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746 lines
20 KiB
Markdown
746 lines
20 KiB
Markdown
![]() |
---
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title: Run SQLite Connector using the CLI
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slug: /connectors/database/sqlite/cli
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---
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# Run SQLite using the metadata CLI
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{% multiTablesWrapper %}
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| Feature | Status |
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| :----------------- | :--------------------------- |
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| Stage | PROD |
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| Metadata | {% icon iconName="check" /%} |
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| Query Usage | {% icon iconName="check" /%} |
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| Data Profiler | {% icon iconName="check" /%} |
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| Data Quality | {% icon iconName="cross" /%} |
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| Lineage | {% icon iconName="check" /%} |
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| DBT | {% icon iconName="check" /%} |
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| Supported Versions | -- |
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| Feature | Status |
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| :----------- | :--------------------------- |
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| Lineage | Partially via Views |
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| Table-level | {% icon iconName="check" /%} |
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| Column-level | {% icon iconName="check" /%} |
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{% /multiTablesWrapper %}
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In this section, we provide guides and references to use the SQLite connector.
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Configure and schedule SQLite 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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- [Query Usage](#query-usage)
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- [Data Profiler](#data-profiler)
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- [Lineage](#lineage)
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- [dbt Integration](#dbt-integration)
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## Requirements
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{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%}
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To deploy OpenMetadata, check the Deployment 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 ingest basic metadata sqlite user must have the following priviledges:
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- `SELECT` Privilege on `sqlite_temp_master`
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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/database/sqliteConnection.json)
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you can find the structure to create a connection to SQLite.
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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 SQLite:
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{% codePreview %}
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{% codeInfoContainer %}
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#### Source Configuration - Service Connection
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{% codeInfo srNumber=1 %}
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**username**: Username to connect to SQLite. Blank for in-memory database.
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{% /codeInfo %}
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{% codeInfo srNumber=2 %}
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**password**: Password to connect to SQLite. Blank for in-memory database.
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{% /codeInfo %}
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{% codeInfo srNumber=3 %}
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**hostPort**: Enter the hostname and port number for your SQLite deployment in the Host and Port field.
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{% /codeInfo %}
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{% codeInfo srNumber=4 %}
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**database**: The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases.
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{% /codeInfo %}
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{% codeInfo srNumber=5 %}
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**databaseMode**: How to run the SQLite database. :memory: by default.
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{% /codeInfo %}
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#### Source Configuration - Source Config
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{% codeInfo srNumber=6 %}
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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/databaseServiceMetadataPipeline.json):
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**markDeletedTables**: To flag tables as soft-deleted if they are not present anymore in the source system.
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**includeTables**: true or false, to ingest table data. Default is true.
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**includeViews**: true or false, to ingest views definitions.
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**databaseFilterPattern**, **schemaFilterPattern**, **tableFilternPattern**: Note that the filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/database)
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{% /codeInfo %}
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#### Sink Configuration
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{% codeInfo srNumber=7 %}
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To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
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{% /codeInfo %}
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#### Workflow Configuration
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{% codeInfo srNumber=8 %}
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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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{% /codeInfo %}
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#### Advanced Configuration
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{% codeInfo srNumber=9 %}
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**Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
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{% /codeInfo %}
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{% codeInfo srNumber=10 %}
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**Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to Athena during the connection. These details must be added as Key-Value pairs.
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- In case you are using Single-Sign-On (SSO) for authentication, add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "sso_login_url"`
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- In case you authenticate with SSO using an external browser popup, then add the `authenticator` details in the Connection Arguments as a Key-Value pair as follows: `"authenticator" : "externalbrowser"`
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{% /codeInfo %}
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{% /codeInfoContainer %}
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{% codeBlock fileName="filename.yaml" %}
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```yaml
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source:
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type: sqlite
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serviceName: <service name>
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serviceConnection:
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config:
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type: SQLite
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```
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```yaml {% srNumber=1 %}
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username: <username>
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```
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```yaml {% srNumber=2 %}
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password: <password>
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```
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```yaml {% srNumber=3 %}
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hostPort: <warehouse>
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```
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```yaml {% srNumber=4 %}
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database: <database>
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```
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```yaml {% srNumber=5 %}
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databaseMode: <database-mode>
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```
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```yaml {% srNumber=9 %}
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# connectionOptions:
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# key: value
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```
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```yaml {% srNumber=10 %}
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# connectionArguments:
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# key: value
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```
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```yaml {% srNumber=6 %}
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sourceConfig:
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config:
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type: DatabaseMetadata
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markDeletedTables: true
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includeTables: true
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includeViews: true
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# includeTags: true
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# databaseFilterPattern:
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# includes:
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# - database1
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# - database2
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# excludes:
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# - database3
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# - database4
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# schemaFilterPattern:
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# includes:
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# - schema1
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# - schema2
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# excludes:
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# - schema3
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# - schema4
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# tableFilterPattern:
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# includes:
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# - users
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# - type_test
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# excludes:
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# - table3
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# - table4
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```
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```yaml {% srNumber=7 %}
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sink:
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type: metadata-rest
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config: {}
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```
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```yaml {% srNumber=8 %}
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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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{% /codeBlock %}
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{% /codePreview %}
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### Workflow Configs for Security Provider
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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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## Openmetadata JWT Auth
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- JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details [here](/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: openmetadata
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securityConfig:
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jwtToken: "{bot_jwt_token}"
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```
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- You can refer to the JWT Troubleshooting section [link](/deployment/security/jwt-troubleshooting) for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc [link](/deployment/security/workflow-config-auth).
