OpenMetadata/openmetadata-docs/content/v1.2.0-SNAPSHOT/connectors/database/athena/yaml.md

---
title: Run the Athena Connector Externally
slug: /connectors/database/athena/yaml
---

# Run the Athena Connector Externally

{% multiTablesWrapper %}

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

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

{% /multiTablesWrapper %}

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

Configure and schedule Athena metadata and profiler workflows from the OpenMetadata UI:

- [Requirements](#requirements)
- [Metadata Ingestion](#metadata-ingestion)
- [Query Usage](#query-usage)
- [Data Profiler](#data-profiler)
- [Lineage](#lineage)
- [dbt Integration](#dbt-integration)

{% partial file="/v1.2.0/connectors/external-ingestion-deployment.md" /%}

## Requirements

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



The Athena connector ingests metadata through JDBC connections.

{% note %}

According to AWS's official [documentation](https://docs.aws.amazon.com/athena/latest/ug/policy-actions.html):

*If you are using the JDBC or ODBC driver, ensure that the IAM
permissions policy includes all of the actions listed in [AWS managed policy: AWSQuicksightAthenaAccess](https://docs.aws.amazon.com/athena/latest/ug/managed-policies.html#awsquicksightathenaaccess-managed-policy).*

{% /note %}

This policy groups the following permissions:

- `athena` – Allows the principal to run queries on Athena resources.
- `glue` – Allows principals access to AWS Glue databases, tables, and partitions. This is required so that the principal can use the AWS Glue Data Catalog with Athena.
- `s3` – Allows the principal to write and read query results from Amazon S3.
- `lakeformation` – Allows principals to request temporary credentials to access data in a data lake location that is registered with Lake Formation.

And is defined as:

```json
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "athena:BatchGetQueryExecution",
                "athena:GetQueryExecution",
                "athena:GetQueryResults",
                "athena:GetQueryResultsStream",
                "athena:ListQueryExecutions",
                "athena:StartQueryExecution",
                "athena:StopQueryExecution",
                "athena:ListWorkGroups",
                "athena:ListEngineVersions",
                "athena:GetWorkGroup",
                "athena:GetDataCatalog",
                "athena:GetDatabase",
                "athena:GetTableMetadata",
                "athena:ListDataCatalogs",
                "athena:ListDatabases",
                "athena:ListTableMetadata"
            ],
            "Resource": [
                "*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "glue:CreateDatabase",
                "glue:DeleteDatabase",
                "glue:GetDatabase",
                "glue:GetDatabases",
                "glue:UpdateDatabase",
                "glue:CreateTable",
                "glue:DeleteTable",
                "glue:BatchDeleteTable",
                "glue:UpdateTable",
                "glue:GetTable",
                "glue:GetTables",
                "glue:BatchCreatePartition",
                "glue:CreatePartition",
                "glue:DeletePartition",
                "glue:BatchDeletePartition",
                "glue:UpdatePartition",
                "glue:GetPartition",
                "glue:GetPartitions",
                "glue:BatchGetPartition"
            ],
            "Resource": [
                "*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetBucketLocation",
                "s3:GetObject",
                "s3:ListBucket",
                "s3:ListBucketMultipartUploads",
                "s3:ListMultipartUploadParts",
                "s3:AbortMultipartUpload",
                "s3:CreateBucket",
                "s3:PutObject",
                "s3:PutBucketPublicAccessBlock"
            ],
            "Resource": [
                "arn:aws:s3:::aws-athena-query-results-*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "lakeformation:GetDataAccess"
            ],
            "Resource": [
                "*"
            ]
        }
    ]
}
```

You can find further information on the Athena connector in the [docs](https://docs.open-metadata.org/connectors/database/athena).

### Python Requirements

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

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

## 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/athenaConnection.json)
you can find the structure to create a connection to Athena.

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 Athena:

{% codePreview %}

{% codeInfoContainer %}

#### Source Configuration - Service Connection

{% codeInfo srNumber=1 %}

- **awsAccessKeyId** & **awsSecretAccessKey**: When you interact with AWS, you specify your AWS security credentials to verify who you are and whether you have
  permission to access the resources that you are requesting. AWS uses the security credentials to authenticate and
  authorize your requests ([docs](https://docs.aws.amazon.com/IAM/latest/UserGuide/security-creds.html)).

