pre => {
        const code = pre.querySelector('code') || pre;
        if (!code || code.dataset.wrapped === 'true') return;
        const raw = code.textContent || '';
        let lines = raw.split('\n');
        while (lines[0] === '') lines.shift();
        while (lines[lines.length - 1] === '') lines.pop();
        code.innerHTML = lines.map(line => {
          const ln = globalLine++;
          const num = showLineNumbers ? `<span class="line-number">${ln}</span>` : '';
          const safe = line.replace(/</g, '&lt;').replace(/>/g, '&gt;') || ' ';
          return `<span class="code-line" data-line="${ln}">${num}${safe}</span>`;
        }).join('');
        code.dataset.wrapped = 'true';
      });
    };
    wrapLines();
  }, [children, showLineNumbers]);
  useEffect(() => {
    const panel = codePanelRef.current;
    const content = codeContentRef.current;
    if (!panel || !content) return;
    const waitForLines = () => {
      const codeLines = content.querySelectorAll('.code-line');
      if (!codeLines.length) {
        requestAnimationFrame(waitForLines);
        return;
      }
      setupHighlighting(codeLines);
    };
    const setupHighlighting = codeLines => {
      const layout = panel.closest('.split-layout');
      const sections = layout.querySelectorAll('.content-section');
      const parseLines = str => {
        if (!str) return [];
        const out = [];
        str.split(',').forEach(p => {
          if (p.includes('-')) {
            const [s, e] = p.split('-').map(Number);
            for (let i = s; i <= e; i++) out.push(i);
          } else {
            const n = Number(p);
            if (!isNaN(n)) out.push(n);
          }
        });
        return out;
      };
      const clearHighlight = () => {
        codeLines.forEach(l => l.classList.remove('highlighted'));
      };
      const highlight = lines => {
        clearHighlight();
        lines.forEach(n => {
          const el = content.querySelector(`.code-line[data-line="${n}"]`);
          if (el) el.classList.add('highlighted');
        });
      };
      const scrollToLines = lines => {
        if (!lines.length) return;
        const first = lines[0];
        const targetLine = lines.length > 1 ? first : lines[0];
        const el = content.querySelector(`.code-line[data-line="${targetLine}"]`);
        if (!el) return;
        isProgrammaticScroll.current = true;
        const containerRect = content.getBoundingClientRect();
        const elRect = el.getBoundingClientRect();
        const offset = elRect.top - containerRect.top + content.scrollTop;
        const TOP_PADDING = 16;
        content.scrollTo({
          top: Math.max(offset - TOP_PADDING, 0),
          behavior: 'smooth'
        });
        setTimeout(() => {
          isProgrammaticScroll.current = false;
        }, 200);
      };
      const activate = (section, scroll) => {
        if (section.classList.contains('active')) return;
        sections.forEach(s => s.classList.remove('active'));
        section.classList.add('active');
        const lines = parseLines(section.dataset.lines);
        highlight(lines);
        if (scroll) scrollToLines(lines);
      };
      const observer = new IntersectionObserver(entries => {
        if (isProgrammaticScroll.current) return;
        entries.forEach(e => {
          if (e.isIntersecting) activate(e.target, false);
        });
      }, {
        threshold: 0.3,
        rootMargin: '-80px 0px -40% 0px'
      });
      sections.forEach(section => {
        observer.observe(section);
        section.addEventListener('click', () => activate(section, true));
        section.addEventListener('mouseenter', () => {
          clearTimeout(hoverTimeout.current);
          hoverTimeout.current = setTimeout(() => activate(section, true), 80);
        });
      });
      if (sections[0]) activate(sections[0], false);
    };
    waitForLines();
  }, []);
  const handleCopy = e => {
    const btn = e.currentTarget;
    const codeLines = codeContentRef.current?.querySelectorAll('.code-line');
    if (!codeLines || codeLines.length === 0) return;
    const text = Array.from(codeLines).map(line => {
      const clone = line.cloneNode(true);
      const lineNumber = clone.querySelector('.line-number');
      if (lineNumber) lineNumber.remove();
      return clone.textContent;
    }).join('\n');
    if (!text) return;
    navigator.clipboard.writeText(text).then(() => {
      btn.dataset.copied = 'true';
      setTimeout(() => btn.dataset.copied = 'false', 1500);
    });
  };
  return <div className="code-panel" ref={codePanelRef}>
      <div className="code-header">
        {fileName}
        <button className="copy-btn" aria-label="Copy full code" data-copied="false" onClick={handleCopy}>
          <svg className="icon-copy" viewBox="0 0 15 16" fill="currentColor">
            <path d="M10.113 3.124H2.205C1.463 3.124.86 3.655.86 4.31v10.005c0 .654.603 1.186 1.345 1.186h7.908c.742 0 1.345-.532 1.345-1.186V4.31c0-.655-.606-1.186-1.345-1.186Z" />
            <path d="M13.138.5H5.229c-.742 0-1.344.531-1.344 1.186 0 .23.209.414.47.414s.47-.184.47-.414c0-.197.182-.357.404-.357h7.909c.223 0 .404.16.404.357V11.69c0 .196-.181.356-.404.356-.262 0-.47.184-.47.415 0 .23.208.415.47.415.742 0 1.344-.532 1.344-1.186V1.686C14.482 1.03 13.88.5 13.138.5Z" />
          </svg>

