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307 lines
10 KiB
Markdown
307 lines
10 KiB
Markdown
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---
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title: Run DeltaLake Connector using the CLI
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slug: /connectors/database/deltalake/cli
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---
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# Run Deltalake 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="cross" /%} |
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| Data Profiler | {% icon iconName="cross" /%} |
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| Data Quality | {% icon iconName="cross" /%} |
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| Lineage | Partially via Views |
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| DBT | {% icon iconName="cross" /%} |
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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 Deltalake connector.
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Configure and schedule Deltalake 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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- [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 run the Deltalake ingestion, you will need to install:
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```bash
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pip3 install "openmetadata-ingestion[deltalake]"
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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/database/deltaLakeConnection.json)
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you can find the structure to create a connection to Deltalake.
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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 Deltalake:
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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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**Metastore Host Port**: Enter the Host & Port of Hive Metastore Service to configure the Spark Session. Either
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of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
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**Metastore File Path**: Enter the file path to local Metastore in case Spark cluster is running locally. Either
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of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
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**Metastore DB**: The JDBC connection to the underlying Hive metastore DB. Either
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of `metastoreHostPort`, `metastoreDb` or `metastoreFilePath` is required.
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**appName (Optional)**: Enter the app name of spark session.
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**Connection Arguments (Optional)**: Key-Value pairs that will be used to pass extra `config` elements to the Spark
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Session builder.
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We are internally running with `pyspark` 3.X and `delta-lake` 2.0.0. This means that we need to consider Spark
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configuration options for 3.X.
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##### Metastore Host Port
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When connecting to an External Metastore passing the parameter `Metastore Host Port`, we will be preparing a Spark Session with the configuration
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```
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.config("hive.metastore.uris", "thrift://{connection.metastoreHostPort}")
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```
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Then, we will be using the `catalog` functions from the Spark Session to pick up the metadata exposed by the Hive Metastore.
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##### Metastore File Path
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If instead we use a local file path that contains the metastore information (e.g., for local testing with the default `metastore_db` directory), we will set
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```
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.config("spark.driver.extraJavaOptions", "-Dderby.system.home={connection.metastoreFilePath}")
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```
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To update the `Derby` information. More information about this in a great [SO thread](https://stackoverflow.com/questions/38377188/how-to-get-rid-of-derby-log-metastore-db-from-spark-shell).
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- You can find all supported configurations [here](https://spark.apache.org/docs/latest/configuration.html)
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- If you need further information regarding the Hive metastore, you can find
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it [here](https://spark.apache.org/docs/3.0.0-preview/sql-data-sources-hive-tables.html), and in The Internals of
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Spark SQL [book](https://jaceklaskowski.gitbooks.io/mastering-spark-sql/content/spark-sql-hive-metastore.html).
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{% /codeInfo %}
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#### Source Configuration - Source Config
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{% codeInfo srNumber=4 %}
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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 they support regex as include or exclude. E.g.,
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{% /codeInfo %}
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#### Sink Configuration
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{% codeInfo srNumber=5 %}
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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=6 %}
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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=2 %}
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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=3 %}
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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: deltalake
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serviceName: "<service name>"
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serviceConnection:
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config:
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type: DeltaLake
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```
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```yaml {% srNumber=1 %}
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metastoreConnection:
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# Pick only of the three
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metastoreHostPort: "<metastore host port>"
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# metastoreDb: jdbc:mysql://localhost:3306/demo_hive
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# metastoreFilePath: "<path_to_metastore>/metastore_db"
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appName: MyApp
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```
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```yaml {% srNumber=2 %}
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# connectionOptions:
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# key: value
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```
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```yaml {% srNumber=3 %}
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# connectionArguments:
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# key: value
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```
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```yaml {% srNumber=4 %}
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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=5 %}
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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=6 %}
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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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## dbt Integration
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{% tilesContainer %}
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{% tile
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icon="mediation"
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title="dbt Integration"
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description="Learn more about how to ingest dbt models' definitions and their lineage."
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link="/connectors/ingestion/workflows/dbt" /%}
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{% /tilesContainer %}
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## Related
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{% tilesContainer %}
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{% tile
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title="Ingest with Airflow"
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description="Configure the ingestion using Airflow SDK"
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link="/connectors/database/deltalake/airflow"
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/ %}
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{% /tilesContainer %}
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