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S3 Datalake

S3 Datalake

PROD
In this section, we provide guides and references to use the S3 Datalake connector. Configure and schedule S3 Datalake metadata and profiler workflows from the OpenMetadata UI:

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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

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Requirements

Note: S3 Datalake connector supports extracting metadata from file types JSON, CSV, TSV & Parquet.

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S3 Permissions

To execute metadata extraction AWS account should have enough access to fetch required data. The Bucket Policy in AWS requires at least these permissions: 
{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:GetObject",
                "s3:ListBucket"
            ],
            "Resource": [
                "arn:aws:s3:::<my bucket>",
                "arn:aws:s3:::<my bucket>/*"
            ]
        }
    ]
}

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Python Requirements

We have support for Python versions 3.9-3.11
If running OpenMetadata version greater than 0.13, you will need to install the Datalake ingestion for S3:

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S3 installation

pip3 install "openmetadata-ingestion[datalake-s3]"

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If version <0.13

You will be installing the requirements for S3 
pip3 install "openmetadata-ingestion[datalake]"

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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

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Source Configuration - Source Config using AWS S3

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This is a sample config for Datalake using AWS S3:

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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.