--- title: Run DB2 Connector using the CLI slug: /connectors/database/db2/cli --- # Run DB2 using the metadata CLI In this section, we provide guides and references to use the DB2 connector. Configure and schedule DB2 metadata and profiler workflows from the OpenMetadata UI: - [Requirements](#requirements) - [Metadata Ingestion](#metadata-ingestion) - [Data Profiler](#data-profiler) - [dbt Integration](#dbt-integration) ## 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 DB2 ingestion, you will need to install: ```bash pip3 install "openmetadata-ingestion[db2]" ``` ## Metadata Ingestion All connectors are defined as JSON Schemas. [Here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/db2Connection.json) you can find the structure to create a connection to DB2. 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 modeled around the following [JSON Schema](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/workflow.json) ### 1. Define the YAML Config This is a sample config for DB2: ```yaml source: type: db2 serviceName: local_db2 serviceConnection: config: type: Db2 username: openmetadata_user password: openmetadata_password hostPort: localhost:5432 # databaseSchema: schema sourceConfig: config: markDeletedTables: true includeTables: true includeViews: true # includeTags: true # databaseFilterPattern: # includes: # - database1 # - database2 # excludes: # - database3 # - database4 # schemaFilterPattern: # includes: # - schema1 # - schema2 # excludes: # - schema3 # - schema4 # tableFilterPattern: # includes: # - table1 # - table2 # excludes: # - table3 # - table4 # For dbt, choose one of Cloud, Local, HTTP, S3 or GCS configurations # dbtConfigSource: # # For cloud # dbtCloudAuthToken: token # dbtCloudAccountId: ID # # For Local # dbtCatalogFilePath: path-to-catalog.json # dbtManifestFilePath: path-to-manifest.json # # For HTTP # dbtCatalogHttpPath: http://path-to-catalog.json # dbtManifestHttpPath: http://path-to-manifest.json # # For S3 # dbtSecurityConfig: # These are modeled after all AWS credentials # awsAccessKeyId: KEY # awsSecretAccessKey: SECRET # awsRegion: us-east-2 # dbtPrefixConfig: # dbtBucketName: bucket # dbtObjectPrefix: "dbt/" # # For GCS # dbtSecurityConfig: # These are modeled after all GCS credentials # type: My Type # projectId: project ID # privateKeyId: us-east-2 # privateKey: | # -----BEGIN PRIVATE KEY----- # Super secret key # -----END PRIVATE KEY----- # clientEmail: client@mail.com # clientId: 1234 # authUri: https://accounts.google.com/o/oauth2/auth (default) # tokenUri: https://oauth2.googleapis.com/token (default) # authProviderX509CertUrl: https://www.googleapis.com/oauth2/v1/certs (default) # clientX509CertUrl: https://cert.url (URI) # dbtPrefixConfig: # dbtBucketName: bucket # dbtObjectPrefix: "dbt/" sink: type: metadata-rest config: {} workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` #### Source Configuration - Service Connection - **username**: Specify the User to connect to DB2. It should have enough privileges to read all the metadata. - **password**: Password to connect to DB2. - **hostPort**: Enter the fully qualified hostname and port number for your DB2 deployment in the Host and Port field. - **Connection Options (Optional)**: Enter the details for any additional connection options that can be sent to DB2 during the connection. These details must be added as Key-Value pairs. - **Connection Arguments (Optional)**: Enter the details for any additional connection arguments such as security or protocol configs that can be sent to DB2 during the connection. These details must be added as Key-Value pairs. - 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"` - 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"` #### Source Configuration - Source Config The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceMetadataPipeline.json): - `markDeletedTables`: To flag tables as soft-deleted if they are not present anymore in the source system. - `includeTables`: true or false, to ingest table data. Default is true. - `includeViews`: true or false, to ingest views definitions. - `databaseFilterPattern`, `schemaFilterPattern`, `tableFilternPattern`: Note that the they support regex as include or exclude. E.g., ```yaml tableFilterPattern: 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: ```yaml 