> ## 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 IOMETE Connector Externally

> Use YAML to ingest metadata from IOMETE, including tables, schemas, and profiling.

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" />
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          </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/iomete.webp" name="IOMETE" stage="BETA" availableFeatures={["Metadata", "Data Profiler", "Data Quality", "dbt", "View Lineage", "View Column-level Lineage", "Sample Data", "Auto-Classification"]} unavailableFeatures={["Query Usage", "Owners", "Tags", "Stored Procedures"]} />

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

* [Requirements](#requirements)
* [Metadata Ingestion](#metadata-ingestion)
* [Data Profiler](#data-profiler)
* [Data Quality](#data-quality)
* [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="/v2.0.x-SNAPSHOT/deployment/ingestion">
    Get more information about running the Ingestion Framework Externally
  </Card>
</Columns>

## Requirements

The IOMETE user must have access to the lakehouse cluster and sufficient privileges to read metadata.

### Python Requirements

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

To run the IOMETE ingestion, you will need to install:

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

## Metadata Ingestion

All connectors are defined as JSON Schemas.
[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/iometeConnection.json)
you can find the structure to create a connection to IOMETE.
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](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)

### 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 IOMETE connector.
    </ContentSection>

    <ContentSection id={2} title="Host and Port" lines="7">
      **hostPort**: Host of the IOMETE service. You can optionally include the port using the `host:port` format (e.g. `dev.iomete.cloud:443`). If no port is specified, port `443` is used by default.
    </ContentSection>

    <ContentSection id={3} title="Username" lines="8">
      **username**: Username to connect to IOMETE. This user should have privileges to read all the metadata in IOMETE.
    </ContentSection>

    <ContentSection id={4} title="Password" lines="9">
      **password**: Password to connect to IOMETE.
    </ContentSection>

    <ContentSection id={5} title="Cluster" lines="10">
      **cluster**: IOMETE lakehouse cluster name to connect to. This is passed as the `cluster` query parameter in the connection URL.
    </ContentSection>

    <ContentSection id={6} title="Data Plane" lines="11">
      **dataPlane**: IOMETE data plane name. This is passed as the `data_plane` query parameter in the connection URL (e.g. `default`).
    </ContentSection>

    <ContentSection id={7} title="Catalog and Database Schema" lines="12-13">
      **catalog** (Optional): Catalog of the data source (e.g. `spark_catalog`). If left blank, OpenMetadata uses the default catalog.
      **databaseSchema** (Optional): IOMETE database (schema) to restrict metadata ingestion to. If left blank, OpenMetadata attempts to scan all schemas in the catalog.
    </ContentSection>

    <ContentSection id={8} title="Source Config" lines="14-57">
      The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json):

      * **markDeletedTables**: To flag tables as soft-deleted if they are not present anymore in the source system.
      * **markDeletedStoredProcedures**: Optional configuration to soft delete stored procedures in OpenMetadata if the source stored procedures are deleted. Also, if the stored procedure is deleted, all the associated entities like lineage, etc., with that stored procedure 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 schemas and delete any other schemas that do not match schemaFilterPattern or do not exist at source.
      * **markDeletedDatabases**: Additional optional configuration for soft deletion, providing a granular option to select which particular entities should be deleted.
      * **includeTables**: Set to `true` or `false` to ingest table data. Default is `true`.
      * **includeViews**: Set to `true` or `false` to ingest view definitions.
      * **includeTags**: Optional configuration to toggle the tags ingestion.
      * **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.
      * **includeStoredProcedures**: Optional configuration to toggle the Stored Procedures ingestion.
      * **includeDDL**: Optional configuration to toggle the DDL Statements ingestion.
      * **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.
      * **queryLogDuration**: Configuration to tune how far we want to look back in query logs to process Stored Procedures results.
      * **queryParsingTimeoutLimit**: Configuration to set the timeout for parsing the query in seconds.
      * **useFqnForFiltering**: Regex will be applied on the fully qualified name (for example, `service_name.db_name.schema_name.table_name`) instead of the raw name (for example, `table_name`).
      * **databaseFilterPattern**, **schemaFilterPattern**: Note that the filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/database).
      * **tableFilterPattern**: Note that the filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/table).
      * **threads (beta)**: The number of threads to use when extracting the metadata using multithreading.
      * **databaseMetadataConfigType** *(string)*: Database Source Config Metadata Pipeline type.
      * **incremental (beta)**: Incremental Extraction configuration. Currently implemented for [BigQuery](/connectors/ingestion/workflows/metadata/incremental-extraction/bigquery), [Redshift](/connectors/ingestion/workflows/metadata/incremental-extraction/redshift), and [Snowflake](/connectors/ingestion/workflows/metadata/incremental-extraction/snowflake).
    </ContentSection>

    <ContentSection id={9} title="Sink Configuration" lines="58-60">
      To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
    </ContentSection>

    <ContentSection id={10} title="Workflow Configuration" lines="61-77">
      The main property here is `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.

