RRepoGEO

REPOGEO REPORT · LITE

datascale-ai/data_engineering_book

Default branch main · commit 22b701a6 · scanned 5/16/2026, 7:02:20 AM

GitHub: 1,157 stars · 94 forks

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

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

OVERALL DIRECTION
  • hightopics#1
    Add specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    large-language-models, llm-data-engineering, data-engineering, rag, multimodal-data, dataops, ai-book, machine-learning-engineering, data-quality, synthetic-data, pretraining-data, alignment-data
  • highreadme#2
    Clarify the README's opening statement to emphasize it's a book/resource

    Why:

    CURRENT
    The `## 简介` section starts with a quote: `> "Data is the new oil, but only if you know how to refine it."`
    COPY-PASTE FIX
    Replace the opening quote in the `## 简介` section with a direct statement: `本书是首部系统性讲解大模型数据工程的开源书籍，涵盖架构、算法及项目实战，旨在帮助读者构建高质量LLM数据流水线。` (This book is the first systematic open-source book on large model data engineering, covering architecture, algorithms, and practical projects, aiming to help readers build high-quality LLM data pipelines.)
  • mediumreadme#3
    Ensure the unique value proposition is immediately clear

    Why:

    CURRENT
    The "版本说明" (Version Notes) section appears immediately after the language links and before the "简介" section.
    COPY-PASTE FIX
    Move the "版本说明" section to appear *after* the entire "简介" section, ensuring the core purpose and content description are presented immediately after the title and before any version details.

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. apache/spark · recommended 1×
  3. delta-io/delta · recommended 1×
  4. mlflow/mlflow · recommended 1×
  5. Unity Catalog · recommended 1×
  • CATEGORY QUERY
    How to build robust data engineering pipelines for large language model pre-training and RAG?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Apache Spark (apache/spark)
    3. Delta Lake (delta-io/delta)
    4. MLflow (mlflow/mlflow)
    5. Unity Catalog
    6. Apache Flink (apache/flink)
    7. Apache Kafka (apache/kafka)
    8. Apache Iceberg (apache/iceberg)
    9. Google Cloud Platform
    10. Google Cloud Dataflow
    11. Apache Beam (apache/beam)
    12. BigQuery
    13. Cloud Storage
    14. Google AI Platform
    15. Vertex AI
    16. AWS
    17. AWS Glue
    18. Amazon S3
    19. Amazon OpenSearch Service
    20. Amazon Redshift
    21. AWS Lambda
    22. Amazon Kinesis
    23. Apache Airflow (apache/airflow)
    24. MinIO (minio/minio)
    25. Azure Data Lake Storage (ADLS)
    26. Prefect (PrefectHQ/prefect)
    27. Dagster (dagster-io/dagster)
    28. Polars (ritchie46/polars)
    29. Pandas (pandas-dev/pandas)
    30. Pinecone
    31. Weaviate (weaviate/weaviate)
    32. Qdrant (qdrant/qdrant)

    AI recommended 32 alternatives but never named datascale-ai/data_engineering_book. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are best practices for improving large language model performance through advanced data engineering?
    you: not recommended
    AI recommended (in order):
    1. Apache Spark
    2. Dask
    3. Pandas
    4. Great Expectations
    5. Pydantic
    6. Hugging Face Transformers
    7. NLPAug
    8. OpenAI API
    9. Snorkel
    10. Scikit-learn
    11. NumPy
    12. PyTorch
    13. TensorFlow
    14. SpaCy
    15. NLTK
    16. DVC (Data Version Control)
    17. MLflow
    18. Weights & Biases (W&B)
    19. Argilla
    20. Label Studio
    21. Ray
    22. Hugging Face Accelerate
    23. DeepSpeed

    AI recommended 23 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?

Embed your GEO score

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