**Note**: To retrieve metadata from an Oracle database, the python-oracledb library can be utilized, which provides support for versions 12c, 18c, 19c, and 21c.
**username**: Specify the User to connect to Oracle. It should have enough privileges to read all the metadata.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**password**: Password to connect to Oracle.
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**hostPort**: Enter the fully qualified hostname and port number for your Oracle deployment in the Host and Port field.
{% /codeInfo %}
{% codeInfo srNumber=4 %}
**oracleConnectionType** :
- **oracleServiceName**: The Oracle Service name is the TNS alias that you give when you remotely connect to your database and this Service name is recorded in tnsnames.
- **databaseSchema**: The name of the database schema available in Oracle that you want to connect with.
- **Oracle instant client directory**: The directory pointing to where the `instantclient` binaries for Oracle are located. In the ingestion Docker image we
provide them by default at `/instantclient`. If this parameter is informed (it is by default), we will run the [thick oracle client](https://python-oracledb.readthedocs.io/en/latest/user_guide/initialization.html#initializing-python-oracledb).
We are shipping the binaries for ARM and AMD architectures from [here](https://www.oracle.com/database/technologies/instant-client/linux-x86-64-downloads.html)
and [here](https://www.oracle.com/database/technologies/instant-client/linux-arm-aarch64-downloads.html) for the instant client version 19.
{% /codeInfo %}
#### Source Configuration - Source Config
{% codeInfo srNumber=7 %}
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 filter supports regex as include or exclude. You can find examples [here](/connectors/ingestion/workflows/metadata/filter-patterns/database)
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}
#### Workflow Configuration
{% codeInfo srNumber=9 %}
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 %}
#### Advanced Configuration
{% codeInfo srNumber=5 %}
**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.
{% /codeInfo %}
{% codeInfo srNumber=6 %}
**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.
- 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"`
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="filename.yaml" %}
```yaml
source:
type: oracle
serviceName: local_oracle
serviceConnection:
config:
type: Oracle
```
```yaml {% srNumber=1 %}
hostPort: hostPort
```
```yaml {% srNumber=2 %}
username: username
```
```yaml {% srNumber=3 %}
password: password
```
```yaml {% srNumber=4 %}
# The type can either be oracleServiceName or databaseSchema
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).
## Openmetadata JWT Auth
- 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).
```yaml
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
```
- 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).
### 2. Prepare the Ingestion DAG
Create a Python file in your Airflow DAGs directory with the following contents:
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=10 %}
#### Import necessary modules
The `Workflow` class that is being imported is a part of a metadata ingestion framework, which defines a process of getting data from different sources and ingesting it into a central metadata repository.
Here we are also importing all the basic requirements to parse YAMLs, handle dates and build our DAG.
{% /codeInfo %}
{% codeInfo srNumber=11 %}
**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=12 %}
- **config**: Specifies config for the metadata ingestion as we prepare above.
{% /codeInfo %}
{% codeInfo srNumber=13 %}
- **metadata_ingestion_workflow()**: This code defines a function `metadata_ingestion_workflow()` that loads a YAML configuration, creates a `Workflow` object, executes the workflow, checks its status, prints the status to the console, and stops the workflow.
{% /codeInfo %}
{% codeInfo srNumber=14 %}
- **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 %}
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.