      * **loggerLevel**: Specify the logger level depending on your needs. If you are troubleshooting an ingestion, use `DEBUG` for more detailed traces.
      * **JWT token**: JWT tokens allow clients to authenticate against the OpenMetadata server. See [Enable JWT Tokens](/deployment/security/enable-jwt-tokens) and [JWT Troubleshooting](/deployment/security/jwt-troubleshooting) for more information.
      * **storeServiceConnection**: If set to `true` (default), sensitive information is stored encrypted with the Fernet Key or externally if you have configured a [Secrets Manager](/deployment/secrets-manager). If set to `false`, the service is created, but the service connection information is only used by the Ingestion Framework at runtime and is not sent to the OpenMetadata server.
      * **SSL configuration**: If you have added SSL to the [OpenMetadata server](/deployment/security/enable-ssl), configure the certificates for ingestion. Set `verifySSL` to `ignore`, or set it to `validate` and provide `sslConfig.caCertificate` with a local path to the server certificate. See [SSL Troubleshooting](/deployment/security/enable-ssl/ssl-troubleshooting) for more information.
      * **ingestionPipelineFQN**: Fully qualified name of the ingestion pipeline, used to identify the current ingestion pipeline.
    </ContentSection>
  </ContentPanel>

  <CodePanel fileName="iomete_config.yaml">
    ```yaml theme={null}
    source:
      type: iomete
      serviceName: <service name>
      serviceConnection:
        config:
          type: Iomete
          hostPort: <hostPort>  # REQUIRED - format: host:port
          username: <username>  # REQUIRED
          password: <password>  # REQUIRED
          cluster: <cluster>  # REQUIRED
          dataPlane: <dataPlane>  # REQUIRED
          # catalog: spark_catalog
          # databaseSchema: schema
    ```

    ```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.

## Data Profiler

The Data Profiler workflow will be using the `orm-profiler` processor.

After running a Metadata Ingestion workflow, we can run the Data Profiler workflow.
While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server.

### 1. Define the YAML Config

This is a sample config for the profiler:

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

    <ContentSection id={2} title="Profiler Config Type" lines="4-6">
      **type**: Set to `Profiler` for data profiling ingestion.
    </ContentSection>

    <ContentSection id={3} title="Profile Sample Config" lines="7-10">
      **profileSampleConfig**: How much data the profiler and tests run on.

      * **sampleConfigType**: `DYNAMIC` (default) resolves the sample size at runtime from the table's row count. `STATIC` uses a fixed size you set yourself.
      * **config**: the settings for the chosen type. With `DYNAMIC`, `smartSampling: true` applies the built-in tiers. Set it to `false` and provide `thresholds` to define your own. With `STATIC`, set `profileSample`, and optionally `profileSampleType` and `samplingMethodType`.
    </ContentSection>

    <ContentSection id={4} title="Thread Count" lines="11">
      **threadCount**: Number of threads to use during metric computations.
    </ContentSection>

    <ContentSection id={5} title="Timeout Seconds" lines="12">
      **timeoutSeconds**: Profiler Timeout in Seconds.
    </ContentSection>

    <ContentSection id={6} title="Database Filter Pattern" lines="13-18">
      **databaseFilterPattern**: Regex to only fetch databases that matches the pattern.
    </ContentSection>

    <ContentSection id={7} title="Schema Filter Pattern" lines="19-24">
      **schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
    </ContentSection>

    <ContentSection id={8} title="Table Filter Pattern" lines="25-30">
      **tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
    </ContentSection>

    <ContentSection id={9} title="Processor Configuration" lines="31-64">
      Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI.

