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title	slug
Run Datalake Connector using the CLI	/connectors/database/datalake/cli

Run Datalake using the metadata CLI

Stage	Metadata	Query Usage	Data Profiler	Data Quality	Lineage	DBT	Supported Versions
PROD	✅	❌	✅	✅	❌	❌	--

Lineage	Table-level	Column-level
❌	❌	❌

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

Requirements

To deploy OpenMetadata, check the Deployment guides.

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.

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>/*"
            ]
        }
    ]
}

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-gcs]"

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:


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
  sourceConfig:
    type: DatabaseMetadata
    config:
      tableFilterPattern:
        includes:
        - ''
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

The sourceConfig is defined here.

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 tableFilternPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,

Source Configuration - Service Connection using GCS

This is a sample config for Datalake using GCS:

source:
  type: datalake
  serviceName: local_datalake
  serviceConnection:
    config:
      type: Datalake
      configSource:
        securityConfig:
          gcsConfig:
            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
  sourceConfig:
    config:
      tableFilterPattern:
        includes:
          - ''
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

The sourceConfig is defined here.

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 gcs bucket

Source Configuration - Service Connection using Azure

This is a sample config for Datalake using Azure:

# 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
  sourceConfig:
    config:
      tableFilterPattern:
        includes:
        - ''
sink:
  type: metadata-rest
  config: {}
workflowConfig:
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

The sourceConfig is defined here.

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

schemaFilterPattern and tableFilternPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,

Source Configuration - Source Config

The sourceConfig is defined here:

markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
includeTables: true or false, to ingest table data. Default is true.
includeViews: true or false, to ingest views definitions.
databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the they support regex as include or exclude. E.g.,

tableFilterPattern:
  includes:
    - users
    - type_test

Sink Configuration

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

Workflow Configuration

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

We support different security providers. You can find their definitions here. You can find the different implementation of the ingestion below.

Openmetadata JWT Auth

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: openmetadata
    securityConfig:
      jwtToken: '{bot_jwt_token}'

Auth0 SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Azure SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: azure
    securityConfig:
      clientSecret: '{your_client_secret}'
      authority: '{your_authority_url}'
      clientId: '{your_client_id}'
      scopes:
        - your_scopes

Custom OIDC SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

Google SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: google
    securityConfig:
      secretKey: '{path-to-json-creds}'

Okta SSO

workflowConfig:
  openMetadataServerConfig:
    hostPort: http://localhost:8585/api
    authProvider: okta
    securityConfig:
      clientId: "{CLIENT_ID - SPA APP}"
      orgURL: "{ISSUER_URL}/v1/token"
      privateKey: "{public/private keypair}"
      email: "{email}"
      scopes:
        - token

Amazon Cognito SSO

The ingestion can be configured by Enabling JWT Tokens

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: auth0
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

OneLogin SSO

Which uses Custom OIDC for the ingestion

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

KeyCloak SSO

Which uses Custom OIDC for the ingestion

workflowConfig:
  openMetadataServerConfig:
    hostPort: 'http://localhost:8585/api'
    authProvider: custom-oidc
    securityConfig:
      clientId: '{your_client_id}'
      secretKey: '{your_client_secret}'
      domain: '{your_domain}'

2. Run with the CLI

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

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.

12 KiB Raw Blame History

Run Datalake using the metadata CLI

Requirements

Python Requirements

S3 installation

GCS installation

Azure installation

If version <0.13

Metadata Ingestion

1. Define the YAML Config

Source Configuration - Source Config using AWS S3

Source Configuration - Service Connection using GCS

Source Configuration - Service Connection using Azure

Source Configuration - Source Config

Sink Configuration

Workflow Configuration

Openmetadata JWT Auth

Auth0 SSO

Azure SSO

Custom OIDC SSO

Google SSO

Okta SSO

Amazon Cognito SSO

OneLogin SSO

KeyCloak SSO

2. Run with the CLI

dbt Integration

12 KiB

Raw Blame History