Datalake

Feature	Status
Stage	PROD
Metadata
Query Usage
Data Profiler
Data Quality
Lineage
DBT
Supported Versions	--

Feature	Status
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:

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.

Ingest with Airflow

Configure the ingestion using Airflow SDK

Ingest with the CLI

Run a one-time ingestion using the metadata CLI

Requirements

OpenMetadata 0.12 or later

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.

Note: 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 <strong>Bucket Policy</strong> in AWS requires at least these permissions:

ADLS Permissions

To extract metadata from Azure ADLS (Storage Account - StorageV2), you will need an App Registration with the following permissions on the Storage Account:

Storage Blob Data Contributor
Storage Queue Data Contributor

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.

Find Databases option on left panel of the settings page

2. Create a New Service

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

Add a new Service from the Database Services page

3. Select the Service Type

Select Datalake as the service type and click Next.

Select your service from the list

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.

Provide a Name and description for your Service

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.

Configure the service connection by filling the form

Connection Options

S3 Permissions

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

5.1 Datalake using AWS S3

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.

5.2 Datalake using GCS

BUCKET NAME

A bucket name in DataLake is a unique identifier used to organize and store data objects. It's similar to a folder name, but it's used for object storage rather than file storage.

PREFIX

The prefix of a data source in datalake refers to the first part of the data path that identifies the source or origin of the data. It's used to organize and categorize data within the datalake, and can help users easily locate and access the data they need.

GCS Credentials

We support two ways of authenticating to GCS:

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

5.3 Datalake using Azure

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. Test the Connection

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

Test the connection and save the Service

7. Configure Metadata Ingestion

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

Configure Metadata Ingestion Page

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 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): Set the Mark Deleted Tables toggle to flag tables as soft-deleted if they are not present anymore in the source system.
Mark Deleted Tables from Filter Only (toggle): Set the Mark Deleted Tables from Filter Only toggle to flag tables as soft-deleted if they are not present anymore within the filtered schema or database only. This flag is useful when you have more than one ingestion pipelines. For example if you have a schema

8. Schedule the Ingestion and Deploy

Scheduling can be set up at an hourly, daily, weekly, or manual 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.

Schedule the Ingestion Pipeline and Deploy

9. View the Ingestion Pipeline

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

View the Ingestion Pipeline from the Service Page

Troubleshooting

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.