OpenMetadata/openmetadata-docs/content/connectors/database/datalake/index.md

title	slug
Datalake	/connectors/database/datalake

Datalake

Stage	Metadata	Query Usage	Data Profiler	Data Quality	Lineage	DBT	Supported Versions
PROD	✅	❌	✅	✅	❌	❌	--

Lineage	Table-level	Column-level
❌	❌	❌

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

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

• Requirements
• Metadata Ingestion
• Data Profiler
• Data Quality

If you don't want to use the OpenMetadata Ingestion container to configure the workflows via the UI, then you can check the following docs to connect using Airflow SDK or with the CLI.

Requirements

To deploy OpenMetadata, check the Deployment guides.

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.

Datalake connector supports extracting metadata from file types JSON, CSV, TSV, Parquet & Avro.

** 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>/*"
            ]
        }
    ]
}

Metadata Ingestion

1. Visit the Services Page

The first step is ingesting the metadata from your sources. Under Settings, you will find a Services link an external source system to OpenMetadata. Once a service is created, it can be used to configure metadata, usage, and profiler workflows.

To visit the Services page, select Services from the Settings menu.

2. Create a New Service

Click on the Add New Service button to start the Service creation.

3. Select the Service Type

Select Datalake as the service type and click Next.

4. Name and Describe your Service

Provide a name and description for your service as illustrated below.

Service Name

OpenMetadata uniquely identifies services by their Service Name. Provide a name that distinguishes your deployment from other services, including the other {connector} services that you might be ingesting metadata from.

5. Configure the Service Connection

In this step, we will configure the connection settings required for this connector. Please follow the instructions below to ensure that you've configured the connector to read from your datalake service as desired.

Once the credentials have been added, click on Test Connection and Save the changes.

Connection Options

** 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>/*"
            ]
        }
    ]
}

Connection Options

AWS Access Key ID

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.

AWS Secret Access Key

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

AWS Region

Specify the region in which your DynamoDB is located.

Note: This setting is required even if you have configured a local AWS profile.

AWS Session Token

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

Endpoint URL (optional)

The DynamoDB connector will automatically determine the DynamoDB endpoint URL based on the AWS Region. You may specify a value to override this behavior.

Database (Optional)

The database of the data source is an optional parameter, if you would like to restrict the metadata reading to a single database. If left blank, OpenMetadata ingestion attempts to scan all the databases.

Connection Options (Optional)

Enter the details for any additional connection options that can be sent to DynamoDB during the connection. These details must be added as Key-Value pairs.

Connection Arguments (Optional)

Enter the details for any additional connection arguments such as security or protocol configs that can be sent to DynamoDB during the connection. These details must be added as Key-Value pairs.

BUCKET NAME

This is the Bucket Name in GCS.

PREFIX

This is the Bucket Name in GCS.

GCS Credentials

We support two ways of authenticating to GCS:

1. Passing the raw credential values provided by BigQuery. This requires us to provide the following information, all provided by BigQuery:
   1. Credentials type, e.g. service_account.
   2. Project ID
   3. Private Key ID
   4. Private Key
   5. Client Email
   6. Client ID
   7. Auth URI, https://accounts.google.com/o/oauth2/auth by default
   8. Token URI, https://oauth2.googleapis.com/token by default
   9. Authentication Provider X509 Certificate URL, https://www.googleapis.com/oauth2/v1/certs by default
   10. Client X509 Certificate URL

• Azure Credentials

  • Client ID : Client ID of the data storage account
  • Client Secret : Client Secret of the account
  • Tenant ID : Tenant ID under which the data storage account falls
  • Account Name : Account Name of the data Storage

• Required Roles

  Please make sure the following roles associated with the data storage account.

  • Storage Blob Data Contributor
  • Storage Queue Data Contributor

The current approach for authentication is based on app registration, reach out to us on slack if you find the need for another auth system

6. Configure Metadata Ingestion

In this step we will configure the metadata ingestion pipeline, Please follow the instructions below

Metadata Ingestion Options

• Name: This field refers to the name of ingestion pipeline, you can customize the name or use the generated name.
• Database Filter Pattern (Optional): Use to database filter patterns to control whether or not to include database as part of metadata ingestion.
  • Include: Explicitly include databases by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all databases with names matching one or more of the supplied regular expressions. All other databases will be excluded.
  • Exclude: Explicitly exclude databases by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all databases with names matching one or more of the supplied regular expressions. All other databases will be included.
• Schema Filter Pattern (Optional): Use to schema filter patterns to control whether or not to include schemas as part of metadata ingestion.
  • Include: Explicitly include schemas by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all schemas with names matching one or more of the supplied regular expressions. All other schemas will be excluded.
  • Exclude: Explicitly exclude schemas by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all schemas with names matching one or more of the supplied regular expressions. All other schemas will be included.
• Table Filter Pattern (Optional): Use to table filter patterns to control whether or not to include tables as part of metadata ingestion.
  • Include: Explicitly include tables by adding a list of comma-separated regular expressions to the Include field. OpenMetadata will include all tables with names matching one or more of the supplied regular expressions. All other tables will be excluded.
  • Exclude: Explicitly exclude tables by adding a list of comma-separated regular expressions to the Exclude field. OpenMetadata will exclude all tables with names matching one or more of the supplied regular expressions. All other tables will be included.
• Include views (toggle): Set the Include views toggle to control whether or not to include views as part of metadata ingestion.
• Include tags (toggle): Set the Include tags toggle to control whether or not to include tags as part of metadata ingestion.
• Enable Debug Log (toggle): Set the Enable Debug Log toggle to set the default log level to debug, these logs can be viewed later in Airflow.
• Mark Deleted Tables (toggle): This is an optional configuration for enabling soft deletion of tables. When this option is enabled, only tables that have been deleted from the source will be soft deleted, and this will apply solely to the schema that is currently being ingested via the pipeline. Any related entities such as test suites or lineage information that were associated with those tables will also be deleted..
• Mark All Deleted Tables (toggle): This is an optional configuration for enabling soft deletion of tables. When this option is enabled, only tables that have been deleted from the source will be soft deleted, and this will apply to all the schemas available in the data source. Any related entities such as test suites or lineage information that were associated with those tables will also be deleted. Do not enable this option when you have multiple metadata ingestion pipelines. Also make sure to enable the markDeletedTables option for this to work.
• Auto Tag PII(toggle): Auto PII tagging checks for column name to mark PII Sensitive/NonSensitive tag

7. Schedule the Ingestion and Deploy

Scheduling can be set up at an hourly, daily, or weekly cadence. The timezone is in UTC. Select a Start Date to schedule for ingestion. It is optional to add an End Date.

Review your configuration settings. If they match what you intended, click Deploy to create the service and schedule metadata ingestion.

If something doesn't look right, click the Back button to return to the appropriate step and change the settings as needed.

After configuring the workflow, you can click on Deploy to create the pipeline.

8. View the Ingestion Pipeline

Once the workflow has been successfully deployed, you can view the Ingestion Pipeline running from the Service Page.

9. Workflow Deployment Error

If there were any errors during the workflow deployment process, the Ingestion Pipeline Entity will still be created, but no workflow will be present in the Ingestion container.

You can then edit the Ingestion Pipeline and Deploy it again.

From the Connection tab, you can also Edit the Service if needed.

Data Profiler

Data Quality