          <svg className="icon-check" viewBox="0 0 20 20" fill="currentColor">
            <path fillRule="evenodd" d="M16.707 5.293a1 1 0 010 1.414l-7.25 7.25a1 1 0 01-1.414 0l-3.25-3.25a1 1 0 011.414-1.414l2.543 2.543 6.543-6.543a1 1 0 011.414 0z" clipRule="evenodd" />
          </svg>
        </button>
      </div>

      <div className="code-content" ref={codeContentRef}>
        {children}
      </div>
    </div>;
};

export const ContentSection = ({id, title, lines, children}) => <div className="content-section" data-content-id={id} data-lines={lines}>
    {title && <h4>{title}</h4>}
    {children}
  </div>;

export const ContentPanel = ({children}) => <div className="content-panel">{children}</div>;

export const CodePreview = ({children}) => {
  const [instanceId] = useState(() => `preview-${Math.random().toString(36).slice(2)}`);
  useEffect(() => {
    const nav = document.querySelector('nav') || document.querySelector('header') || document.querySelector('[class*="nav"]');
    if (nav) {
      document.documentElement.style.setProperty('--navbar-height', `${nav.offsetHeight}px`);
    }
  }, []);
  return <div className="split-layout" data-preview-id={instanceId}>
      {children}
    </div>;
};

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/gcs.webp" name="GCS Datalake" stage="PROD" availableFeatures={["Metadata", "Data Profiler", "Data Quality", "Sample Data", "Auto-Classification"]} unavailableFeatures={["Query Usage", "Lineage", "Column-level Lineage", "Owners", "dbt", "Tags", "Stored Procedures"]} />

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

* [Requirements](#requirements)
* [Metadata Ingestion](#metadata-ingestion)
* [dbt Integration](#dbt-integration)

## How to Run the Connector Externally

To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with
custom Airflow plugins to handle the workflow deployment.

If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check
the following docs to run the Ingestion Framework **anywhere**.

<Columns cols={2}>
  <Card title="External Schedulers" href="/v1.12.x/deployment/ingestion">
    Get more information about running the Ingestion Framework Externally
  </Card>
</Columns>

## Requirements

**Note:** GCS Datalake connector supports extracting metadata from file types `JSON`, `CSV`, `TSV` & `Parquet`.

### Python Requirements

<Tip>
  We have support for Python versions **3.9-3.11**
</Tip>

If running OpenMetadata version greater than 0.13, you will need to install the Datalake ingestion for GCS

#### GCS installation

```bash theme={null}
pip3 install "openmetadata-ingestion[datalake-gcp]"
```

#### If version \<0.13

You will be installing the requirements for GCS

```bash theme={null}
pip3 install "openmetadata-ingestion[datalake]"
```

## Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Datalake.
In order to create and run a Metadata Ingestion workflow, we will follow the steps to create a YAML configuration able to connect to the source, process the Entities if needed, and reach the OpenMetadata server.
The workflow is modeled around the following JSON Schema.

## 1. Define the YAML Config

<CodePreview>
  <ContentPanel>
    <ContentSection id={1} title="Source Configuration" lines="1-3">
      Configure the source type and service name for your GCS Datalake connector.
    </ContentSection>

    <ContentSection id={2} title="GCP Configuration" lines="4-11">
      **gcpConfig:**

