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title | slug |
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Run Quicksight Connector using the CLI | /connectors/dashboard/quicksight/cli |
Run Quicksight using the metadata CLI
In this section, we provide guides and references to use the Quicksight connector.
Configure and schedule Quicksight 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.
Python Requirements
To run the Quicksight ingestion, you will need to install:
pip3 install "openmetadata-ingestion[quicksight]"
Metadata Ingestion
All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Quicksight.
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 Quicksightled around the following JSON Schema
1. Define the YAML Config
This is a sample config for Quicksight:
source:
type: Quicksight
serviceName: local_Quicksight
serviceConnection:
config:
type: QuickSight
awsConfig:
awsAccessKeyId: key
awsSecretAccessKey: secret
awsRegion: ap-south-1
awsSessionToken: token
awsAccountId: account-id
identityType: identityType
sourceConfig:
config:
type: DashboardMetadata
# dbServiceNames:
# - service1
# - service2
# dashboardFilterPattern:
# includes:
# - dashboard1
# - dashboard2
# excludes:
# - dashboard3
# - dashboard4
# chartFilterPattern:
# includes:
# - chart1
# - chart2
# excludes:
# - chart3
# - chart4
sink:
type: metadata-rest
config: {}
workflowConfig:
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
openMetadataServerConfig:
hostPort: <OpenMetadata host and port>
authProvider: <OpenMetadata auth provider>
Source Configuration - Service Connection
-
awsConfig
- AWS Access Key ID: Enter your secure access key ID for your Glue connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.
- AWS Secret Access Key: Enter the Secret Access Key (the passcode key pair to the key ID from above).
- AWS Region: Enter the location of the amazon cluster that your data and account are associated with.
- AWS Session Token (optional): The AWS session token is an optional parameter. If you want, enter the details of your temporary session token.
- Endpoint URL (optional): Your Glue connector will automatically determine the AWS Glue endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL.
-
identityType: The authentication method that the user uses to sign in.
-
awsAccountId: AWS Account ID
-
namespace: The Amazon QuickSight namespace that contains the dashboard IDs in this request ( To be provided when identityType is
ANONYMOUS
)
Source Configuration - Source Config
The sourceConfig
is defined here:
dbServiceNames
: Database Service Name for the creation of lineage, if the source supports it.dashboardFilterPattern
andchartFilterPattern
: Note that thedashboardFilterPattern
andchartFilterPattern
both support regex as include or exclude. E.g.,
dashboardFilterPattern:
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.