RRepoGEO

REPOGEO REPORT · LITE

datascale-ai/data_engineering_book

Default branch main · commit 44afaa56 · scanned 6/27/2026, 2:31:48 AM

GitHub: 1,221 stars · 110 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)

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

AI VISIBILITY SCORE
22 /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
1 / 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 datascale-ai/data_engineering_book, 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
  • highreadme#1
    Reposition README opening to clarify it's a book on LLM data engineering

    Why:

    CURRENT
    > **版本说明**：中文版是当前 2026 Springer 出版主线，结构冻结为 14 篇、48 章、15 个实战项目与 8 个附录（A–H）。英文版和日文版仍在跟进翻译，站点中会保留翻译状态说明页。
    COPY-PASTE FIX
    > **《大模型数据工程：架构、算法及项目实战》** 是一本全面深入的开源书籍，旨在系统性地解决大模型时代的数据质量挑战。本书作为 2026 Springer 出版主线，结构冻结为 14 篇、48 章、15 个实战项目与 8 个附录（A–H）。英文版和日文版仍在跟进翻译，站点中会保留翻译状态说明页。
  • mediumabout#2
    Enhance 'About' description to explicitly state it's a book/guide

    Why:

    CURRENT
    大模型数据工程：架构、算法及项目实战
    COPY-PASTE FIX
    一本关于大模型数据工程的全面指南：涵盖架构、算法及项目实战。

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 datascale-ai/data_engineering_book
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Databricks Lakehouse Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Databricks Lakehouse Platform · recommended 1×
  2. Snowflake · recommended 1×
  3. Google Cloud Dataflow · recommended 1×
  4. Google BigQuery · recommended 1×
  5. Google Cloud Storage · recommended 1×
  • CATEGORY QUERY
    How to build robust data engineering pipelines for large language models?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Snowflake
    3. Google Cloud Dataflow
    4. Google BigQuery
    5. Google Cloud Storage
    6. Vertex AI
    7. AWS Glue
    8. Amazon S3
    9. Amazon Redshift
    10. Amazon EMR
    11. Amazon SageMaker
    12. Apache Airflow
    13. Prefect
    14. dbt

    AI recommended 14 alternatives but never named datascale-ai/data_engineering_book. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking comprehensive guide for LLM data preparation, from raw data to deployment.
    you: not recommended
    AI recommended (in order):
    1. Google Cloud AI Platform / Vertex AI
    2. Hugging Face Datasets Library
    3. fast.ai Course
    4. Snorkel AI
    5. Cleanlab
    6. Label Studio
    7. Weights & Biases (W&B)

    AI recommended 7 alternatives but never named datascale-ai/data_engineering_book. 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 datascale-ai/data_engineering_book?
    pass
    AI did not name datascale-ai/data_engineering_book — 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?

  • If a team adopts datascale-ai/data_engineering_book in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datascale-ai/data_engineering_book 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 datascale-ai/data_engineering_book solve, and who is the primary audience?
    pass
    AI did not name datascale-ai/data_engineering_book — 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?

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