RRepoGEO

REPOGEO REPORT · LITE

apache/griffin

Default branch master · commit e293406f · scanned 6/18/2026, 9:47:55 PM

GitHub: 1,170 stars · 585 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface apache/griffin, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Update the repository description for clarity and keywords

    Why:

    CURRENT
    Mirror of Apache griffin
    COPY-PASTE FIX
    Apache Griffin is an open-source, model-driven data quality service platform for defining, measuring, and monitoring data quality across diverse big data systems like Spark and Hadoop.
  • lowreadme#2
    Complete the dev-list email address in README

    Why:

    CURRENT
    - dev-list: <
    COPY-PASTE FIX
    - dev-list: <dev@griffin.apache.org>

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface apache/griffin
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Collibra Data Quality & Observability
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Collibra Data Quality & Observability · recommended 2×
  2. Informatica Data Quality (IDQ) · recommended 2×
  3. Talend Data Quality · recommended 2×
  4. Ataccama ONE · recommended 2×
  5. Datameer · recommended 1×
  • CATEGORY QUERY
    How to monitor and ensure data quality across disparate data sources and systems?
    you: not recommended
    AI recommended (in order):
    1. Collibra Data Quality & Observability
    2. Informatica Data Quality (IDQ)
    3. Talend Data Quality
    4. Ataccama ONE
    5. Datameer
    6. Great Expectations (great-expectations/great_expectations)
    7. Monte Carlo

    AI recommended 7 alternatives but never named apache/griffin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good tools for defining data quality metrics and generating detailed reports?
    you: not recommended
    AI recommended (in order):
    1. Collibra Data Quality & Observability
    2. Ataccama ONE
    3. Informatica Data Quality (IDQ)
    4. Talend Data Quality
    5. Great Expectations
    6. Soda Core
    7. Soda Cloud

    AI recommended 7 alternatives but never named apache/griffin. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of apache/griffin?
    pass
    AI named apache/griffin explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts apache/griffin in production, what risks or prerequisites should they evaluate first?
    pass
    AI named apache/griffin explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo apache/griffin solve, and who is the primary audience?
    pass
    AI named apache/griffin explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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apache/griffin — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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