      **tableConfig**: `tableConfig` allows you to set up some configuration at the table level including:

      * Profile sample settings per table
      * Custom profile queries
      * Column-level configuration (include/exclude columns, specific metrics)
      * Partition configuration for large tables
    </ContentSection>

    <ContentSection id={10} title="Sink Configuration" lines="65-67">
      To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
    </ContentSection>
  </ContentPanel>

  <CodePanel fileName="{connector}_profiler.yaml">
    ```yaml theme={null}
    source:
      type: iomete
      serviceName: iomete
      sourceConfig:
        config:
          type: Profiler
          # profileSampleConfig:
          #   sampleConfigType: DYNAMIC  # DYNAMIC (default) or STATIC
          #   config:
          #     smartSampling: true
          # threadCount: 5
          # timeoutSeconds: 43200
          # databaseFilterPattern:
          #   includes:
          #     - database1
          #     - database2
          #   excludes:
          #     - database3
          # schemaFilterPattern:
          #   includes:
          #     - schema1
          #     - schema2
          #   excludes:
          #     - schema3
          # tableFilterPattern:
          #   includes:
          #     - table1
          #     - table2
          #   excludes:
          #     - table3
    processor:
      type: orm-profiler
      config: {}  # Remove braces if adding properties
        # tableConfig:
        #   - fullyQualifiedName: <table fqn>
        #     profileSampleConfig:
        #       sampleConfigType: STATIC  # omit entirely to inherit dynamic sampling
        #       config:
        #         profileSample: <number between 0 and 99>
        #     profileQuery: <query to use for sampling data for the profiler>
        #     columnConfig:
        #       excludeColumns:
        #         - <column name>
        #       includeColumns:
        #         - columnName: <column name>
        #         - metrics:
        #           - MEAN
        #           - MEDIAN
        #           - ...
        #     partitionConfig:
        #       enablePartitioning: <set to true to use partitioning>
        #       partitionColumnName: <partition column name>
        #       partitionIntervalType: <TIME-UNIT, INTEGER-RANGE, INGESTION-TIME, COLUMN-VALUE>
        #       Pick one of the variation shown below
        #       ----'TIME-UNIT' or 'INGESTION-TIME'-------
        #       partitionInterval: <partition interval>
        #       partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>
        #       ------------'INTEGER-RANGE'---------------
        #       partitionIntegerRangeStart: <integer>
        #       partitionIntegerRangeEnd: <integer>
        #       -----------'COLUMN-VALUE'----------------
        #       partitionValues:
        #         - <value>
        #         - <value>
    sink:
      type: metadata-rest
      config: {}
    ```
  </CodePanel>
</CodePreview>

* You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from [here](/how-to-guides/data-quality-observability/profiler/profiler-workflow)

### 2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

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

Note now instead of running `ingest`, we are using the `profile` command to select the Profiler workflow.

<Columns cols={2}>
  <Card title="Data Profiler" href="/v2.0.x-SNAPSHOT/how-to-guides/data-quality-observability/profiler/profiler-workflow">
    Find more information about the Data Profiler here
  </Card>
</Columns>

## Auto Classification

The Auto Classification workflow will be using the `orm-profiler` processor.

After running a Metadata Ingestion workflow, we can run the Auto Classification workflow.
While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server.

### 1. Define the YAML Config

This is a sample config for the Auto Classification Workflow:

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

    <ContentSection id={2} title="Auto Classification Config Type" lines="4-6">
      **type**: Set to `AutoClassification` for automatic PII tagging.
    </ContentSection>

    <ContentSection id={3} title="Store Sample Data" lines="7">
      **storeSampleData**: Option to turn on/off storing sample data. If enabled, we will ingest sample data for each table.
    </ContentSection>

    <ContentSection id={4} title="Enable Auto Classification" lines="8">
      **enableAutoClassification**: Optional configuration to automatically tag columns that might contain sensitive information.
    </ContentSection>

    <ContentSection id={5} title="Confidence" lines="9">
      **confidence**: Set the Confidence value for which you want the column to be tagged as PII. Confidence value ranges from 0 to 100. A higher number will yield less false positives but more false negatives. A lower number will yield more false positives but less false negatives.
    </ContentSection>

    <ContentSection id={6} title="Database Filter Pattern" lines="10-15">
      **databaseFilterPattern**: Regex to only fetch databases that matches the pattern.
    </ContentSection>

    <ContentSection id={7} title="Schema Filter Pattern" lines="16-21">
      **schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
    </ContentSection>

    <ContentSection id={8} title="Table Filter Pattern" lines="22-27">
      **tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
    </ContentSection>

    <ContentSection id={9} title="Processor Configuration" lines="28-30">
      Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI.

