Data Engineering Book
An open source, community-driven guide
Details
- External ID
- 47008163
- Source
- HN
- Company
- —
- Product
- Data Engineering Book
- Website domain
- github.com
- Launched
- Feb. 13, 2026
- Cohort
- —
- Upvotes
- 251
- Upvotes percentile
- 0.9649595687331537
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
Hi HN! I'm currently a Master's student at USTC (University of Science and Technology of China). I've been diving deep into Data Engineering, especially in the context of Large Language Models (LLMs).The Problem: I found that learning resources for modern data engineering are often fragmented and scattered across hundreds of medium articles or disjointed tutorials. It's hard to piece everything together into a coherent system.The Solution: I decided to open-source my learning notes and build them into a structured book. My goal is to help developers fast-track their learning curve.Key Features:LLM-Centric: Focuses on data pipelines specifically designed for LLM training and RAG systems.Scenario-Based: Instead of just listing tools, I compare different methods/architectures based on specific business scenarios (e.g., "When to use Vector DB vs. Keyword Search").Hands-on Projects: Includes full code for real-world implementations, not just "Hello World" examples.This is a work in progress, and I'm treating it as "Book-as-Code". I would love to hear your feedback on the roadmap or any "anti-patterns" I might have included!Check it out:Online: https://datascale-ai.github.io/data_engineering_book/GitHub: https://github.com/datascale-ai/data_engineering_book
Enrichment
- Theme
- ML inference and model optimization
- Vertical
- Education
- Function
- Content generation
- Audience
- Developer
- AI stance
- Not AI
- Project type
- Hobby / open-source project
- Normalized one-liner
- open source data engineering guide
- Manually corrected
- False
Could you build this?
Yes This is an open-source educational text and markdown guide/book hosted on GitHub, requiring no complex technical infrastructure.
Discussion
20 comments analyzed.
Competitors mentioned: Lance (columnar storage format for ML lifecycle), Vortex (storage format), Nimble (Meta's storage format), Delta (table format), Iceberg (table format)
Concerns raised: Lacks coverage of storage formats purpose-built for ML lifecycle, No focus on code-specific modalities in data tools, Missing pre-tagged, pre-categorized datasets for code training, SGlang unavailable on MacOS for constrained generation, Unclear line between vector DB and keyword search for RAG
Feature requests: Cover hybrid search patterns and re-ranking for production RAG, Include emerging storage formats (Lance, Vortex, Nimble, Delta, Iceberg), Add code-specific datasets with curriculum filtering, Cover EBNF-constrained synthetic data generation for code
Competitors
Other products that read as similar to this one — 176 launches clear the similarity bar, closest 8 shown.
Attention rank: #11 of 177 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 105 days after the earliest competitor.
- book-data-structures-ai · github · 2026-09-13 · 15 upvotes · similarity 0.48
- A better LLM-wiki with multi-path research [550 stars] · hn · 2026-06-11 · 5 upvotes · similarity 0.47
- Extrai · hn · 2025-11-03 · 5 upvotes · similarity 0.45
- LLM Wiki · hn · 2026-04-06 · 6 upvotes · similarity 0.45
- Llm-language-learning · github · 2026-09-14 · 14 upvotes · similarity 0.43
- Project AELLA · hn · 2025-11-11 · 6 upvotes · similarity 0.43
- Maths, CS and AI Compendium · hn · 2026-02-16 · 88 upvotes · similarity 0.43
- datamodellib · ph · 2026-09-30 · 1 upvotes · similarity 0.42
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a content generation tool for Government yet.