Get an EPSON tank printer, or a laser..
You will get job run level lineage for any datasets created in the system.
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.
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.
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...
Lineage-aware. Versioned. Trustworthy Data - for Engineers and AI.
Your engineers waste up to 40% of their time monitoring, investigating and fixing data. Even then you don’t trust the accuracy, source, or freshness of metrics on your dashboard. You wish AI can answer your data questions but it cannot show you proof, or where it came from. AI helps software engineers to move fast and break things, because they can always rollback, with git. But you cannot do that for data. Bad data entering the system, spreads across the company before spotting, and takes weeks to clean up.
DV changes this, giving you lineage-aware, versioned data. It records data-lineage when data is captured, transformed, and committed, at commit/snapshot level. So when things break, DV knows what other data is impacted downstream, and it can rollback the whole chain to the previous state, instantly - no data copy/restore needed. It can also backfill the data across the chain automatically.
With DV, both your team and your AI agents can finally see: - where data came from - how it was transformed - how to revert safely with a single click
Your engineers can move fast on data, without breaking trust. Your analysts can build pipelines by simply describing business questions to AI.
DV is Git for data, so you can focus on your business, putting analytics on auto pilot.
-- Please contact me if you are interested in preview program.
For the past few months, we were working on a financial transparency tool for governments and non-profits. I left my job to make this software a reality. I and a couple of my friends have worked hard to get this working and we need some serious review from experts on this. Please take a look and give us feedback.
Elevator Pitch: Allow organizations to publish financial information online.
Looking for honest, brutal feedback. Thank you for your time!
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Clickable links:
Website - http://openreporting.com
Demo report - http://sampleorg.openreporting.com/Report/Category-198-Yearl...
Hello,I was working on this financial transparency tool [ http://openreporting.com ] for governments and non-profits for the last few months. I left my job to make this software a reality. But it's only myself and a couple of my friends who have burned any neurons to get this working and we need some serious review from experts like you.. Can I ask you to please take a look and give some...
I use a personal laptop, an office laptop, an office desktop and a mobile phone. And I was thinking what if I can zip all this in to one small portable device...If yes, + We need to carry only one device at home and office + There is no need to sync your different machines + Your digital world is always there in your pocket + Use docking stations to connect to big displays and keyboards ...