      * **type**: Credentials Type is the type of the account, for a service account the value of this field is `service_account`. To fetch this key, look for the value associated with the `type` key in the service account key file.
      * **projectId**: A project ID is a unique string used to differentiate your project from all others in Google Cloud. To fetch this key, look for the value associated with the `project_id` key in the service account key file. You can also pass multiple project id to ingest metadata from different BigQuery projects into one service.
      * **privateKeyId**: This is a unique identifier for the private key associated with the service account. To fetch this key, look for the value associated with the `private_key_id` key in the service account file.
      * **privateKey**: This is the private key associated with the service account that is used to authenticate and authorize access to BigQuery. To fetch this key, look for the value associated with the `private_key` key in the service account file.
      * **clientEmail**: This is the email address associated with the service account. To fetch this key, look for the value associated with the `client_email` key in the service account key file.
      * **clientId**: This is a unique identifier for the service account. To fetch this key, look for the value associated with the `client_id` key in the service account key  file.
      * **authUri**: This is the URI for the authorization server. To fetch this key, look for the value associated with the `auth_uri` key in the service account key file. The default value to Auth URI is [https://accounts.google.com/o/oauth2/auth](https://accounts.google.com/o/oauth2/auth).
      * **tokenUri**: The Google Cloud Token URI is a specific endpoint used to obtain an OAuth 2.0 access token from the Google Cloud IAM service. This token allows you to authenticate and access various Google Cloud resources and APIs that require authorization. To fetch this key, look for the value associated with the `token_uri` key in the service account credentials file. Default Value to Token URI is [https://oauth2.googleapis.com/token](https://oauth2.googleapis.com/token).
      * **authProviderX509CertUrl**: This is the URL of the certificate that verifies the authenticity of the authorization server. To fetch this key, look for the value associated with the `auth_provider_x509_cert_url` key in the service account key file. The Default value for Auth Provider X509Cert URL is [https://www.googleapis.com/oauth2/v1/certs](https://www.googleapis.com/oauth2/v1/certs)
      * **clientX509CertUrl**: This is the URL of the certificate that verifies the authenticity of the service account. To fetch this key, look for the value associated with the `client_x509_cert_url` key in the service account key  file.
    </ContentSection>

    <ContentSection id={3} title="Bucket Configuration" lines="10">
      **bucketName**: name of the bucket in GCS

      **Prefix**: prefix in gcp bucket
    </ContentSection>

    <ContentSection id={4} title="Source Config" lines="12-55">
      #### Source Configuration - Source Config

      The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json):

      <div>
        **markDeletedTables**: To flag tables as soft-deleted if they are not present anymore in the source system.
      </div>

      <div>
        **markDeletedStoredProcedures**: Optional configuration to soft delete stored procedures in OpenMetadata if the source stored procedures are deleted. Also, if the stored procedures is deleted, all the associated entities like lineage, etc., with that stored procedures will be deleted.

        **markDeletedSchemas**: Optional configuration to soft delete schemas stored in OpenMetadata if the source schema is deleted. Setting this flag to true will only keep filtered schema and delete any other schemas that do not match schemaFilterPattern or do not exist at source.

        **markDeletedDatabases**: Additional optional configuration for soft deletion, providing granular option to select which particular entities should be deleted.

        **includeTables**: true or false, to ingest table data. Default is true.
      </div>

      <div>
        **includeViews**: true or false, to ingest views definitions.
      </div>

      <div>
        **includeTags**: Optional configuration to toggle the tags ingestion.
      </div>

      <div>
        **includeOwners**: 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.
      </div>

      <div>
        **includeStoredProcedures**: Optional configuration to toggle the Stored Procedures ingestion.
      </div>

      <div>
        **includeDDL**: Optional configuration to toggle the DDL Statements ingestion.
      </div>

      <div>
        **overrideMetadata** *(boolean)*: Set the 'Override Metadata' toggle to control whether to override the existing metadata in the OpenMetadata server with the metadata fetched from the source. If the toggle is set to true, the metadata fetched from the source will override the existing metadata in the OpenMetadata server. If the toggle is set to false, the metadata fetched from the source will not override the existing metadata in the OpenMetadata server. This is applicable for fields like description, tags, owner and displayName.
      </div>

      <div>
        **queryLogDuration**: Configuration to tune how far we want to look back in query logs to process Stored Procedures results.
      </div>

      <div>
        **queryParsingTimeoutLimit**: Configuration to set the timeout for parsing the query in seconds.
      </div>

      <div>
        **useFqnForFiltering**: 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).
      </div>

      <div>
        **databaseFilterPattern**, **schemaFilterPattern**: Note that the filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/database)
      </div>

      <div>
        **tableFilterPattern**: Note that the filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/table)
      </div>

      <div>
        **threads (beta)**: The number of threads to use when extracting the metadata using multithreading.
      </div>

      <div>
        **databaseMetadataConfigType** *(string)*: Database Source Config Metadata Pipeline type.
      </div>

      <div>
        **incremental (beta)**: Incremental Extraction configuration. Currently implemented for:

        * [BigQuery](/connectors/ingestion/workflows/metadata/incremental-extraction/bigquery)
        * [Redshift](/connectors/ingestion/workflows/metadata/incremental-extraction/redshift)
        * [Snowflake](/connectors/ingestion/workflows/metadata/incremental-extraction/snowflake)
      </div>
    </ContentSection>

    <ContentSection id={5} title="Sink Configuration" lines="56-58">
      To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
    </ContentSection>

    <ContentSection id={6} title="Workflow Configuration" lines="59-75">
      <div>
        The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
      </div>

      <div>
        **Logger Level**

        You can specify the `loggerLevel` depending on your needs. If you are trying to troubleshoot an ingestion, running with `DEBUG` will give you far more traces for identifying issues.
      </div>

      <div>
        **JWT Token**

        JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/deployment/security/enable-jwt-tokens).

