OpenMetadata/openmetadata-docs/content/v1.3.x-SNAPSHOT/connectors/database/datalake/yaml.md

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

Run the Datalake Connector Externally

{% multiTablesWrapper %}

Feature	Status
Stage	PROD
Metadata	{% icon iconName="check" /%}
Query Usage	{% icon iconName="cross" /%}
Data Profiler	{% icon iconName="check" /%}
Data Quality	{% icon iconName="check" /%}
Stored Procedures	{% icon iconName="cross" /%}
DBT	{% icon iconName="check" /%}
Supported Versions	--

Feature	Status
Lineage	{% icon iconName="cross" /%}
Table-level	{% icon iconName="cross" /%}
Column-level	{% icon iconName="cross" /%}

{% /multiTablesWrapper %}

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

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

• Requirements
• Metadata Ingestion
• dbt Integration

{% partial file="/v1.3/connectors/external-ingestion-deployment.md" /%}

Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%} To deploy OpenMetadata, check the Deployment guides. {%/inlineCallout%}

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

S3 Permissions

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

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

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

If running OpenMetadata version greater than 0.13, you will need to install the Datalake ingestion for GCS or S3:

S3 installation

pip3 install "openmetadata-ingestion[datalake-s3]"

GCS installation

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

Azure installation

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

If version <0.13

You will be installing the requirements together for S3 and GCS

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

Source Configuration - Source Config using AWS S3

This is a sample config for Datalake using AWS S3:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

• awsAccessKeyId: Enter your secure access key ID for your DynamoDB connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
• awsSecretAccessKey: Enter the Secret Access Key (the passcode key pair to the key ID from above).
• awsRegion: Specify the region in which your DynamoDB is located. This setting is required even if you have configured a local AWS profile.
• schemaFilterPattern and tableFilterPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,

{% /codeInfo %}

{% partial file="/v1.3/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.3/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.3/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

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

source:
  type: datalake
  serviceName: local_datalake
  serviceConnection:
    config:
      type: Datalake

      configSource:      
        securityConfig: 
          awsAccessKeyId: aws access key id
          awsSecretAccessKey: aws secret access key
          awsRegion: aws region
      bucketName: bucket name
      prefix: prefix

{% partial file="/v1.3/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.3/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.3/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

This is a sample config for Datalake using GCS:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% codeInfo srNumber=5 %}

• type: Credentials type, e.g. service_account.
• projectId
• privateKey
• privateKeyId
• clientEmail
• clientId
• authUri: https://accounts.google.com/o/oauth2/auth by default
• tokenUri: https://oauth2.googleapis.com/token by default
• authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs by default
• clientX509CertUrl
• bucketName: name of the bucket in GCS
• Prefix: prefix in gcp bucket

{% /codeInfo %}

{% partial file="/v1.3/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.3/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.3/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

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

source:
  type: datalake
  serviceName: local_datalake
  serviceConnection:
    config:
      type: Datalake
      configSource:
        securityConfig:

          gcpConfig:
            type: type of account
            projectId: project id
            privateKeyId: private key id
            privateKey: private key
            clientEmail: client email
            clientId: client id
            authUri: https://accounts.google.com/o/oauth2/auth
            tokenUri: https://oauth2.googleapis.com/token
            authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs
            clientX509CertUrl:  clientX509 Certificate Url
      bucketName: bucket name
      prefix: prefix

{% partial file="/v1.3/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.3/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.3/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

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="/v1.3/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.3/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.3/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="/v1.3/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.3/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.3/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.3/connectors/yaml/ingestion-cli.md" /%}

dbt Integration

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