Access keys consist of two parts: An **access key ID** (for example, `AKIAIOSFODNN7EXAMPLE`), and a **secret access key** (for example, `wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY`).

You must use both the access key ID and secret access key together to authenticate your requests.

You can find further information on how to manage your access keys [here](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_access-keys.html).

{% /codeInfo %}

{% codeInfo srNumber=2 %}
**awsSessionToken**: If you are using temporary credentials to access your services, you will need to inform the AWS Access Key ID
and AWS Secrets Access Key. Also, these will include an AWS Session Token.

{% /codeInfo %}

{% codeInfo srNumber=3 %}

**awsRegion**: Each AWS Region is a separate geographic area in which AWS clusters data centers ([docs](https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Concepts.RegionsAndAvailabilityZones.html)).

As AWS can have instances in multiple regions, we need to know the region the service you want reach belongs to.

Note that the AWS Region is the only required parameter when configuring a connection. When connecting to the
services programmatically, there are different ways in which we can extract and use the rest of AWS configurations.

You can find further information about configuring your credentials [here](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html#configuring-credentials).

{% /codeInfo %}

{% codeInfo srNumber=4 %}

**endPointURL**: To connect programmatically to an AWS service, you use an endpoint. An *endpoint* is the URL of the
entry point for an AWS web service. The AWS SDKs and the AWS Command Line Interface (AWS CLI) automatically use the
default endpoint for each service in an AWS Region. But you can specify an alternate endpoint for your API requests.

Find more information on [AWS service endpoints](https://docs.aws.amazon.com/general/latest/gr/rande.html).

{% /codeInfo %}

{% codeInfo srNumber=5 %}

**profileName**: A named profile is a collection of settings and credentials that you can apply to a AWS CLI command.
When you specify a profile to run a command, the settings and credentials are used to run that command.
Multiple named profiles can be stored in the config and credentials files.

You can inform this field if you'd like to use a profile other than `default`.

Find here more information about [Named profiles for the AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-profiles.html).

{% /codeInfo %}

{% codeInfo srNumber=6 %}

**assumeRoleArn**: Typically, you use `AssumeRole` within your account or for cross-account access. In this field you'll set the
`ARN` (Amazon Resource Name) of the policy of the other account.

A user who wants to access a role in a different account must also have permissions that are delegated from the account
administrator. The administrator must attach a policy that allows the user to call `AssumeRole` for the `ARN` of the role in the other account.

This is a required field if you'd like to `AssumeRole`.

Find more information on [AssumeRole](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html).
{% /codeInfo %}

{% codeInfo srNumber=7 %}

**assumeRoleSessionName**: An identifier for the assumed role session. Use the role session name to uniquely identify a session when the same role
is assumed by different principals or for different reasons.

By default, we'll use the name `OpenMetadataSession`.

Find more information about the [Role Session Name](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html#:~:text=An%20identifier%20for%20the%20assumed%20role%20session.).

{% /codeInfo %}

{% codeInfo srNumber=8 %}

**assumeRoleSourceIdentity**: The source identity specified by the principal that is calling the `AssumeRole` operation. You can use source identity
information in AWS CloudTrail logs to determine who took actions with a role.

Find more information about [Source Identity](https://docs.aws.amazon.com/STS/latest/APIReference/API_AssumeRole.html#:~:text=Required%3A%20No-,SourceIdentity,-The%20source%20identity).

{% /codeInfo %}

{% codeInfo srNumber=9 %}

**s3StagingDir**: The S3 staging directory is an optional parameter. Enter a staging directory to override the default staging directory for AWS Athena.

{% /codeInfo %}

{% codeInfo srNumber=10 %}

**workgroup**: The Athena workgroup is an optional parameter. If you wish to have your Athena connection related to an existing AWS workgroup add your workgroup name here.