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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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## Query Usage
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The Query Usage workflow will be using the `query-parser` processor.
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After running a Metadata Ingestion workflow, we can run Query Usage workflow.
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While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server.
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### 1. Define the YAML Config
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This is a sample config for SQLite Usage:
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{% codePreview %}
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{% codeInfoContainer %}
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{% codeInfo srNumber=15 %}
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#### Source Configuration - Source Config
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You can find all the definitions and types for the `sourceConfig` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceQueryUsagePipeline.json).
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**queryLogDuration**: Configuration to tune how far we want to look back in query logs to process usage data.
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{% /codeInfo %}
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{% codeInfo srNumber=16 %}
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**stageFileLocation**: Temporary file name to store the query logs before processing. Absolute file path required.
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{% /codeInfo %}
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{% codeInfo srNumber=17 %}
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**resultLimit**: Configuration to set the limit for query logs
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{% /codeInfo %}
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{% codeInfo srNumber=18 %}
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**queryLogFilePath**: Configuration to set the file path for query logs
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{% /codeInfo %}
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{% codeInfo srNumber=19 %}
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#### Processor, Stage and Bulk Sink Configuration
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To specify where the staging files will be located.
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Note that the location is a directory that will be cleaned at the end of the ingestion.
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{% /codeInfo %}
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{% codeInfo srNumber=20 %}
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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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{% /codeInfo %}
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{% /codeInfoContainer %}
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{% codeBlock fileName="filename.yaml" %}
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```yaml
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source:
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type: sqlite-usage
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serviceName: <service name>
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sourceConfig:
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config:
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type: DatabaseUsage
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```
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```yaml {% srNumber=15 %}
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# Number of days to look back
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queryLogDuration: 7
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```
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```yaml {% srNumber=16 %}
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# This is a directory that will be DELETED after the usage runs
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stageFileLocation: <path to store the stage file>
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```
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```yaml {% srNumber=17 %}
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# resultLimit: 1000
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```
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```yaml {% srNumber=18 %}
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# If instead of getting the query logs from the database we want to pass a file with the queries
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# queryLogFilePath: path-to-file
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```
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```yaml {% srNumber=19 %}
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processor:
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type: query-parser
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config: {}
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stage:
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type: table-usage
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config:
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filename: /tmp/sqlite_usage
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bulkSink:
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type: metadata-usage
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config:
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filename: /tmp/sqlite_usage
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```
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```yaml {% srNumber=20 %}
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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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{% /codeBlock %}
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{% /codePreview %}
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### 2. Run with the CLI
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There is an extra requirement to run the Usage pipelines. You will need to install:
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After saving the YAML config, we will run the command the same way we did for the metadata ingestion:
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```bash
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metadata ingest -c <path-to-yaml>
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```
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## Data Profiler
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The Data Profiler workflow will be using the `orm-profiler` processor.
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|
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After running a Metadata Ingestion workflow, we can run Data Profiler workflow.
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While the `serviceName` will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the `serviceConnection` details from the server.
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|
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### 1. Define the YAML Config
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This is a sample config for the profiler:
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{% codePreview %}
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{% codeInfoContainer %}
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{% codeInfo srNumber=21 %}
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#### Source Configuration - Source Config
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You can find all the definitions and types for the `sourceConfig` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json).
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**generateSampleData**: Option to turn on/off generating sample data.
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{% /codeInfo %}
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{% codeInfo srNumber=22 %}
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**profileSample**: Percentage of data or no. of rows we want to execute the profiler and tests on.
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{% /codeInfo %}
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{% codeInfo srNumber=23 %}
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**threadCount**: Number of threads to use during metric computations.
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{% /codeInfo %}
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{% codeInfo srNumber=24 %}
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**processPiiSensitive**: Optional configuration to automatically tag columns that might contain sensitive information.
