OpenMetadata/openmetadata-docs/content/v1.6.x-SNAPSHOT/connectors/database/adls-datalake/yaml.md

title	slug
Run the ADLS Datalake Connector Externally	/connectors/database/adls-datalake/yaml

{% connectorDetailsHeader name="ADLS Datalake" stage="PROD" platform="OpenMetadata" availableFeatures=["Metadata", "Data Profiler", "Data Quality"] unavailableFeatures=["Query Usage", "Lineage", "Column-level Lineage", "Owners", "dbt", "Tags", "Stored Procedures"] / %}

In this section, we provide guides and references to use the ADLS Datalake connector.

Configure and schedule ADLS Datalake metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion
• dbt Integration

{% partial file="/v1.6/connectors/external-ingestion-deployment.md" /%}

Requirements

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

ADLS Permissions

To extract metadata from Azure ADLS (Storage Account - StorageV2), you will need an App Registration with the following permissions on the Storage Account:

• Storage Blob Data Contributor
• Storage Queue Data Contributor

Python Requirements

{% partial file="/v1.6/connectors/python-requirements.md" /%}

Azure installation

pip3 install "openmetadata-ingestion[datalake-azure]"

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

This is a sample config for Datalake using Azure:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=9 %}

• Client ID : Client ID of the data storage account
• Client Secret : Client Secret of the account
• Tenant ID : Tenant ID under which the data storage account falls
• Account Name : Account Name of the data Storage

{% /codeInfo %}

{% partial file="/v1.6/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.6/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.6/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

# Datalake with Azure 
source:
  type: datalake
  serviceName: local_datalake
  serviceConnection:
    config:
      type: Datalake
      configSource:    

        securityConfig: 
          clientId: client-id
          clientSecret: client-secret
          tenantId: tenant-id
          accountName: account-name
      prefix: prefix

{% partial file="/v1.6/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.6/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.6/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.6/connectors/yaml/ingestion-cli.md" /%}

dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage here.