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ADLS Datalake

ADLS Datalake

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

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Requirements

Note: ADLS Datalake connector supports extracting metadata from file types JSON, CSV, TSV & Parquet.

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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 Reader
  • Storage Queue Data Reader

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Python Requirements

We have support for Python versions 3.9-3.11

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Azure installation

pip3 install "openmetadata-ingestion[datalake-azure]"

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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.

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1. Define the YAML Config

This is a sample config for Datalake using Azure:

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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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dbt Integration

You can learn more about how to ingest dbt models’ definitions and their lineage here.