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MongoDB

MongoDB

PROD
Feature List
✓ Metadata
✓ Data Profiler
✓ Sample Data
✓ Auto-Classification
✕ Query Usage
✕ Data Quality
✕ dbt
✕ Owners
✕ Lineage
✕ Column-level Lineage
✕ Tags
✕ Stored Procedures
In this section, we provide guides and references to use the MongoDB connector. Configure and schedule MongoDB metadata workflows from the OpenMetadata UI:

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Requirements

To fetch the metadata from MongoDB to OpenMetadata, the MongoDB user must have access to perform find operation on collection and listCollection operations on database available in MongoDB.

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Python Requirements

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

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Metadata Ingestion

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

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1. Define the YAML Config

This is a sample config for MongoDB:

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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.

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Data Profiler

The Data Profiler workflow will be using the orm-profiler processor. After running a Metadata Ingestion workflow, we can run Data Profiler 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.

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Limitations

The MongodDB data profiler current supports only the following features:
  1. Row count: The number of rows in the collection. Sampling or custom query is not supported.
  2. Sample data: If a custom query is defined it will be used for sample data.

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1. Define the YAML Config

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

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2. Prepare the Profiler DAG

Here, we follow a similar approach as with the metadata and usage pipelines, although we will use a different Workflow class:

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dbt Integration

dbt Integration

Learn more about how to ingest dbt models