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80 lines
2.7 KiB
Markdown
80 lines
2.7 KiB
Markdown
---
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title: Python SDK for dbt
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slug: /sdk/python/ingestion/dbt
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---
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# Python SDK for dbt
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We are going to show the integration of dbt with the Python SDK.
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We will be going through a series of steps on how to configure dbt in OpenMetadata and how the python SDK parses the dbt `manifest.json`, `catalog.json` and `run_results.json` files.
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## Adding dbt configuration in JSON Config
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Below is an example showing the yaml config of the Redshift connector. The below example shows how to fetch the dbt files from AWS s3 bucket.
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For more information on getting the dbt files from other sources like gcs, file server etc. please take a look [here](/sdk/python/ingestion/dbt#locate-the-dbt-files).
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```yaml
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source:
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type: redshift
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serviceName: aws_redshift
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serviceConnection:
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config:
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type: Redshift
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hostPort: cluster.name.region.redshift.amazonaws.com:5439
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username: username
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password: strong_password
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database: dev
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sourceConfig:
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config:
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dbtConfigSource:
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dbtSecurityConfig:
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awsAccessKeyId: <AWS Access Key Id>
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awsSecretAccessKey: <AWS Secret Access Key>
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awsRegion: AWS Region
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dbtPrefixConfig:
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dbtBucketName: <Bucket Name>
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dbtObjectPrefix: <Path of the folder in which dbt files are stored>
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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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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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Add the details of the AWS s3 bucket in the above config:
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- `dbtConfigSource`: Details of the AWS s3 resource
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- `dbtPrefixConfig`: Bucket name and path of the dbt files in bucket
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## Locate the dbt Files
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The `get_dbt_details` method takes in the source config provided in the json and detects source type (gcs, s3, local or file server) based on the fields provided in the config.
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```python
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from metadata.utils.dbt_config import get_dbt_details
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dbt_details = get_dbt_details(self.source_config.dbtConfigSource)
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self.dbt_catalog = dbt_details[0] if dbt_details else None
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self.dbt_manifest = dbt_details[1] if dbt_details else None
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self.dbt_run_results = dbt_details[2] if dbt_details else None
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```
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After scanning the buckets or the path provided, the `manifest`,`catalog` and `run_results` files are fetched.
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## Parsing the dbt Files
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The `_parse_data_model` method parses the manifest, catalog and run_results files that are fetched and converts the dbt data into `DataModel`.
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```python
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from metadata.ingestion.source.database.dbt_source import _parse_data_model()
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_parse_data_model()
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```
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## Extracting the dbt data
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The models which are extracted are shown in the Openmetada UI in the `dbt` tab
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{% image
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src="/images/v0.13.3/sdk/python/ingestion/extracting-dbt-data.png"
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alt="Extracting dbt data"
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/%} |