OpenMetadata/openmetadata-docs/content/v1.8.x-SNAPSHOT/connectors/database/gcs-datalake/yaml.md

title	slug
Run the GCS Datalake Connector Externally	/connectors/database/gcs-datalake/yaml

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

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

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

• Requirements
• Metadata Ingestion
• dbt Integration

{% partial file="/v1.8/connectors/external-ingestion-deployment.md" /%}

Requirements

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

Python Requirements

{% partial file="/v1.8/connectors/python-requirements.md" /%}

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

GCS installation

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

If version <0.13

You will be installing the requirements for GCS

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

This is a sample config for Datalake using GCS:

{% codePreview %}

{% codeInfoContainer %}

Source Configuration - Service Connection

{% partial file="/v1.8/connectors/yaml/common/gcp-config-def.md" /%}

{% codeInfo srNumber=5 %}

• bucketName: name of the bucket in GCS
• Prefix: prefix in gcp bucket

{% /codeInfo %}

{% partial file="/v1.8/connectors/yaml/database/source-config-def.md" /%}

{% partial file="/v1.8/connectors/yaml/ingestion-sink-def.md" /%}

{% partial file="/v1.8/connectors/yaml/workflow-config-def.md" /%}

{% /codeInfoContainer %}

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

source:
  type: datalake
  serviceName: local_datalake
  serviceConnection:
    config:
      type: Datalake
      configSource:
        securityConfig:
          gcpConfig:

{% partial file="/v1.8/connectors/yaml/common/gcp-config.md" /%}

      bucketName: bucket name
      prefix: prefix

{% partial file="/v1.8/connectors/yaml/database/source-config.md" /%}

{% partial file="/v1.8/connectors/yaml/ingestion-sink.md" /%}

{% partial file="/v1.8/connectors/yaml/workflow-config.md" /%}

{% /codeBlock %}

{% /codePreview %}

{% partial file="/v1.8/connectors/yaml/ingestion-cli.md" /%}

dbt Integration

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