data-engineering

Open-source ETL/ELT you deploy on your own servers or cloud. Built on DuckDB: no-code/low-code visual pipelines or SQL, 385 components, dbt, CDC, data quality, reverse ETL, lineage, MCP for AI agents. No vendor cloud, no per-row billing.

Neum AI is a best-in-class framework to manage the creation and synchronization of vector embeddings at large scale.

A MCP (Model Context Protocol) server for interacting with dbt.

AI agent tooling for data engineering workflows.

A curated collection of AI, data engineering, and DevOps projects featuring real-world applications, advanced techniques, and tutorials—ideal for learners and practitioners exploring data science and machine learning.

Use LLMs to robustly extract web data

An End to End GenAI Framework

Synthetic test data that hits the numbers you declare, exactly. Multi-table with verified foreign-key integrity, deterministic, no model in the data path. Python + MCP server.

Power up your data science workflow with ChatGPT.

Toolbox for building Generative AI applications on top of Apache Spark.

Framework for processing and filtering datasets

A fluent, scalable, and easy-to-use LLM data processing framework.

📊 ETL pipeline for YouTube competitor analytics. Orchestrated with Airflow, Docker, and AWS. Features sentiment analysis and a Power BI dashboard.

Harvest-first Rust toolkit for open data metadata: sync 300+ portals (CKAN, DCAT, SPARQL, data.json, Socrata, OpenDataSoft, ArcGIS, CSW, STAC ) into PostgreSQL, add optional local embeddings and semantic search. Ceres Open Data Index (2M+ datasets) on Hugging Face.