
* Added mark delete logic * Final test and optimization * After merge fixes * Added include tags for dash pipelines dbt * added docs and fixed test * Fixed py tests * Added UI changes for following newly added fields: - markDeletedDashboards - markDeletedMlModels - markDeletedPipelines - markDeletedTopics - includeTags * Fixed failing unit tests * updated json files of localization for other languages * Improved localization changes * added localization changes for other languages * Updated mark deleted desc * updated the ingestion fields descriptions in the ingestion form for UI * automated localization changes for other languages * updated descriptions for includeTags field for dbtPipeline and databaseServiceMetadataPipeline json * fixed issue where includeTags field was being sent in the dbtConfigSource * Added flow to input taxonomy while adding BigQuery service. --------- Co-authored-by: Aniket Katkar <aniketkatkar97@gmail.com>
7.7 KiB
title | slug |
---|---|
Run Domo Pipeline Connector using the CLI | /connectors/pipeline/domo-pipeline/cli |
Run Domo Pipeline using the Metadata CLI
In this section, we provide guides and references to use the Domo Pipeline connector.
Configure and schedule Domo Pipeline metadata and profiler workflows from the OpenMetadata UI:
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.
For metadata ingestion, kindly make sure add alteast data
scopes to the clientId provided.
Question related to scopes, click here.
Python Requirements
To run the Domo Pipeline ingestion, you will need to install:
pip3 install "openmetadata-ingestion[domo]"
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Airbyte.
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 Domo-Pipeline:
source:
type: domopipeline
serviceName: domopipeline_source
serviceConnection:
config:
type: DomoPipeline
clientId: clientid
secretToken: secret-token
accessToken: access-token
apiHost: api.domo.com
sandboxDomain: https://<api_domo>.domo.com
sourceConfig:
config:
type: PipelineMetadata
# markDeletedPipelines: True
# includeTags: True
# pipelineFilterPattern:
# includes:
# - pipeline1
# - pipeline2
# excludes:
# - pipeline3
# - pipeline4
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
securityconfig:
jwtToken:
Source Configuration - Service Connection
- Client ID: Client ID to Connect to DOMO Pipeline.
- Secret Token: Secret Token to Connect DOMO Pipeline.
- Access Token: Access to Connect to DOMO Pipeline.
- API Host: API Host to Connect to DOMO Pipeline instance.
- SandBox Domain: Connect to SandBox Domain.
Source Configuration - Source Config
The sourceConfig
is defined here:
dbServiceNames
: Database Service Name for the creation of lineage, if the source supports it.pipelineFilterPattern
andchartFilterPattern
: Note that thepipelineFilterPattern
andchartFilterPattern
both support regex as include or exclude. E.g.,includeTags
: Set the Include tags toggle to control whether or not to include tags as part of metadata ingestion.markDeletedPipelines
: Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.
pipelineFilterPattern:
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.