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* 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>
264 lines
7.2 KiB
Markdown
264 lines
7.2 KiB
Markdown
---
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title: Run Fivetran Connector using the CLI
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slug: /connectors/pipeline/fivetran/cli
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---
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# Run Fivetran using the metadata CLI
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In this section, we provide guides and references to use the Fivetran connector.
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Configure and schedule Fivetran metadata and profiler workflows from the OpenMetadata UI:
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- [Requirements](#requirements)
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- [Metadata Ingestion](#metadata-ingestion)
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## Requirements
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<InlineCallout color="violet-70" icon="description" bold="OpenMetadata 0.12 or later" href="/deployment">
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To deploy OpenMetadata, check the <a href="/deployment">Deployment</a> guides.
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</InlineCallout>
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To run the Ingestion via the UI you'll need to use the OpenMetadata Ingestion Container, which comes shipped with
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custom Airflow plugins to handle the workflow deployment.
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### Python Requirements
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To run the Fivetran ingestion, you will need to install:
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```bash
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pip3 install "openmetadata-ingestion[fivetran]"
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```
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## Metadata Ingestion
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All connectors are defined as JSON Schemas.
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[Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/pipeline/fivetranConnection.json)
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you can find the structure to create a connection to Fivetran.
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In order to create and run a Metadata Ingestion workflow, we will follow
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the steps to create a YAML configuration able to connect to the source,
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process the Entities if needed, and reach the OpenMetadata server.
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The workflow is modeled around the following
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[JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json)
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### 1. Define the YAML Config
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This is a sample config for Fivetran:
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```yaml
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source:
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type: fivetran
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serviceName: local_fivetran
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serviceConnection:
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config:
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type: Fivetran
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apiKey: <fivetran api key>
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apiSecret: <fivetran api secret>
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# hostPort: https://api.fivetran.com (default)
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# limit: 1000 (default)
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sourceConfig:
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config:
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type: PipelineMetadata
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# markDeletedPipelines: True
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# includeTags: True
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# includeLineage: true
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# pipelineFilterPattern:
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# includes:
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# - pipeline1
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# - pipeline2
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# excludes:
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# - pipeline3
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# - pipeline4
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sink:
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type: metadata-rest
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config: {}
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workflowConfig:
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# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
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openMetadataServerConfig:
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hostPort: <OpenMetadata host and port>
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authProvider: <OpenMetadata auth provider>
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```
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#### Source Configuration - Service Connection
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- **apiKey**: Fivetran API Key.
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- **apiSecret**: Fivetran API Secret.
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#### Source Configuration - Source Config
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The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/pipelineServiceMetadataPipeline.json):
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- `dbServiceNames`: Database Service Name for the creation of lineage, if the source supports it.
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- `pipelineFilterPattern` and `chartFilterPattern`: Note that the `pipelineFilterPattern` and `chartFilterPattern` both support regex as include or exclude. E.g.,
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- `includeTags`: Set the Include tags toggle to control whether or not to include tags as part of metadata ingestion.
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- `markDeletedPipelines`: Set the Mark Deleted Pipelines toggle to flag pipelines as soft-deleted if they are not present anymore in the source system.
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```yaml
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pipelineFilterPattern:
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includes:
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- users
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- type_test
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```
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#### Sink Configuration
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To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
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#### Workflow Configuration
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The main property here is the `openMetadataServerConfig`, where you can define the host and security provider of your OpenMetadata installation.
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For a simple, local installation using our docker containers, this looks like:
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: openmetadata
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securityConfig:
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jwtToken: '{bot_jwt_token}'
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```
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We support different security providers. You can find their definitions [here](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client).
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You can find the different implementation of the ingestion below.
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<Collapse title="Configure SSO in the Ingestion Workflows">
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### Openmetadata JWT Auth
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: openmetadata
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securityConfig:
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jwtToken: '{bot_jwt_token}'
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```
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### Auth0 SSO
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: auth0
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securityConfig:
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clientId: '{your_client_id}'
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secretKey: '{your_client_secret}'
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domain: '{your_domain}'
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```
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### Azure SSO
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: azure
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securityConfig:
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clientSecret: '{your_client_secret}'
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authority: '{your_authority_url}'
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clientId: '{your_client_id}'
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scopes:
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- your_scopes
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```
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### Custom OIDC SSO
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: custom-oidc
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securityConfig:
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clientId: '{your_client_id}'
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secretKey: '{your_client_secret}'
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domain: '{your_domain}'
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```
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### Google SSO
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: google
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securityConfig:
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secretKey: '{path-to-json-creds}'
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```
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### Okta SSO
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: http://localhost:8585/api
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authProvider: okta
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securityConfig:
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clientId: "{CLIENT_ID - SPA APP}"
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orgURL: "{ISSUER_URL}/v1/token"
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privateKey: "{public/private keypair}"
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email: "{email}"
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scopes:
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- token
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```
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### Amazon Cognito SSO
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The ingestion can be configured by [Enabling JWT Tokens](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens)
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: auth0
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securityConfig:
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clientId: '{your_client_id}'
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secretKey: '{your_client_secret}'
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domain: '{your_domain}'
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```
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### OneLogin SSO
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Which uses Custom OIDC for the ingestion
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: custom-oidc
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securityConfig:
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clientId: '{your_client_id}'
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secretKey: '{your_client_secret}'
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domain: '{your_domain}'
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```
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### KeyCloak SSO
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Which uses Custom OIDC for the ingestion
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```yaml
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workflowConfig:
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openMetadataServerConfig:
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hostPort: 'http://localhost:8585/api'
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authProvider: custom-oidc
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securityConfig:
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clientId: '{your_client_id}'
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secretKey: '{your_client_secret}'
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domain: '{your_domain}'
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```
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</Collapse>
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### 2. Run with the CLI
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First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
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```bash
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metadata ingest -c <path-to-yaml>
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```
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Note that from connector to connector, this recipe will always be the same. By updating the YAML configuration,
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you will be able to extract metadata from different sources.
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