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data-lineage

Construct and visualize column-level data lineage graphs for Java/Spring and OpenFlow SQL projects.

2|Updated Apr 12, 2026
Search Tags:#data lineage#data flow analysis#data traceability#Java/Spring#OpenFlow SQL
One-click install
npx skills add https://github.com/never-summer/open-table-migrator --skill data-lineage-never-summer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-lineage
Source: https://github.com/never-summer/open-table-migrator/tree/main/skills/data_lineage
Command: npx skills add https://github.com/never-summer/open-table-migrator --skill data-lineage-never-summer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tree_sitter, sqlglot, data_lineage_extractors, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill automatically constructs detailed data lineage for projects, mapping the flow of data between sources and sinks, and revealing how data is transformed across different stages.

Core Features & Use Cases

  • Column-Level Lineage: Trace the origin and destination of every column in a project.
  • Java/Spring Support: Detects and analyzes data flows in Java/Spring projects using jOOQ, JdbcTemplate, Spring Data, Spring Kafka, and Spring Web.
  • OpenFlow SQL Support: Analyzes Hive/Spark-flavored ETL projects with ctl.yml templating and JSON workflow configurations.
  • Use Case: Use this Skill to understand how data moves through a complex data pipeline, identify data quality issues, and optimize data processing workflows.

Quick Start

To generate a data lineage report for a Java/Spring project, run the following command:

python3 -m skills.data_lineage <path_to_project>

For OpenFlow SQL projects, ensure that you have the appropriate configuration files (ctl.yml and conf/*.json) in place.

Frequently Asked Questions about data-lineage

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build column-level data lineage for a Java/Spring project?▼

You can build column-level data lineage by running a Python script that parses your Java/Spring project to track data transformations and dependencies across different data lifecycle stages.

What is column-level data traceability in ETL pipelines?▼

Column-level data traceability tracks the origin and destination of every data column across sources, sinks, and transformations, revealing how data changes throughout the pipeline lifecycle.

Does this data flow analysis tool support OpenFlow SQL and Hive projects?▼

Yes, the data flow analysis supports OpenFlow SQL projects by analyzing Hive/Spark-flavored ETL configurations using ctl.yml templating and JSON workflow files to map dependencies.

Can I trace data flow in Java projects using jOOQ and Spring Kafka?▼

Yes, you can trace data flow in Java projects using jOOQ, JdbcTemplate, Spring Data, Spring Kafka, and Spring Web, as the extractor detects and analyzes data flows across these frameworks.

Do I need Python and sqlglot to analyze data lineage?▼

Yes, you need Python with dependencies including tree-sitter, sqlglot, and data lineage extractors to parse project code and construct data lineage graphs.

What are the limitations of automated data lineage extraction?▼

Automated data lineage extraction is limited to Java/Spring and OpenFlow SQL projects, requiring specific configuration files like ctl.yml and conf/*.json for Hive/Spark ETL analysis to function correctly.

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