        You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration.
      </div>

      <div>
        **Store Service Connection**

        If set to `true` (default), we will store the sensitive information either encrypted via the Fernet Key in the database or externally, if you have configured any [Secrets Manager](/deployment/secrets-manager).

        If set to `false`, the service will be created, but the service connection information will only be used by the Ingestion Framework at runtime, and won't be sent to the OpenMetadata server.
      </div>

      <div>
        **SSL Configuration**

        If you have added SSL to the [OpenMetadata server](/deployment/security/enable-ssl), then you will need to handle the certificates when running the ingestion too. You can either set `verifySSL` to `ignore`, or have it as `validate`, which will require you to set the `sslConfig.caCertificate` with a local path where your ingestion runs that points to the server certificate file.

        Find more information on how to troubleshoot SSL issues [here](/deployment/security/enable-ssl/ssl-troubleshooting).
      </div>

      <div>
        **ingestionPipelineFQN**

        Fully qualified name of ingestion pipeline, used to identify the current ingestion pipeline.
      </div>
    </ContentSection>
  </ContentPanel>

  <CodePanel fileName="gcs_datalake_config.yaml">
    ```yaml theme={null}
    source:
      type: datalake
      serviceName: local_datalake
      serviceConnection:
        config:
          type: Datalake
          configSource:
            securityConfig:
              gcpConfig:
          bucketName: bucket name  # REQUIRED
          prefix: prefix
    ```

    ```yaml theme={null}
              type: service_account
              projectId: project-id # ["project-id-1", "project-id-2"]
              privateKeyId: abc123
              privateKey: |
                -----BEGIN PRIVATE KEY-----
                Super secret key
                -----END PRIVATE KEY-----
              clientEmail: role@project.iam.gserviceaccount.com
              clientId: "1234"
              # authUri: https://accounts.google.com/o/oauth2/auth (default)
              # tokenUri: https://oauth2.googleapis.com/token (default)
              # authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default)
              clientX509CertUrl: https://www.googleapis.com/robot/v1/metadata/x509/role%40project.iam.gserviceaccount.com
    ```

    ```yaml theme={null}
      sourceConfig:
        config:
          type: DatabaseMetadata
          markDeletedTables: true
          markDeletedStoredProcedures: true
          markDeletedSchemas: true
          markDeletedDatabases: true
          includeTables: true
          includeViews: true
          # includeTags: true
          # includeOwners: false
          # includeStoredProcedures: true
          # includeDDL: true
          # overrideMetadata: false
          # queryLogDuration: 1
          # queryParsingTimeoutLimit: 300
          # useFqnForFiltering: false
          # threads: 1
          # databaseMetadataConfigType: ()
          # incremental:
          #   enabled: true
          #   lookbackDays: 7
          #   safetyMarginDays: 1
          # databaseFilterPattern:
          #   includes:
          #     - database1
          #     - database2
          #   excludes:
          #     - database3
          #     - database4
          # schemaFilterPattern:
          #   includes:
          #     - schema1
          #     - schema2
          #   excludes:
          #     - schema3
          #     - schema4
          # tableFilterPattern:
          #   includes:
          #     - users
          #     - type_test
          #   excludes:
          #     - table3
          #     - table4
    ```

    ```yaml theme={null}
    sink:
      type: metadata-rest
      config: {}
    ```

    ```yaml theme={null}
    workflowConfig:
      loggerLevel: INFO  # DEBUG, INFO, WARNING or ERROR
      openMetadataServerConfig:
        hostPort: "http://localhost:8585/api"
        authProvider: openmetadata
        securityConfig:
          jwtToken: "{bot_jwt_token}"
        ## Store the service Connection information
        storeServiceConnection: true  # false
        ## Secrets Manager Configuration
        # secretsManagerProvider: aws, azure or noop
        # secretsManagerLoader: airflow or env
        ## If SSL, fill the following
        # verifySSL: validate  # or ignore
        # sslConfig:
        #   caCertificate: /local/path/to/certificate
    # ingestionPipelineFQN: <service name>.<ingestion name> ## e.g., "my_redshift.metadata"
    ```
  </CodePanel>
</CodePreview>

### 2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:

```bash theme={null}
metadata ingest -c <path-to-yaml>
```

Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration,
you will be able to extract metadata from different sources.

## dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage [here](/v1.12.x/connectors/database/dbt).