{% /codeInfo %}

#### Source Configuration - Source Config

{% codeInfo srNumber=13 %}

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=14 %}


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

{% /codeInfo %}

{% partial file="/v1.2.0/connectors/workflow-config.md" /%}

#### Advanced Configuration

{% codeInfo srNumber=11 %}

**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=12 %}

**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.

{% /codeInfo %}

{% /codeInfoContainer %}

{% codeBlock fileName="filename.yaml" %}

```yaml
source:
  type: athena
  serviceName: local_athena
  serviceConnection:
    config:
      type: Athena
      awsConfig:
```
```yaml {% srNumber=1 %}
        awsAccessKeyId: KEY
        awsSecretAccessKey: SECRET
```
```yaml {% srNumber=2 %}
        # awsSessionToken: TOKEN
```
```yaml {% srNumber=3 %}
        awsRegion: us-east-2
```
```yaml {% srNumber=4 %}
        # endPointURL: https://athena.us-east-2.amazonaws.com/custom
```
```yaml {% srNumber=5 %}
        # profileName: profile
```
```yaml {% srNumber=6 %}
        # assumeRoleArn: "arn:partition:service:region:account:resource"
```
```yaml {% srNumber=7 %}
        # assumeRoleSessionName: session
```
```yaml {% srNumber=8 %}
        # assumeRoleSourceIdentity: identity
```
```yaml {% srNumber=9 %}
      s3StagingDir: s3 directory for datasource
```
```yaml {% srNumber=10 %}
      workgroup: workgroup name
```
```yaml {% srNumber=11 %}
      # connectionOptions:
        # key: value
```
```yaml {% srNumber=12 %}
      # connectionArguments:
        # key: value
```

```yaml {% srNumber=13 %}
  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=14 %}
sink:
  type: metadata-rest
  config: {}
```

{% partial file="/v1.2.0/connectors/workflow-config-yaml.md" /%}

{% /codeBlock %}

{% /codePreview %}

### 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 <path-to-yaml>
```

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.

## Query Usage

The Query Usage workflow will be using the `query-parser` processor.

After running a Metadata Ingestion workflow, we can run Query Usage 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 BigQuery Usage:

{% codePreview %}

{% codeInfoContainer %}

{% codeInfo srNumber=25 %}

#### 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/databaseServiceQueryUsagePipeline.json).

**queryLogDuration**: Configuration to tune how far we want to look back in query logs to process usage data.

{% /codeInfo %}

{% codeInfo srNumber=26 %}

**stageFileLocation**: Temporary file name to store the query logs before processing. Absolute file path required.

{% /codeInfo %}

{% codeInfo srNumber=27 %}

**resultLimit**: Configuration to set the limit for query logs

{% /codeInfo %}

{% codeInfo srNumber=28 %}

**queryLogFilePath**: Configuration to set the file path for query logs

{% /codeInfo %}


{% codeInfo srNumber=29 %}

#### Processor, Stage and Bulk Sink Configuration

To specify where the staging files will be located.

Note that the location is a directory that will be cleaned at the end of the ingestion.

{% /codeInfo %}

{% codeInfo srNumber=30 %}

#### 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: athena-usage
  serviceName: <service name>
  sourceConfig:
    config:
      type: DatabaseUsage
```
```yaml {% srNumber=25 %}
      # Number of days to look back
      queryLogDuration: 7
```

```yaml {% srNumber=26 %}
      # This is a directory that will be DELETED after the usage runs
      stageFileLocation: <path to store the stage file>
```

```yaml {% srNumber=27 %}
      # resultLimit: 1000
```

```yaml {% srNumber=28 %}
      # If instead of getting the query logs from the database we want to pass a file with the queries
      # queryLogFilePath: path-to-file
```

```yaml {% srNumber=29 %}
processor:
  type: query-parser
  config: {}
stage:
  type: table-usage
  config:
    filename: /tmp/athena_usage
bulkSink:
  type: metadata-usage
  config:
    filename: /tmp/athena_usage
```

```yaml {% srNumber=30 %}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>
```