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](https://github.com/open-metadata/OpenMetadata/tree/main/openmetadata-spec/src/main/resources/json/schema/security/client). You can find the different implementation of the ingestion below. ### Openmetadata JWT Auth ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: openmetadata securityConfig: jwtToken: '{bot_jwt_token}' ``` ### Auth0 SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: auth0 securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Azure SSO ```yaml 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 ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: custom-oidc securityConfig: clientId: '{your_client_id}' secretKey: '{your_client_secret}' domain: '{your_domain}' ``` ### Google SSO ```yaml workflowConfig: openMetadataServerConfig: hostPort: 'http://localhost:8585/api' authProvider: google securityConfig: secretKey: '{path-to-json-creds}' ``` ### Okta SSO ```yaml 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](https://docs.open-metadata.org/deployment/security/enable-jwt-tokens) ```yaml 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 ```yaml 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 ```yaml 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: ```bash metadata ingest -c ``` 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. ## Data Profiler The Data Profiler workflow will be using the `orm-profiler` processor. While the `serviceConnection` will still be the same to reach the source system, the `sourceConfig` will be updated from previous configurations. ### 1. Define the YAML Config This is a sample config for the profiler: ```yaml source: type: db2 serviceName: local_db2 serviceConnection: config: type: Db2 username: openmetadata_user password: openmetadata_password hostPort: localhost:5432 # databaseSchema: schema sourceConfig: config: type: Profiler # generateSampleData: true # profileSample: 85 # threadCount: 5 (default) # databaseFilterPattern: # includes: # - database1 # - database2 # excludes: # - database3 # - database4 # schemaFilterPattern: # includes: # - schema1 # - schema2 # excludes: # - schema3 # - schema4 # tableFilterPattern: # includes: # - table1 # - table2 # excludes: # - table3 # - table4 processor: type: orm-profiler config: {} # Remove braces if adding properties # tableConfig: # - fullyQualifiedName: # profileSample: # default will be 100 if omitted # profileQuery: # columnConfig: # excludeColumns: # - # includeColumns: # - columnName: # - metrics: # - MEAN # - MEDIAN # - ... # partitionConfig: # enablePartitioning: # partitionColumnName: # partitionInterval: # partitionIntervalUnit: sink: type: metadata-rest config: {} workflowConfig: # loggerLevel: DEBUG # DEBUG, INFO, WARN or ERROR openMetadataServerConfig: hostPort: authProvider: ``` #### Source Configuration - You can find all the definitions and types for the `serviceConnection` [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/entity/services/connections/database/db2Connection.json). - The `sourceConfig` is defined [here](https://github.com/open-metadata/OpenMetadata/blob/main/openmetadata-spec/src/main/resources/json/schema/metadataIngestion/databaseServiceProfilerPipeline.json). Note that the filter patterns support regex as includes or excludes. E.g., ```yaml tableFilterPattern: includes: - *users$ ``` #### Processor Choose the `orm-profiler`. Its config can also be updated to define tests from the YAML itself instead of the UI: ```yaml processor: type: orm-profiler config: tableConfig: - fullyQualifiedName:
profileSample: partitionConfig: partitionField: partitionQueryDuration: partitionValues: profileQuery: columnConfig: excludeColumns: - includeColumns: - columnName: - metrics: - MEAN - MEDIAN - ... ``` `tableConfig` allows you to set up some configuration at the table level. All the properties are optional. `metrics` should be one of the metrics listed [here](https://docs.open-metadata.org/openmetadata/ingestion/workflows/profiler/metrics) #### Workflow Configuration The same as the metadata ingestion. ### 2. Run with the CLI After saving the YAML config, we will run the command the same way we did for the metadata ingestion: ```bash metadata profile -c ``` Note how instead of running `ingest`, we are using the `profile` command to select the Profiler workflow. ## dbt Integration You can learn more about how to ingest dbt models' definitions and their lineage [here](/connectors/ingestion/workflows/dbt).