Inaccurate data dependencies

Oct 13, 2025 by pynkocean.com 81 views

Introduction

Dag-factory is a powerful tool for building and managing data pipelines. However, it currently has a limitation that prevents it from fully integrating with lineage tracking services such as DataHub, OpenLineage, and other metadata-driven tools. This limitation is the lack of built-in support for inlets. In this article, we will explore the need for inlets in dag-factory, discuss the use cases and motivations, and provide a detailed explanation of the related issues.

Why is this Needed?

Airflow 2.4.0+ introduced Dataset-based scheduling, which revolutionized the way data pipelines are managed. This new feature relies heavily on both inlets and outlets to track data dependencies. Inlets are used to define the input data sources, while outlets are used to define the output data destinations. Many data lineage tools, such as DataHub, Marquez, and OpenLineage, rely on both inlets and outlets to correctly infer dataset relationships.

Without inlets, lineage propagation in Airflow does not fully capture data source dependencies, resulting in incomplete lineage graphs. This is a significant limitation, as it prevents data engineers and scientists from accurately tracking the flow of data through their pipelines. By supporting inlets in dag-factory, we can ensure that data lineage is properly tracked, and that data dependencies are accurately captured.

Use Case/Motivation

The use case for supporting inlets in dag-factory is straightforward. Data engineers and scientists need to be able to track the flow of data through their pipelines, and inlets are a critical component of this process. By supporting inlets, dag-factory can provide a more complete and accurate picture of data lineage, which is essential for ensuring data quality and integrity.

Related Issues

There are several related issues that are relevant to this feature request. Some of the key issues include:

Incomplete lineage graphs: Without inlets, lineage propagation in Airflow does not fully capture data source dependencies, resulting in incomplete lineage graphs.
Inaccurate data dependencies: Without inlets, data engineers and scientists may not be able to accurately track the flow of data through their pipelines, leading to inaccurate data dependencies.
Limited integration with lineage tools: Without inlets, dag-factory may not be able to fully integrate with lineage tools such as DataHub, OpenLineage, and other metadata-driven tools.

Are You Willing to Submit a PR?

Yes, I am willing to submit a PR!

Implementation Plan

To implement this feature, we will need to make the following changes to the dag-factory codebase:

Add inlet support: We will need to add support for inlets to the dag-factory codebase. This will involve modifying the existing outlet code to include inlet support.
Update lineage propagation: We will need to update the lineage propagation code to include inlet support. This will involve modifying the existing code to accurately capture data source dependencies.
Integrate with lineage tools: We will need to integrate the updated dag-factory codebase with lineage tools such as DataHub, OpenLineage, and other metadata-driven tools.

Timeline

The timeline for implementing this feature is as follows:

Week 1-2: Add inlet support to the dag-factory codebase.
Week 3-4: Update lineage propagation code to include inlet support.
Week 5-6: Integrate updated dag-factory codebase with lineage tools.

Resources

The resources required to implement this feature include:

Development time: 6 weeks
Development team: 1-2 developers
Testing and QA: 2 weeks
Documentation: 1 week

Risks and Assumptions

The risks and assumptions associated with this feature include:

Complexity: The implementation of this feature may be complex, and may require significant changes to the existing codebase.
Integration: The integration of the updated dag-factory codebase with lineage tools may be challenging, and may require significant testing and QA.
Timeline: The timeline for implementing this feature may be aggressive, and may require significant resources and effort.

Conclusion

Introduction

In our previous article, we discussed the need for supporting inlets in dag-factory. In this article, we will answer some of the most frequently asked questions about this feature.

Q: What are inlets, and why are they important?

A: Inlets are a critical component of data pipelines, and they play a key role in tracking data dependencies. Inlets define the input data sources, while outlets define the output data destinations. Without inlets, lineage propagation in Airflow does not fully capture data source dependencies, resulting in incomplete lineage graphs.

Q: How will supporting inlets in dag-factory improve data lineage?

A: By supporting inlets, dag-factory can provide a more complete and accurate picture of data lineage. This is essential for ensuring data quality and integrity, and it will enable data engineers and scientists to accurately track the flow of data through their pipelines.

Q: What are the benefits of supporting inlets in dag-factory?

A: The benefits of supporting inlets in dag-factory include:

Improved data lineage: By supporting inlets, dag-factory can provide a more complete and accurate picture of data lineage.
Increased data quality: By accurately tracking data dependencies, data engineers and scientists can ensure that their data is accurate and reliable.
Better integration with lineage tools: By supporting inlets, dag-factory can integrate more seamlessly with lineage tools such as DataHub, OpenLineage, and other metadata-driven tools.

Q: How will supporting inlets in dag-factory affect existing pipelines?

A: Supporting inlets in dag-factory will not affect existing pipelines. Existing pipelines will continue to work as they do today, and they will not require any changes to support inlets.

Q: What is the timeline for implementing this feature?

A: The timeline for implementing this feature is as follows:

Week 1-2: Add inlet support to the dag-factory codebase.
Week 3-4: Update lineage propagation code to include inlet support.
Week 5-6: Integrate updated dag-factory codebase with lineage tools.

Q: What resources will be required to implement this feature?

A: The resources required to implement this feature include:

Development time: 6 weeks
Development team: 1-2 developers
Testing and QA: 2 weeks
Documentation: 1 week

Q: What are the risks and assumptions associated with this feature?

A: The risks and assumptions associated with this feature include:

Complexity: The implementation of this feature may be complex, and may require significant changes to the existing codebase.
Integration: The integration of the updated dag-factory codebase with lineage tools may be challenging, and may require significant testing and QA.
Timeline: The timeline for implementing this feature may be aggressive, and may require significant resources and effort.

Q: How can I get involved in implementing this feature?

A: If you are interested in getting involved in implementing this feature, please contact us at [insert contact information]. We are always looking for developers who are interested in contributing to the dag-factory project.

Conclusion

In conclusion, supporting inlets in dag-factory is a critical feature that is essential for ensuring data quality and integrity. By supporting inlets, dag-factory can provide a more complete and accurate picture of data lineage, which is essential for ensuring data quality and integrity. We believe that this feature is essential for the success of dag-factory, and we are willing to submit a PR to implement it.