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{% /codeInfo %}
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{% codeInfo srNumber=25 %}
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**confidence**: Set the Confidence value for which you want the column to be marked
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{% /codeInfo %}
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{% codeInfo srNumber=26 %}
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**timeoutSeconds**: Profiler Timeout in Seconds
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{% /codeInfo %}
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{% codeInfo srNumber=27 %}
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**databaseFilterPattern**: Regex to only fetch databases that matches the pattern.
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{% /codeInfo %}
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{% codeInfo srNumber=28 %}
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**schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
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{% /codeInfo %}
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{% codeInfo srNumber=29 %}
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**tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
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{% /codeInfo %}
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||
|
{% codeInfo srNumber=30 %}
|
||
|
|
||
|
#### Processor Configuration
|
||
|
|
||
|
Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI:
|
||
|
|
||
|
**tableConfig**: `tableConfig` allows you to set up some configuration at the table level.
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
|
||
|
{% codeInfo srNumber=31 %}
|
||
|
|
||
|
#### Sink Configuration
|
||
|
|
||
|
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
|
||
|
{% codeInfo srNumber=32 %}
|
||
|
|
||
|
#### 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:
|
||
|
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
{% /codeInfoContainer %}
|
||
|
|
||
|
{% codeBlock fileName="filename.yaml" %}
|
||
|
|
||
|
|
||
|
```yaml
|
||
|
source:
|
||
|
type: sqlite
|
||
|
serviceName: local_sqlite
|
||
|
sourceConfig:
|
||
|
config:
|
||
|
type: Profiler
|
||
|
```
|
||
|
|
||
|
```yaml {% srNumber=21 %}
|
||
|
generateSampleData: true
|
||
|
```
|
||
|
```yaml {% srNumber=22 %}
|
||
|
# profileSample: 85
|
||
|
```
|
||
|
```yaml {% srNumber=23 %}
|
||
|
# threadCount: 5
|
||
|
```
|
||
|
```yaml {% srNumber=24 %}
|
||
|
processPiiSensitive: false
|
||
|
```
|
||
|
```yaml {% srNumber=25 %}
|
||
|
# confidence: 80
|
||
|
```
|
||
|
```yaml {% srNumber=26 %}
|
||
|
# timeoutSeconds: 43200
|
||
|
```
|
||
|
```yaml {% srNumber=27 %}
|
||
|
# databaseFilterPattern:
|
||
|
# includes:
|
||
|
# - database1
|
||
|
# - database2
|
||
|
# excludes:
|
||
|
# - database3
|
||
|
# - database4
|
||
|
```
|
||
|
```yaml {% srNumber=28 %}
|
||
|
# schemaFilterPattern:
|
||
|
# includes:
|
||
|
# - schema1
|
||
|
# - schema2
|
||
|
# excludes:
|
||
|
# - schema3
|
||
|
# - schema4
|
||
|
```
|
||
|
```yaml {% srNumber=29 %}
|
||
|
# tableFilterPattern:
|
||
|
# includes:
|
||
|
# - table1
|
||
|
# - table2
|
||
|
# excludes:
|
||
|
# - table3
|
||
|
# - table4
|
||
|
```
|
||
|
|
||
|
```yaml {% srNumber=30 %}
|
||
|
processor:
|
||
|
type: orm-profiler
|
||
|
config: {} # Remove braces if adding properties
|
||
|
# tableConfig:
|
||
|
# - fullyQualifiedName: <table fqn>
|
||
|
# profileSample: <number between 0 and 99> # default
|
||
|
|
||
|
# profileSample: <number between 0 and 99> # default will be 100 if omitted
|
||
|
# profileQuery: <query to use for sampling data for the profiler>
|
||
|
# columnConfig:
|
||
|
# excludeColumns:
|
||
|
# - <column name>
|
||
|
# includeColumns:
|
||
|
# - columnName: <column name>
|
||
|
# - metrics:
|
||
|
# - MEAN
|
||
|
# - MEDIAN
|
||
|
# - ...
|
||
|
# partitionConfig:
|
||
|
# enablePartitioning: <set to true to use partitioning>
|
||
|
# partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
|
||
|
# partitionInterval: <partition interval>
|
||
|
# partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>
|
||
|
|
||
|
```
|
||
|
|
||
|
```yaml {% srNumber=31 %}
|
||
|
sink:
|
||
|
type: metadata-rest
|
||
|
config: {}
|
||
|
```
|
||
|
|
||
|
```yaml {% srNumber=32 %}
|
||
|
workflowConfig:
|
||
|
# loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR
|
||
|
openMetadataServerConfig:
|
||
|
hostPort: <OpenMetadata host and port>
|
||
|
authProvider: <OpenMetadata auth provider>
|
||
|
```
|
||
|
|
||
|
{% /codeBlock %}
|
||
|
|
||
|
{% /codePreview %}
|
||
|
|
||
|
- You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from [here](/connectors/ingestion/workflows/profiler)
|
||
|
|
||
|
|
||
|
|
||
|
### 2. Prepare the Profiler DAG
|
||
|
|
||
|
Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:
|
||
|
|
||
|
|
||
|
|
||
|
|
||
|
{% codePreview %}
|
||
|
|
||
|
{% codeInfoContainer %}
|
||
|
|
||
|
{% codeInfo srNumber=33 %}
|
||
|
|
||
|
#### Import necessary modules
|
||
|
|
||
|
The `ProfilerWorkflow` class that is being imported is a part of a metadata orm_profiler framework, which defines a process of extracting Profiler data.
