---
title: Run DynamoDB Connector using Airflow SDK
slug: /connectors/database/dynamodb/airflow
---

# Run DynamoDB using the Airflow SDK

{% multiTablesWrapper %}

| Feature            | Status                       |
| :----------------- | :--------------------------- |
| Stage              | PROD                         |
| Metadata           | {% icon iconName="check" /%} |
| Query Usage        | {% icon iconName="cross" /%} |
| Data Profiler      | {% icon iconName="cross" /%} |
| Data Quality       | {% icon iconName="cross" /%} |
| Lineage            | {% icon iconName="cross" /%}          |
| DBT                | {% icon iconName="cross" /%} |
| Supported Versions | --                           |

| Feature      | Status                       |
| :----------- | :--------------------------- |
| Lineage      | {% icon iconName="cross" /%}          |
| Table-level  | {% icon iconName="cross" /%} |
| Column-level | {% icon iconName="cross" /%} |

{% /multiTablesWrapper %}

In this section, we provide guides and references to use the DynamoDB connector.

Configure and schedule DynamoDB metadata workflows from the OpenMetadata UI:

- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)


## Requirements

{%inlineCallout icon="description" bold="OpenMetadata 0.12 or later" href="/deployment"%}
To deploy OpenMetadata, check the Deployment guides.
{%/inlineCallout%}

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.

The DynamoDB connector ingests metadata using the DynamoDB boto3 client.

OpenMetadata retrieves information about all tables in the AWS account, the user must have permissions to perform the `dynamodb:ListTables` operation.

Below defined policy grants the permissions to list all tables in DynamoDB:

```json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "dynamodb:ListTables"
            ],
            "Resource": "*"
        }
    ]
}
```

For more information on Dynamodb permissions visit the [AWS DynamoDB official documentation](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/api-permissions-reference.html).

### Python Requirements

To run the DynamoDB ingestion, you will need to install:

```bash
pip3 install "openmetadata-ingestion[dynamodb]"
```

## 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/dynamoDBConnection.json)
you can find the structure to create a connection to DynamoDB.

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 DynamoDB:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

**awsAccessKeyId**: Enter your secure access key ID for your DynamoDB connection. The specified key ID should be authorized to read all databases you want to include in the metadata ingestion workflow.

{% /codeInfo %}

{% codeInfo srNumber=2 %}

**awsSecretAccessKey**: Enter the Secret Access Key (the passcode key pair to the key ID from above).

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**awsSessionToken**: The AWS session token is an optional parameter. If you want, enter the details of your temporary session token.

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**awsRegion**: Enter the location of the amazon cluster that your data and account are associated with.

{% /codeInfo %}

{% codeInfo srNumber=5 %}

**endPointURL**: Your DynamoDB connector will automatically determine the AWS DynamoDB endpoint URL based on the region. You may override this behavior by entering a value to the endpoint URL.

{% /codeInfo %}

{% codeInfo srNumber=6 %}

**databaseName**: Optional name to give to the database in OpenMetadata. If left blank, we will use default as the database name.

{% /codeInfo %}

#### Source Configuration - Source Config

{% codeInfo srNumber=9 %}

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)

{% /codeInfo %}

#### Sink Configuration

{% codeInfo srNumber=10 %}

To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.

{% /codeInfo %}

{% partial file="workflow-config.md" /%}

#### Advanced Configuration

{% codeInfo srNumber=7 %}

**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=8 %}

**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: dynamodb
  serviceName: local_dynamodb
  serviceConnection:
    config:
      type: DynamoDB
      awsConfig:
```
```yaml {% srNumber=1 %}
        awsAccessKeyId: aws_access_key_id
```
```yaml {% srNumber=2 %}
        awsSecretAccessKey: aws_secret_access_key
```
```yaml {% srNumber=3 %}
        awsSessionToken: AWS Session Token
```
```yaml {% srNumber=4 %}
        awsRegion: aws region
```
```yaml {% srNumber=5 %}
        endPointURL: https://dynamodb.<region_name>.amazonaws.com
```
```yaml {% srNumber=6 %}
      database: custom_database_name
```
```yaml {% srNumber=7 %}
      # connectionOptions:
      #   key: value
```
```yaml {% srNumber=8 %}
      # connectionArguments:
      #   key: value
```

```yaml {% srNumber=9 %}
  sourceConfig:
    config:
      type: DatabaseMetadata
      markDeletedTables: true
      includeTables: true
      includeViews: true
      # includeTags: true
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
      # tableFilterPattern:
      #   includes:
      #     - users
      #     - type_test
      #   excludes:
      #     - table3
      #     - table4
```

```yaml {% srNumber=10 %}
sink:
  type: metadata-rest
  config: {}
```

{% partial file="workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

### 2. Prepare the Ingestion DAG

Create a Python file in your Airflow DAGs directory with the following contents:

{% codePreview %}

{% codeInfoContainer %}


{% codeInfo srNumber=12 %}

#### 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=13 %}

**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=14 %}

- **config**: Specifies config for the metadata ingestion as we prepare above.

{% /codeInfo %}

{% codeInfo srNumber=15 %}

- **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=16 %}

- **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=12 %}
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=13 %}
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=14 %}
config = """
<your YAML configuration>
"""


```

```python {% srNumber=15 %}
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=16 %}
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 %}

## dbt Integration

{% tilesContainer %}

{% tile
  icon="mediation"
  title="dbt Integration"
  description="Learn more about how to ingest dbt models' definitions and their lineage."
  link="/connectors/ingestion/workflows/dbt" /%}

{% /tilesContainer %}

## Related

{% tilesContainer %}

{% tile
    title="Ingest with the CLI"
    description="Run a one-time ingestion using the metadata CLI"
    link="/connectors/database/dynamodb/cli"
  / %}

{% /tilesContainer %}