Any tool capable of running Python code can be used to configure the metadata extraction from your sources({% partial file="/v1.4/connectors/python-requirements.md" /%}).
Run the ingestion process externally from GitHub Actions
{% /inlineCallout %}
{% /inlineCalloutContainer %}
Let's jump now into some examples on how you could create the function the run the different workflows. Note that this code
can then be executed inside a DAG, a GitHub action, or a vanilla Python script. It will work for any environment.
### Metadata Workflow
This is the first workflow you have to configure and run. It will take care of fetching the metadata from your sources,
be it Database Services, Dashboard Services, Pipelines, etc.
The rest of the workflows (Lineage, Profiler,...) will be executed on top of the metadata already available in the platform.
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `MetadataWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need. In the end, it's just
Python code.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.metadata import MetadataWorkflow
from metadata.workflow.workflow_output_handler import print_status
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake
serviceName: <servicename>
serviceConnection:
config:
type: Snowflake
...
sourceConfig:
config:
type: DatabaseMetadata
markDeletedTables: true
includeTables: true
...
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = MetadataWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
print_status(workflow)
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
### Lineage Workflow
This workflow will take care of scanning your query history and defining lineage relationships between your tables.
You can find more information about this workflow [here](/connectors/ingestion/lineage).
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `MetadataWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
Note that we are using the same class as in the Metadata Ingestion.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.metadata import MetadataWorkflow
from metadata.workflow.workflow_output_handler import print_status
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake-lineage
serviceName: <servicename>
sourceConfig:
config:
type: DatabaseLineage
queryLogDuration: 1
parsingTimeoutLimit: 300
...
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = MetadataWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
print_status(workflow)
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
### Usage Workflow
As with the lineage workflow, we'll scan the query history for any DML statements. The goal is to ingest queries
into the platform, figure out the relevancy of your assets and frequently joined tables.
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `UsageWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.usage import UsageWorkflow
from metadata.workflow.workflow_output_handler import print_status
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake-usage
serviceName: <servicename>
sourceConfig:
config:
type: DatabaseUsage
queryLogDuration: 1
parsingTimeoutLimit: 300
...
processor:
type: query-parser
config: {}
stage:
type: table-usage
config:
filename: "/tmp/snowflake_usage"
bulkSink:
type: metadata-usage
config:
filename: "/tmp/snowflake_usage"
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = UsageWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
print_status(workflow)
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
### Profiler Workflow
This workflow will execute queries against your database and send the results into OpenMetadata. The goal is to compute
metrics about your data and give you a high-level view of its shape, together with the sample data.
This is an interesting previous step before creating Data Quality Workflows.
You can find more information about this workflow [here](/connectors/ingestion/workflows/profiler).
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `ProfilerWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.profiler import ProfilerWorkflow
from metadata.workflow.workflow_output_handler import print_status
```
```python {% srNumber=2 %}
CONFIG = """
source:
type: snowflake
serviceName: <servicename>
sourceConfig:
config:
type: Profiler
generateSampleData: true
...
processor:
type: orm-profiler
config: {}
sink:
type: metadata-rest
config: {}
workflowConfig:
openMetadataServerConfig:
hostPort: "http://localhost:8585/api"
authProvider: openmetadata
securityConfig:
jwtToken: "{bot_jwt_token}"
"""
```
```python {% srNumber=3 %}
def run():
workflow = ProfilerWorkflow.create(CONFIG)
workflow.execute()
workflow.raise_from_status()
print_status(workflow)
workflow.stop()
```
{% /codeBlock %}
{% /codePreview %}
### Data Quality Workflow
This workflow will execute queries against your database and send the results into OpenMetadata. The goal is to compute
metrics about your data and give you a high-level view of its shape, together with the sample data.
This is an interesting previous step before creating Data Quality Workflows.
You can find more information about this workflow [here](/connectors/ingestion/workflows/data-quality).
{% codePreview %}
{% codeInfoContainer %}
{% codeInfo srNumber=1 %}
**Adding the imports**
The first step is to import the `TestSuiteWorkflow` class, which will take care of the full ingestion logic. We'll
add the import for printing the results at the end.
{% /codeInfo %}
{% codeInfo srNumber=2 %}
**Defining the YAML**
Then, we need to pass the YAML configuration. For this simple example we are defining a variable, but you can
read from a file, parse secrets from your environment, or any other approach you'd need.
Note how we have not added here the `serviceConnection`. Since the service would have been created during the
metadata ingestion, we can let the Ingestion Framework dynamically fetch the Service Connection information.
If, however, you are configuring the workflow with `storeServiceConnection: false`, you'll need to explicitly
define the `serviceConnection`.
Moreover, see how we are not configuring any tests in the `processor`. You can do [that](/connectors/ingestion/workflows/data-quality#full-yaml-config-example),
but even if nothing gets defined in the YAML, we will execute all the tests configured against the table.
{% note %}
You can find complete YAMLs in each connector [docs](/connectors) and find more information about the available
configurations.
{% /note %}
{% /codeInfo %}
{% codeInfo srNumber=3 %}
**Preparing the Workflow**
Finally, we'll prepare a function that we can execute anywhere.
It will take care of instantiating the workflow, executing it and giving us the results.
{% /codeInfo %}
{% /codeInfoContainer %}
{% codeBlock fileName="ingestion.py" %}
```python
import yaml
```
```python {% srNumber=1 %}
from metadata.workflow.data_quality import TestSuiteWorkflow
from metadata.workflow.workflow_output_handler import print_status