{% /codeBlock %}
{% /codePreview %}

### 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 ingest -c <path-to-yaml>
```

## 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=13 %}
#### 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=14 %}

**profileSample**: Percentage of data or no. of rows we want to execute the profiler and tests on.

{% /codeInfo %}

{% codeInfo srNumber=15 %}

**threadCount**: Number of threads to use during metric computations.

{% /codeInfo %}

{% codeInfo srNumber=16 %}

**processPiiSensitive**: Optional configuration to automatically tag columns that might contain sensitive information.

{% /codeInfo %}

{% codeInfo srNumber=17 %}

**confidence**: Set the Confidence value for which you want the column to be marked

{% /codeInfo %}


{% codeInfo srNumber=18 %}

**timeoutSeconds**: Profiler Timeout in Seconds

{% /codeInfo %}

{% codeInfo srNumber=19 %}

**databaseFilterPattern**: Regex to only fetch databases that matches the pattern.

{% /codeInfo %}

{% codeInfo srNumber=20 %}

**schemaFilterPattern**: Regex to only fetch tables or databases that matches the pattern.

{% /codeInfo %}

{% codeInfo srNumber=21 %}

**tableFilterPattern**: Regex to only fetch tables or databases that matches the pattern.

{% /codeInfo %}

{% codeInfo srNumber=22 %}

#### 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=23 %}

#### Sink Configuration

To send the metadata to OpenMetadata, it needs to be specified as `type: metadata-rest`.
{% /codeInfo %}


{% codeInfo srNumber=24 %}

#### 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: athena
  serviceName: local_athena
  sourceConfig:
    config:
      type: Profiler
```

```yaml {% srNumber=13 %}
      generateSampleData: true
```
```yaml {% srNumber=14 %}
      # profileSample: 85
```
```yaml {% srNumber=15 %}
      # threadCount: 5
```
```yaml {% srNumber=16 %}
      processPiiSensitive: false
```
```yaml {% srNumber=17 %}
      # confidence: 80
```
```yaml {% srNumber=18 %}
      # timeoutSeconds: 43200
```
```yaml {% srNumber=19 %}
      # databaseFilterPattern:
      #   includes:
      #     - database1
      #     - database2
      #   excludes:
      #     - database3
      #     - database4
```
```yaml {% srNumber=20 %}
      # schemaFilterPattern:
      #   includes:
      #     - schema1
      #     - schema2
      #   excludes:
      #     - schema3
      #     - schema4
```
```yaml {% srNumber=21 %}
      # tableFilterPattern:
      #   includes:
      #     - table1
      #     - table2
      #   excludes:
      #     - table3
      #     - table4
```

```yaml {% srNumber=22 %}
processor:
  type: orm-profiler
  config: {}  # Remove braces if adding properties
    # tableConfig:
    #   - fullyQualifiedName: <table fqn>
    #     profileSample: <number between 0 and 99> # default 

    #     profileSample: <number between 0 and 99> # default will be 100 if omitted
    #     profileQuery: <query to use for sampling data for the profiler>
    #     columnConfig:
    #       excludeColumns:
    #         - <column name>
    #       includeColumns:
    #         - columnName: <column name>
    #         - metrics:
    #           - MEAN
    #           - MEDIAN
    #           - ...
    #     partitionConfig:
    #       enablePartitioning: <set to true to use partitioning>
    #       partitionColumnName: <partition column name. Must be a timestamp or datetime/date field type>
    #       partitionInterval: <partition interval>
    #       partitionIntervalUnit: <YEAR, MONTH, DAY, HOUR>

```

```yaml {% srNumber=23 %}
sink:
  type: metadata-rest
  config: {}
```

```yaml {% srNumber=24 %}
workflowConfig:
  # loggerLevel: DEBUG  # DEBUG, INFO, WARN or ERROR
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>
```

{% /codeBlock %}

{% /codePreview %}

- 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. 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 <path-to-yaml>
```

Note now instead of running `ingest`, we are using the `profile` command to select the Profiler workflow.

## Lineage

You can learn more about how to ingest lineage [here](/connectors/ingestion/workflows/lineage).

## 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 Airflow"
    description="Configure the ingestion using Airflow SDK"
    link="/connectors/database/athena/airflow"
  / %}

{% /tilesContainer %}