Creating Lineage from S3 Stored Procedure Logic and DataHub SDK Usage

ingestion
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The make_data_job_urn method is used to create a unique Uniform Resource Name (URN) for a data job in DataHub. This URN is a standardized way to identify and reference data jobs within the DataHub ecosystem. The method constructs the URN using several key parameters that describe the data job’s context and identity.

Parameters:

  1. orchestrator:

    • This parameter specifies the orchestration platform or tool that manages the data job. Common examples include “airflow”, “azkaban”, or “luigi”. It helps identify the system responsible for executing the data job.

  2. flow_id:

    • The flow_id represents the identifier of the data flow or workflow to which the data job belongs. In orchestration systems like Airflow, this might correspond to a DAG (Directed Acyclic Graph) ID. It helps group related jobs under a single workflow.

  3. job_id:

    • The job_id is a unique identifier for the specific data job within the flow. It distinguishes one job from another within the same flow. In Airflow, this might be the task ID.

  4. cluster:

    • The cluster parameter indicates the environment or cluster where the data job is executed. This could be “prod” for production, “dev” for development, or any other environment designation. It helps differentiate between different deployment environments.

Example Usage:

Here’s an example of how the make_data_job_urn method might be used:

from datahub.emitter.mce_builder import make_data_job_urn

# Example parameters
orchestrator = "airflow"
flow_id = "example_flow"
job_id = "example_job"
cluster = "prod"

# Create the data job URN
data_job_urn = make_data_job_urn(orchestrator, flow_id, job_id, cluster)
print(data_job_urn)

This would produce a URN like:

urn:li:dataJob:(urn:li:dataFlow:(airflow,example_flow,prod),example_job)

Purpose:

The URN uniquely identifies the data job within DataHub, allowing for consistent referencing and management of metadata related to the job. It is essential for establishing lineage, tracking job execution, and integrating with other metadata entities in DataHub.

If you need more detailed information or further assistance, feel free to ask!

Sources:

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<@U06TM7M40RK> what could be the values in orchestrator?

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It seems likely that the orchestrator parameter in the make_data_job_urn method represents the orchestration platform or tool that manages the execution of data jobs. Common values for the orchestrator parameter could include:

  1. Airflow: A popular open-source workflow management platform used to programmatically author, schedule, and monitor workflows.

  2. Azkaban: A batch workflow job scheduler created at LinkedIn to run Hadoop jobs.

  3. Luigi: A Python module that helps you build complex pipelines of batch jobs.

  4. Prefect: A modern workflow orchestration tool that allows you to build, run, and monitor data pipelines.

  5. Dagster: An orchestration platform for the development, production, and observation of data assets.

  6. Kubernetes: If using Kubernetes to orchestrate jobs directly, this could be specified as the orchestrator.

  7. Custom Orchestrators: Any custom or in-house orchestration tools used within an organization.

The choice of orchestrator value depends on the specific tool or platform being used to manage the data jobs. It helps in identifying the system responsible for executing the job and can be used to integrate with the specific features or metadata of that orchestration platform.

If you need more detailed information or further assistance, feel free to ask!

Sources:

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Please tag @ RunLLM if you still need further assistance !

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Hope this helps ! ><