      **tableConfig**: `tableConfig` allows you to set up some configuration at the table level.
    </ContentSection>

    <ContentSection id={10} title="Sink Configuration" lines="31-33">
      To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
    </ContentSection>
  </ContentPanel>

  <CodePanel fileName="{connector}_auto_classification.yaml">
    ```yaml theme={null}
    source:
      type: iomete
      serviceName: iomete
      sourceConfig:
        config:
          type: AutoClassification
          # storeSampleData: true
          # enableAutoClassification: true
          # confidence: 80
          # databaseFilterPattern:
          #   includes:
          #     - database1
          #     - database2
          #   excludes:
          #     - database3
          # schemaFilterPattern:
          #   includes:
          #     - schema1
          #     - schema2
          #   excludes:
          #     - schema3
          # tableFilterPattern:
          #   includes:
          #     - table1
          #     - table2
          #   excludes:
          #     - table3
    processor:
      type: orm-profiler
      config: {}
    sink:
      type: metadata-rest
      config: {}
    ```
  </CodePanel>
</CodePreview>

### 2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion:

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

<Tip>
  Now instead of running `ingest`, we are using the `classify` command to select the Auto Classification workflow.
</Tip>

## Data Quality

### Adding Data Quality Test Cases from yaml config

When creating a JSON config for a test workflow the source configuration is very simple.

```yaml theme={null}
source:
  type: TestSuite
  serviceName: <your_service_name>
  sourceConfig:
    config:
      type: TestSuite
      entityFullyQualifiedName: <entityFqn>
```

The only sections you need to modify here are the `serviceName` (this name needs to be unique) and `entityFullyQualifiedName` (the entity for which we'll be executing tests against) keys.

Once you have defined your source configuration you'll need to define te processor configuration.

```yaml theme={null}
processor:
  type: "orm-test-runner"
  config:
    forceUpdate: <false|true>
    testCases:
      - name: <testCaseName>
        testDefinitionName: columnValueLengthsToBeBetween
        columnName: <columnName>
        parameterValues:
          - name: minLength
            value: 10
          - name: maxLength
            value: 25
      - name: <testCaseName>
        testDefinitionName: tableRowCountToEqual
        parameterValues:
          - name: value
            value: 10
```

The processor type should be set to ` "orm-test-runner"`. For accepted test definition names and parameter value names refer to the [tests page](/how-to-guides/data-quality-observability/quality/tests-yaml).

<Tip>
  Note that while you can define tests directly in this YAML configuration, running the
  workflow will execute ALL THE TESTS present in the table, regardless of what you are defining in the YAML.

  This makes it easy for any user to contribute tests via the UI, while maintaining the test execution external.
</Tip>

You can keep your YAML config as simple as follows if the table already has tests.

```yaml theme={null}
processor:
  type: "orm-test-runner"
  config: {}
```

### Key reference:

* `forceUpdate`: if the test case exists (base on the test case name) for the entity, implements the strategy to follow when running the test (i.e. whether or not to update parameters)
* `testCases`: list of test cases to add to the entity referenced. Note that we will execute all the tests present in the Table.
* `name`: test case name
* `testDefinitionName`: test definition
* `columnName`: only applies to column test. The name of the column to run the test against
* `parameterValues`: parameter values of the test

The `sink` and `workflowConfig` will have the same settings as the ingestion and profiler workflow.

### Full  `yaml` config example

```yaml theme={null}
source:
  type: TestSuite
  serviceName: MyAwesomeTestSuite
  sourceConfig:
    config:
      type: TestSuite
      entityFullyQualifiedName: MySQL.default.openmetadata_db.tag_usage
#     testCases: ["run_only_this_test_case"] # Optional, if not provided all tests will be executed

processor:
  type: "orm-test-runner"
  config:
    forceUpdate: false
    testCases:
      - name: column_value_length_tagFQN
        testDefinitionName: columnValueLengthsToBeBetween
        columnName: tagFQN
        parameterValues:
          - name: minLength
            value: 10
          - name: maxLength
            value: 25
      - name: table_row_count_test
        testDefinitionName: tableRowCountToEqual
        parameterValues:
          - name: value
            value: 10

sink:
  type: metadata-rest
  config: {}
workflowConfig:
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>
```

### How to Run Tests

To run the tests from the CLI execute the following command

```
metadata test -c /path/to/my/config.yaml
```

## Lineage

You can learn more about how to ingest lineage [here](/v2.0.x-SNAPSHOT/connectors/ingestion/workflows/lineage).

## dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage [here](/v2.0.x-SNAPSHOT/connectors/database/dbt).