Building Full-Chain Data Lineage for E‑commerce Scenarios

The presentation introduces the concept of full‑chain data lineage in e‑commerce, aiming to trace and manage data from source to endpoint.

Main sections:

Data full‑link lineage overview.

How to build a lineage foundation.

Application practice of lineage in e‑commerce.

Summary and outlook.

It describes typical data flow in retail (product data, logistics, user feedback) and outlines challenges such as data explosion, warehouse change monitoring, and development efficiency, highlighting how lineage helps evaluate data value, control resource growth, and ensure consistency.

The solution addresses four key problems: controlling data bloat, improving warehouse change monitoring, enhancing development efficiency, and supporting metric system construction, each with specific lineage‑based approaches.

To build the lineage foundation, the article details the overall architecture (graph database with nodes and edges), quality measurement metrics (accuracy, success rate, coverage, query capability), and application‑level lineage that connects front‑end requests to back‑end data services.

Practical e‑commerce applications include automated table migration (generating and comparing SQL automatically), field‑level lineage exploration (visualizing SQL as graphs), and automated metric decomposition (linking fields to metric definitions, reducing duplicate work). Technical steps involve SQL parsing, syntax‑sugar dissolution, and operator‑level rewriting.

Finally, the summary emphasizes that a robust data lineage foundation improves data management efficiency and quality, and future work will integrate large‑model capabilities to further enhance functionality across scenarios like table migration, warehouse evaluation, and metric decomposition.