|
||
|
|
||
|
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
|
||
|
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
{% codeInfo srNumber=34 %}
|
||
|
|
||
|
**Default arguments for all tasks in the Airflow DAG.**
|
||
|
- Default arguments dictionary contains default arguments for tasks in the DAG, including the owner's name, email address, number of retries, retry delay, and execution timeout.
|
||
|
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
|
||
|
{% codeInfo srNumber=35 %}
|
||
|
|
||
|
- **config**: Specifies config for the profiler as we prepare above.
|
||
|
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
{% codeInfo srNumber=36 %}
|
||
|
|
||
|
- **metadata_ingestion_workflow()**: This code defines a function `metadata_ingestion_workflow()` that loads a YAML configuration, creates a `ProfilerWorkflow` object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
|
||
|
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
{% codeInfo srNumber=37 %}
|
||
|
|
||
|
- **DAG**: creates a DAG using the Airflow framework, and tune the DAG configurations to whatever fits with your requirements
|
||
|
- For more Airflow DAGs creation details visit [here](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/dags.html#declaring-a-dag).
|
||
|
|
||
|
{% /codeInfo %}
|
||
|
|
||
|
{% /codeInfoContainer %}
|
||
|
|
||
|
{% codeBlock fileName="filename.py" %}
|
||
|
|
||
|
```python {% srNumber=33 %}
|
||
|
import yaml
|
||
|
from datetime import timedelta
|
||
|
from airflow import DAG
|
||
|
from metadata.orm_profiler.api.workflow import ProfilerWorkflow
|
||
|
|
||
|
try:
|
||
|
from airflow.operators.python import PythonOperator
|
||
|
except ModuleNotFoundError:
|
||
|
from airflow.operators.python_operator import PythonOperator
|
||
|
|
||
|
from airflow.utils.dates import days_ago
|
||
|
|
||
|
|
||
|
```
|
||
|
```python {% srNumber=34 %}
|
||
|
default_args = {
|
||
|
"owner": "user_name",
|
||
|
"email_on_failure": False,
|
||
|
"retries": 3,
|
||
|
"retry_delay": timedelta(seconds=10),
|
||
|
"execution_timeout": timedelta(minutes=60),
|
||
|
}
|
||
|
|
||
|
|
||
|
```
|
||
|
|
||
|
```python {% srNumber=35 %}
|
||
|
config = """
|
||
|
<your YAML configuration>
|
||
|
"""
|
||
|
|
||
|
|
||
|
```
|
||
|
|
||
|
```python {% srNumber=36 %}
|
||
|
def metadata_ingestion_workflow():
|
||
|
workflow_config = yaml.safe_load(config)
|
||
|
workflow = ProfilerWorkflow.create(workflow_config)
|
||
|
workflow.execute()
|
||
|
workflow.raise_from_status()
|
||
|
workflow.print_status()
|
||
|
workflow.stop()
|
||
|
|
||
|
|
||
|
```
|
||
|
|
||
|
```python {% srNumber=37 %}
|
||
|
with DAG(
|
||
|
"profiler_example",
|
||
|
default_args=default_args,
|
||
|
description="An example DAG which runs a OpenMetadata ingestion workflow",
|
||
|
start_date=days_ago(1),
|
||
|
is_paused_upon_creation=False,
|
||
|
catchup=False,
|
||
|
) as dag:
|
||
|
ingest_task = PythonOperator(
|
||
|
task_id="profile_and_test_using_recipe",
|
||
|
python_callable=metadata_ingestion_workflow,
|
||
|
)
|
||
|
|
||
|
|
||
|
```
|
||
|
|
||
|
{% /codeBlock %}
|
||
|
|
||
|
{% /codePreview %}
|
||
|
|
||
|
## Lineage
|
||
|
|
||
|
You can learn more about how to ingest lineage [here](/connectors/ingestion/workflows/lineage).
|
||
|
|
||
|
## dbt Integration
|
||
|
|
||
|
You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).
|