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Athena

Athena

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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:

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How to Run the Connector Externally

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. If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

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Requirements

The Athena connector ingests metadata through JDBC connections.
According to AWS’s official documentation: 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.
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. Resources of each table and database needs to be added as resource for each database user wants to ingest.
  • lakeformation – Allows principals to request temporary credentials to access data in a data lake location that is registered with Lake Formation and allows access to the LF-tags linked to databases, tables and columns. And is defined as:
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Action": [
                "athena:ListTableMetadata",
                "athena:ListDatabases",
                "athena:GetTableMetadata",
                "athena:ListQueryExecutions",
                "athena:StartQueryExecution",
                "athena:GetQueryExecution",
                "athena:GetQueryResults",
                "athena:BatchGetQueryExecution"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:athena:<<AWS_REGION>>:<<ACCOUNT_ID>>:workgroup/<<WORKGROUP_NAME>>",
                "arn:aws:athena:<<AWS_REGION>>:<<ACCOUNT_ID>>:datacatalog/<<DATA_CATALOG_NAME>>"
            ]
        },
        {
            "Action": [
                "glue:GetTables",
                "glue:GetTable",
                "glue:GetDatabases",
                "glue:GetPartitions"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:glue:<AWS_REGION>:<ACCOUNT_ID>:table/<<DATABASE_NAME>>/*",
                "arn:aws:glue:<AWS_REGION>:<ACCOUNT_ID>:database/<<DATABASE_NAME>>",
                "arn:aws:glue:<AWS_REGION>:<ACCOUNT_ID>:catalog"
            ]
        },
        {
            "Action": [
                "s3:ListBucket",
                "s3:GetObject",
                "s3:GetBucketLocation",
                "s3:PutObject"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:s3:::<<ATHENA_S3_BUCKET>>/*"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
              "lakeformation:GetResourceLFTags"
            ],
            "Resource": [
                "arn:aws:athena:<<AWS_REGION>>:<<ACCOUNT_ID>>:datacatalog/<<DATA_CATALOG_NAME>>/database/<<DATABASE_NAME>>"
                "arn:aws:athena:<<AWS_REGION>>:<<ACCOUNT_ID>>:datacatalog/<<DATA_CATALOG_NAME>>/database/<<DATABASE_NAME>>/table/<<TABLE_NAME>>"
                "arn:aws:athena:<<AWS_REGION>>:<<ACCOUNT_ID>>:datacatalog/<<DATA_CATALOG_NAME>>/database/<<DATABASE_NAME>>/table/<<TABLE_NAME>>/column/<<COLUMN_NAME>>"
            ]
        },
        {
            "Action": [
                "lambda:InvokeFunction"
            ],
            "Effect": "Allow",
            "Resource": [
                "arn:aws:lambda:<<AWS_REGION>>:<<ACCOUNT_ID>>:function:<<CONNECTOR_NAME>>"
            ]
        },
        {
            "Effect": "Allow",
            "Action": [
                "kms:Decrypt",
                "kms:DescribeKey"
            ],
            "Resource": [
                "arn:aws:kms:<<AWS_REGION>>:<<ACCOUNT_ID>>:key/<<KMS_KEY_ID>>"
            ]
        }
    ]
}

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LF-Tags

Athena connector ingests and creates LF-tags in OpenMetadata with LF-tag key mapped to OpenMetadata’s classification and the values mapped to tag labels. To ingest LF-tags provide the appropriate permissions as to the resources as mentioned above and enable the includeTags toggle in the ingestion config.
If you have external services other than glue and facing permission issues, add the permissions to the list above.
You can find further information on the Athena connector in the docs.

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Python Requirements

We have support for Python versions 3.9-3.11
To run the Athena ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[athena]"

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Metadata Ingestion

All connectors are defined as JSON Schemas. Here 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

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1. Define the YAML Config

This is a sample config for Athena:

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2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: 
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.

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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.

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1. Define the YAML Config

This is a sample config for Usage:

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2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata usage -c <path-to-yaml>

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Lineage

After running a Metadata Ingestion workflow, we can run Lineage 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.

