Skip to main content
Dremio

Dremio

BETA
In this section, we provide guides and references to use the Dremio connector. Configure and schedule Dremio metadata workflows from the OpenMetadata UI:

​
How to Run the Connector Externally

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. If, instead, you want to manage your workflows externally on your preferred orchestrator, you can check the following docs to run the Ingestion Framework anywhere.

External Schedulers

Get more information about running the Ingestion Framework Externally

​
Requirements

Dremio connector supports both Dremio Cloud (SaaS) and Dremio Software (self-hosted). Make sure you have the Dremio Cloud region, personal access token, and project ID, or the Dremio Software host, username, and password.

​
Python Requirements

We have support for Python versions 3.9-3.11
To run the Dremio ingestion, you will need to install: 
pip3 install "openmetadata-ingestion[dremio]"

​
Metadata Ingestion

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

​
2. Run with the CLI

First, we will need to save the YAML file. Afterward, and with all requirements installed, we can run: 
metadata ingest -c <path-to-yaml>
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.

​
Query Usage

The Query Usage workflow will be using the query-parser processor. After running a Metadata Ingestion workflow, we can run Query Usage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

​
1. Define the YAML Config

This is a sample config for Usage:

​
2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata usage -c <path-to-yaml>

​
Lineage

After running a Metadata Ingestion workflow, we can run Lineage workflow. While the serviceName will be the same to that was used in Metadata Ingestion, so the ingestion bot can get the serviceConnection details from the server.

​
1. Define the YAML Config

This is a sample config for Lineage:
  • You can learn more about how to configure and run the Lineage Workflow to extract Lineage data from here

​
2. Run with the CLI

After saving the YAML config, we will run the command the same way we did for the metadata ingestion: 
metadata ingest -c <path-to-yaml>