{% /codeInfoContainer %}
{% codeBlock fileName="filename.py" %}
```python {% srNumber=10 %}
import pathlib
import yaml
from datetime import timedelta
from airflow import DAG
from metadata.config.common import load_config_file
from metadata.ingestion.api.workflow import Workflow
from airflow.utils.dates import days_ago
try:
from airflow.operators.python import PythonOperator
except ModuleNotFoundError:
from airflow.operators.python_operator import PythonOperator
```
```python {% srNumber=11 %}
default_args = {
"owner": "user_name",
"email": ["username@org.com"],
"email_on_failure": False,
"retries": 3,
"retry_delay": timedelta(minutes=5),
"execution_timeout": timedelta(minutes=60)
}
```
```python {% srNumber=12 %}
config = """
<yourYAMLconfiguration>
"""
```
```python {% srNumber=13 %}
def metadata_ingestion_workflow():
workflow_config = yaml.safe_load(config)
workflow = Workflow.create(workflow_config)
workflow.execute()
workflow.raise_from_status()
workflow.print_status()
workflow.stop()
```
```python {% srNumber=14 %}
with DAG(
"sample_data",
default_args=default_args,
description="An example DAG which runs a OpenMetadata ingestion workflow",
start_date=days_ago(1),
is_paused_upon_creation=False,
schedule_interval='*/5 * ** *',
catchup=False,
) as dag:
ingest_task = PythonOperator(
task_id="ingest_using_recipe",
python_callable=metadata_ingestion_workflow,
)
```
{% /codeBlock %}
{% /codePreview %}
## Data Profiler
The Data Profiler workflow will be using the `orm-profiler` processor.
After running a Metadata Ingestion workflow, we can run Data Profiler workflow.
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.
### 1. Define the YAML Config
This is a sample config for the profiler:
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=15 %}
#### Source Configuration - Source Config
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).
**generateSampleData**: Option to turn on/off generating sample data.
{% /codeInfo %}
{% codeInfo srNumber=16 %}
**profileSample**: Percentage of data or no. of rows we want to execute the profiler and tests on.
{% /codeInfo %}
{% codeInfo srNumber=17 %}
**threadCount**: Number of threads to use during metric computations.
{% /codeInfo %}
{% codeInfo srNumber=18 %}
**processPiiSensitive**: Optional configuration to automatically tag columns that might contain sensitive information.
{% /codeInfo %}
{% codeInfo srNumber=19 %}
**confidence**: Set the Confidence value for which you want the column to be marked
{% /codeInfo %}
{% codeInfo srNumber=20 %}
**timeoutSeconds**: Profiler Timeout in Seconds
{% /codeInfo %}
{% codeInfo srNumber=21 %}
**databaseFilterPattern**: Regex to only fetch databases that matches the pattern.
{% /codeInfo %}
{% codeInfo srNumber=22 %}
**schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
{% /codeInfo %}
{% codeInfo srNumber=23 %}
**tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.
{% /codeInfo %}
{% codeInfo srNumber=24 %}
#### 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=25 %}
#### Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}
{% codeInfo srNumber=26 %}
#### 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: oracle
serviceName: local_oracle
sourceConfig:
config:
type: Profiler
```
```yaml {% srNumber=15 %}
generateSampleData: true
```
```yaml {% srNumber=16 %}
# profileSample: 85
```
```yaml {% srNumber=17 %}
# threadCount: 5
```
```yaml {% srNumber=18 %}
processPiiSensitive: false
```
```yaml {% srNumber=19 %}
# confidence: 80
```
```yaml {% srNumber=20 %}
# timeoutSeconds: 43200
```
```yaml {% srNumber=21 %}
# databaseFilterPattern:
# includes:
# - database1
# - database2
# excludes:
# - database3
# - database4
```
```yaml {% srNumber=22 %}
# schemaFilterPattern:
# includes:
# - schema1
# - schema2
# excludes:
# - schema3
# - schema4
```
```yaml {% srNumber=23 %}
# tableFilterPattern:
# includes:
# - table1
# - table2
# excludes:
# - table3
# - table4
```
```yaml {% srNumber=24 %}
processor:
type: orm-profiler
config: {} # Remove braces if adding properties
# tableConfig:
# - fullyQualifiedName: <tablefqn>
# profileSample: <numberbetween0and99> # default
# profileSample: <numberbetween0and99> # default will be 100 if omitted
- 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=27 %}
#### 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=28 %}
**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=29 %}
- **config**: Specifies config for the profiler as we prepare above.
{% /codeInfo %}
{% codeInfo srNumber=30 %}
- **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=31 %}
- **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).
