OpenMetadata/openmetadata-docs/content/v1.6.x-SNAPSHOT/connectors/database/s3-datalake/yaml.md

title	slug
Run the S3 Datalake Connector Externally	/connectors/database/s3-datalake/yaml

{% connectorDetailsHeader name="S3 Datalake" stage="PROD" platform="OpenMetadata" availableFeatures=["Metadata", "Data Profiler", "Data Quality"] unavailableFeatures=["Query Usage", "Lineage", "Column-level Lineage", "Owners", "dbt", "Tags", "Stored Procedures"] / %}

In this section, we provide guides and references to use the S3 Datalake connector.

Configure and schedule S3 Datalake metadata and profiler workflows from the OpenMetadata UI:

• Requirements
• Metadata Ingestion
• dbt Integration

{% partial file="/v1.6/connectors/external-ingestion-deployment.md" /%}

Requirements

Note: S3 Datalake connector supports extracting metadata from file types JSON, CSV, TSV & Parquet.

S3 Permissions

To execute metadata extraction AWS account should have enough access to fetch required data. The Bucket Policy in AWS requires at least these permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::<my bucket>",
                "arn:aws:s3:::<my bucket>/*"
            ]
        }
    ]
}

Python Requirements

{% partial file="/v1.6/connectors/python-requirements.md" /%}

If running OpenMetadata version greater than 0.13, you will need to install the Datalake ingestion for S3:

S3 installation

pip3 install "openmetadata-ingestion[datalake-s3]"

If version <0.13

You will be installing the requirements for S3

pip3 install "openmetadata-ingestion[datalake]"

Metadata Ingestion

All connectors are defined as JSON Schemas. Here you can find the structure to create a connection to Datalake.

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.

1. Define the YAML Config

Source Configuration - Source Config using AWS S3

This is a sample config for Datalake using AWS S3:

{% 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.
• awsSecretAccessKey: Enter the Secret Access Key (the passcode key pair to the key ID from above).
• awsRegion: Specify the region in which your DynamoDB is located. This setting is required even if you have configured a local AWS profile.
• schemaFilterPattern and tableFilterPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,

{% /codeInfo %}

{% partial file="/v1.6/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.6/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.6/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

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

source:
  type: datalake
  serviceName: local_datalake
  serviceConnection:
    config:
      type: Datalake

      configSource:      
        securityConfig: 
          awsAccessKeyId: aws access key id
          awsSecretAccessKey: aws secret access key
          awsRegion: aws region
      bucketName: bucket name
      prefix: prefix

{% partial file="/v1.6/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.6/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.6/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.6/connectors/yaml/ingestion-cli.md" /%}

dbt Integration

You can learn more about how to ingest dbt models' definitions and their lineage here.