RRepoGEO

REPOGEO REPORT · LITE

pracdata/awesome-open-source-data-engineering

Default branch main · commit 5737495b · scanned 6/7/2026, 4:23:03 PM

GitHub: 566 stars · 72 forks

AI VISIBILITY SCORE
28 /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
2 / 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 pracdata/awesome-open-source-data-engineering, 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
  • highreadme#1
    Reposition the README's opening statement to emphasize its role as a guide

    Why:

    CURRENT
    A curated list of open source tools used in analytics platforms and data engineering ecosystem
    COPY-PASTE FIX
    Your essential guide to open source tools for building and managing modern analytics platforms and data engineering ecosystems.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://pracdata.io

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 pracdata/awesome-open-source-data-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Apache Kafka
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Apache Kafka · recommended 1×
  2. Apache Spark · recommended 1×
  3. Delta Lake · recommended 1×
  4. Apache Airflow · recommended 1×
  5. Trino · recommended 1×
  • CATEGORY QUERY
    What are the essential open source tools for building a modern data platform?
    you: not recommended
    AI recommended (in order):
    1. Apache Kafka
    2. Apache Spark
    3. Delta Lake
    4. Apache Airflow
    5. Trino
    6. Apache Flink
    7. Prometheus
    8. Grafana

    AI recommended 8 alternatives but never named pracdata/awesome-open-source-data-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking open source components to build a scalable, self-hosted data lakehouse solution.
    you: not recommended
    AI recommended (in order):
    1. Apache Iceberg (apache/iceberg)
    2. Delta Lake (delta-io/delta)
    3. Apache Spark (apache/spark)
    4. MinIO (minio/minio)
    5. Apache HDFS (apache/hadoop)
    6. Apache Flink (apache/flink)
    7. Apache Kafka (apache/kafka)
    8. Trino (trinodb/trino)
    9. Apache Hive (apache/hive)
    10. Apache Superset (apache/superset)

    AI recommended 10 alternatives but never named pracdata/awesome-open-source-data-engineering. 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 pracdata/awesome-open-source-data-engineering?
    pass
    AI named pracdata/awesome-open-source-data-engineering explicitly

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

  • If a team adopts pracdata/awesome-open-source-data-engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pracdata/awesome-open-source-data-engineering 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 pracdata/awesome-open-source-data-engineering solve, and who is the primary audience?
    pass
    AI did not name pracdata/awesome-open-source-data-engineering — likely talking about a different project

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

Embed your GEO score

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MARKDOWN (README)
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pracdata/awesome-open-source-data-engineering — 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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