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1. Define the YAML Config

This is a sample config for Lineage:
  • You can learn more about how to configure and run the Lineage Workflow to extract Lineage data from here

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2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata ingest -c <path-to-yaml>

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Data Profiler

The Data Profiler workflow will be using the orm-profiler processor. After running a Metadata Ingestion workflow, we can run the 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.

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1. Define the YAML Config

This is a sample config for the profiler:
  • You can learn more about how to configure and run the Profiler Workflow to extract Profiler data and execute the Data Quality from here

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2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata profile -c <path-to-yaml>
Note now instead of running ingest, we are using the profile command to select the Profiler workflow.

Data Profiler

Find more information about the Data Profiler here

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Auto Classification

The Auto Classification workflow will be using the orm-profiler processor. After running a Metadata Ingestion workflow, we can run the Auto Classification 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.

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1. Define the YAML Config

This is a sample config for the Auto Classification Workflow:

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2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata classify -c <path-to-yaml>
Now instead of running ingest, we are using the classify command to select the Auto Classification workflow.

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Data Quality

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Adding Data Quality Test Cases from yaml config

When creating a JSON config for a test workflow the source configuration is very simple. 
source:
  type: TestSuite
  serviceName: <your_service_name>
  sourceConfig:
    config:
      type: TestSuite
      entityFullyQualifiedName: <entityFqn>
The only sections you need to modify here are the serviceName (this name needs to be unique) and entityFullyQualifiedName (the entity for which we’ll be executing tests against) keys. Once you have defined your source configuration you’ll need to define te processor configuration. 
processor:
  type: "orm-test-runner"
  config:
    forceUpdate: <false|true>
    testCases:
      - name: <testCaseName>
        testDefinitionName: columnValueLengthsToBeBetween
        columnName: <columnName>
        parameterValues:
          - name: minLength
            value: 10
          - name: maxLength
            value: 25
      - name: <testCaseName>
        testDefinitionName: tableRowCountToEqual
        parameterValues:
          - name: value
            value: 10
The processor type should be set to "orm-test-runner". For accepted test definition names and parameter value names refer to the tests page.
Note that while you can define tests directly in this YAML configuration, running the workflow will execute ALL THE TESTS present in the table, regardless of what you are defining in the YAML.This makes it easy for any user to contribute tests via the UI, while maintaining the test execution external.
You can keep your YAML config as simple as follows if the table already has tests. 
processor:
  type: "orm-test-runner"
  config: {}

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Key reference:

  • forceUpdate: if the test case exists (base on the test case name) for the entity, implements the strategy to follow when running the test (i.e. whether or not to update parameters)
  • testCases: list of test cases to add to the entity referenced. Note that we will execute all the tests present in the Table.
  • name: test case name
  • testDefinitionName: test definition
  • columnName: only applies to column test. The name of the column to run the test against
  • parameterValues: parameter values of the test
The sink and workflowConfig will have the same settings as the ingestion and profiler workflow.

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Full yaml config example

source:
  type: TestSuite
  serviceName: MyAwesomeTestSuite
  sourceConfig:
    config:
      type: TestSuite
      entityFullyQualifiedName: MySQL.default.openmetadata_db.tag_usage
#     testCases: ["run_only_this_test_case"] # Optional, if not provided all tests will be executed

processor:
  type: "orm-test-runner"
  config:
    forceUpdate: false
    testCases:
      - name: column_value_length_tagFQN
        testDefinitionName: columnValueLengthsToBeBetween
        columnName: tagFQN
        parameterValues:
          - name: minLength
            value: 10
          - name: maxLength
            value: 25
      - name: table_row_count_test
        testDefinitionName: tableRowCountToEqual
        parameterValues:
          - name: value
            value: 10

sink:
  type: metadata-rest
  config: {}
workflowConfig:
  openMetadataServerConfig:
    hostPort: <OpenMetadata host and port>
    authProvider: <OpenMetadata auth provider>

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How to Run Tests

To run the tests from the CLI execute the following command 
metadata test -c /path/to/my/config.yaml

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dbt Integration

dbt Integration

Learn more about how to ingest dbt models