Every data team runs the same stack: Python for glue code, SQL for transforms, Spark or dbt for scale, YAML for orchestration. Four languages, four mental models, four places for bugs.

What if one language could do it all?

ThinkingLanguage (TL) is a purpose-built language for Data Engineering and AI. The pipe operator is a first-class citizen. Tables, schemas, filters, joins, and aggregations are native - not library calls.

let users = read_csv("users.csv")

users |> filter(age > 30) |> join(orders, on: id == user_id) |> aggregate(by: name, total: sum(amount)) |> sort(total, "desc") |> show()

What's under the hood: - Apache Arrow columnar format - DataFusion query engine with lazy evaluation and automatic optimization - CSV, Parquet, and PostgreSQL connectors - 1M rows filtered + aggregated + sorted in 0.3 ms - Written in Rust

Includes a JIT compiler (Cranelift/LLVM), native AI/ML operations (train, predict, embed), streaming pipelines with Kafka, GPU (CUDA, ROCm). Python FFI Bridge (Run/Call Python Libraries) and a full ecosystem with notebooks and a package registry.

Download via npx, ssh native installer, crates, github

This is open source (Apache Licence). If you're a data engineer tired of context-switching between five tools, or a Rust developer who wants to contribute to something new - check it out the github repo: https://github.com/mplusm/thinkinglanguage

Data Deserves its own language.