GitHub user Nicoleee1108 added a comment to the discussion: Task ideas for the
dkNet-AI · Apache Texera Agent Hackathon
Row-Level Data Lineage: "Why Is This Row Here?"
Categories: Data Experience · Innovation
The problem
Every data analyst, at some point, stares at a number in a result table and
asks: why is this number what it is? Which input rows produced it? Was it
inflated by a duplicate join key? Skewed by one outlier? In SQL or Pandas, this
question is essentially unanswerable after the fact — once you've aggregated,
the input rows are gone, and you have to re-run with manual instrumentation to
find out.
The idea
Right-click any output row in Texera's result panel → click "Why?" → the
canvas dims, then visually traces backwards through every operator,
highlighting the upstream tuples that contributed to that row. Click any
intermediate operator and the result panel shows that operator's contributing
rows. The workflow explains itself.
A region = West, total = $1.2M row becomes a story you can audit:
- 1,847 rows survived the Filter
- Came from 1,847 Join outputs
- Sourced from 864 customer rows and 1,847 order rows in the CSV scans
GitHub link:
https://github.com/apache/texera/discussions/5059#discussioncomment-16924906
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