I built a Rust-powered data engine that hit GitHub’s global Rust trending by nailing three things at once—picking the right language for hard data problems, telling a compelling story in the README, and solving a pain that a lot of enterprises quietly suffer from every week.

Why Rust shines for data transformation

Rust fits CocoIndex https://github.com/cocoindex-io/cocoindex because data infrastructure needs reliability, performance, and tight control over resources, not just "works on my laptop" scripts. A data transformation engine that powers AI workloads is long‑running, CPU‑intensive, and often I/O‑bound; Rust’s zero‑cost abstractions, ownership model, and lack of a garbage collector let you squeeze maximum throughput out of modern hardware while catching many bugs at compile time instead of in production.

For AI‑heavy data transformation, Rust gives three key advantages:

• Robustness: the type system and borrowing rules make it much harder to ship code that corrupts state or behaves unpredictably in production.
• Performance and predictability: you can build incremental data transformations and fine‑grained caching that respond quickly to source changes, without garbage‑collection pauses.
• Ecosystem quality: the Rust crate ecosystem around async, observability, and databases enables a lean, focused data transformation engine like CocoIndex to stay small but powerful.

The CocoIndex story as a data transformation engine

CocoIndex positions itself as an "ultra performant data transformation framework for AI," with a Rust core and a Python‑first developer experience. Instead of a pile of ad‑hoc data scripts, users define flows that turn raw text, structured records, PDFs, or events into embeddings, knowledge graphs, and other derived structures, while the engine keeps inputs and outputs in sync through incremental data transformation.

This framing makes the project feel like a foundational data transformation layer for AI systems rather than a one‑off utility. By emphasizing "data transformation for AI" consistently in the README, docs, and blogs, the repository tells a coherent story that helped it climb global Rust trending and gain attention across Rust, data, and AI communities.

How to write a great README for data transformation projects

A big part of trending is packaging; the CocoIndex README reads like a clear product page for data transformation, not just a list of APIs. It leads with the "data transformation for AI" headline, highlights incremental processing and data lineage, then shows a short flow that reads raw documents, transforms them, and exports to targets like Postgres or vector stores.

For data transformation repos, strong READMEs usually include:

• A precise one‑liner that calls out "data transformation" and your audience (e.g., AI agents, search, knowledge graphs).
• An end‑to‑end example that transforms real source data into a real target, with incremental updates handled automatically by the framework.
• A gallery of examples—like document embeddings, hybrid structured + unstructured flows, and knowledge graph exports—so readers see their own data transformation problems reflected.

Choosing resonant data transformation problems

The "meeting notes → knowledge graph" example is a strong illustration of how to pick a data transformation problem that resonates with enterprises. The flow takes unstructured Markdown meeting notes in Google Drive, performs LLM‑powered extraction, and incrementally transforms them into a Neo4j knowledge graph that stays up to date as notes change.

This post went viral on LinkedIn because it mirrors a widespread pain: meeting knowledge is scattered, unstructured, and quickly becomes stale, yet decisions and ownership live there. By framing the solution explicitly as "data transformation for AI"—transforming messy notes into a live, queryable knowledge graph—CocoIndex connected directly to a class of problems many enterprise users share, which in turn drove attention back to the GitHub repo.

You can read more about the meeting notes graph example here: https://cocoindex.io/blogs/meeting-notes-graph

Pattern you followed (with "data transformation" at the center)

The path to Rust trending followed a clear pattern that others can reuse while keeping "data transformation" as the core concept. Pick a category where Rust is an obvious fit (high‑performance, incremental data transformation for AI), tell a consistent story around that phrase in the README and docs, and showcase concrete flows like the meeting‑notes knowledge graph that solve highly relatable enterprise data transformation problems.

Anywhere the story previously leaned on generic approaches, it now emphasizes "data transformation"—a continuous, observable process of turning changing source data into AI‑ready structures with incremental updates, lineage, and production‑grade guarantees.

Check out CocoIndex on GitHub: https://github.com/cocoindex-io/cocoindex

⭐ Star the repo if you’re working on AI data pipelines, knowledge graphs, or incremental indexing!