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Home/Connectors/Database/Datalake/Cli

Run Datalake using the metadata CLI

FeatureStatus
StagePROD
Metadata
Query Usage
Data Profiler
Data Quality
Lineage
DBT
Supported Versions--
FeatureStatus
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

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

Python Requirements

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

S3 installation

GCS installation

Azure installation

If version <0.13

You will be installing the requirements together for S3 and GCS

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:

Source Configuration - Service Connection

  • 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 tableFilternPattern: Note that the schemaFilterPattern and tableFilterPattern both support regex as include or exclude. E.g.,

Source Configuration - Source Config

The sourceConfig is defined here:

markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.

includeTables: true or false, to ingest table data. Default is true.

includeViews: true or false, to ingest views definitions.

databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here

Sink Configuration

To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.

Workflow Configuration

The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.

For a simple, local installation using our docker containers, this looks like:

filename.yaml
This is a sample config for Datalake using GCS:
Source Configuration - Service Connection
Source Configuration - Source Config
The sourceConfig is defined here:
markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
includeTables: true or false, to ingest table data. Default is true.
includeViews: true or false, to ingest views definitions.
databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here
Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.
Workflow Configuration
The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
filename.yaml
This is a sample config for Datalake using Azure:
Source Configuration - Service Connection
  • 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
Source Configuration - Source Config
The sourceConfig is defined here:
markDeletedTables: To flag tables as soft-deleted if they are not present anymore in the source system.
includeTables: true or false, to ingest table data. Default is true.
includeViews: true or false, to ingest views definitions.
databaseFilterPattern, schemaFilterPattern, tableFilternPattern: Note that the filter supports regex as include or exclude. You can find examples here
Sink Configuration
To send the metadata to OpenMetadata, it needs to be specified as type: metadata-rest.
Workflow Configuration
The main property here is the openMetadataServerConfig, where you can define the host and security provider of your OpenMetadata installation.
For a simple, local installation using our docker containers, this looks like:
filename.yaml
Workflow Configs for Security Provider
We support different security providers. You can find their definitions here.
Openmetadata JWT Auth
  • JWT tokens will allow your clients to authenticate against the OpenMetadata server. To enable JWT Tokens, you will get more details here.
  • You can refer to the JWT Troubleshooting section link for any issues in your JWT configuration. If you need information on configuring the ingestion with other security providers in your bots, you can follow this doc link.
2. Run with the CLI
First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run:
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.
dbt Integration
You can learn more about how to ingest dbt models' definitions and their lineage here.