Show HN: I built a local data lake for AI powered data engineering and analytics
I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively.
You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You don’t have to setup local LLMs, it comes built in.
This is completely free. No cloud account required.
Downloading the software - https://getnile.ai/downloads
Watch a demo - https://www.youtube.com/watch?v=C6qSFLylryk
Check the code repo - https://github.com/NileData/local
This is still early and I'd genuinely love your feedback on what's broken, what's missing, and if you find this useful for your data and analytics work.
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8 comments
| 7 points
| on HN
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1. You may not want to expose bits and pieces of your data and metadata to an LLM, you dont want your data to be used for training. If you are using LLM running on your machine, as in this case, you are covered there.
2. Claude can do a lot of stuff, but doing multi step analysis consistently and reliably is not guaranteed due to the non-deterministic nature of LLMs. Every time it may take a different route. Nile local offers a bunch of data primitives like query, build-pipe, discover, etc. that reduces the non-determinism and bring reliability and transparency (how the answer was derrived) to the data analysis.
You don't need to setup LLM locally, the tool does that. You can choose which model to go with. It has Gemma and Qwen supported now.
You will get job run level lineage for any datasets created in the system.