RRepoGEO

REPOGEO REPORT · LITE

apache/griffin

Default branch master · commit e293406f · scanned 5/9/2026, 2:53:20 AM

GitHub: 1,170 stars · 586 forks

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

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

OVERALL DIRECTION
  • hightopics#1
    Add specific data quality and big data topics

    Why:

    CURRENT
    griffin
    COPY-PASTE FIX
    griffin, data-quality, data-governance, big-data, spark, hadoop, data-monitoring, data-validation, etl
  • highhomepage#2
    Add the official project homepage URL

    Why:

    COPY-PASTE FIX
    https://griffin.apache.org/
  • mediumreadme#3
    Refine README opening to explicitly state open-source nature and core category

    Why:

    CURRENT
    # Apache Griffin  
[](https://travis-ci.org/apache/griffin) [](https://www.apache.org/licenses/LICENSE-2.0.html)    

The data quality (DQ) is a key criteria for many data consumers like IoT, machine learning etc., however, there is no standard agreement on how to determine “good” data.
Apache Griffin is a model-driven data quality service platform where you can examine your data on-demand. It provides a standard process to define data quality measures, executions and reports, allowing those examinations across multiple data systems.
    COPY-PASTE FIX
    # Apache Griffin

Apache Griffin is an open-source, model-driven data quality (DQ) service platform designed to help data engineers and data governance teams ensure high data quality across various data systems. It provides a standard process to define data quality measures, executions, and reports, allowing on-demand data examination.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Collibra Data Quality & Observability · recommended 1×
  2. Informatica Data Quality (IDQ) · recommended 1×
  3. Talend Data Quality · recommended 1×
  4. Ataccama ONE · recommended 1×
  5. great-expectations/great_expectations · recommended 1×
  • CATEGORY QUERY
    How can I ensure data quality and integrity across various data systems effectively?
    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. Great Expectations (great-expectations/great_expectations)
    6. Monte Carlo
    7. dbt (data build tool) (dbt-labs/dbt-core)
    8. dbt-expectations (calogica/dbt-expectations)

    AI recommended 8 alternatives but never named apache/griffin. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open source tools help define and monitor data quality metrics with detailed reports?
    you: not recommended
    AI recommended (in order):
    1. Great Expectations
    2. Deequ
    3. Soda Core
    4. OpenMetadata
    5. Apache Griffin
    6. Pandarallel
    7. Jinja2

    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?

Embed your GEO score

Drop this badge into the README of apache/griffin. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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  • Brand-free category queries5 vs 2 in Lite
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