dmpetrov

I like the idea a lot but it's still unclear from the docs what the hard security boundary is once you start calling LLMs - can it avoid "breaking out" into the host env in practice?

Good point - it does sound a bit like marketing bs. We’ll rephrase it.

Please share your feedback!

Good question! I’m not so familiar with it.

It looks like Daft is closer to Lance with it’s own data format and engine. But I’d appreciate more insights from users or the creators.

Lance is just a data format. Lance DB might be more comparable to DataChain.

DataChain focuses on data transformation and versioning, whereas LanceDB appears to be more about retrieving and serving data. Both designed for multimodal use cases.

From technical side: Lance has it's own data format and DB engine while DataChain utilizes existing DB engines (SQLite in open-source and ClickHouse/BigQuery in SaaS).

In SaaS, DataChain has analytics features including data lineage tracking and visualization for PDFs, videos, and annotated images (e.g., bounding boxes, poses). I'm curious to understand the unique value of LanceDB's SaaS — insight would be helpful!

You could think of it as OLTP (Lance) versus OLAP (DataChain) for multimodal data, though this analogy may not be perfect.

It's not a format :)

It's simpliy about linking metadata from a json to a corresponding image or video file, like pairing data003.png & data003.json to a single, virtual record. Some format use this approach: open-image or laion datasets.

Absolutely, that's a common scenario!

Just connect from your Python code (like the lambda in the example) to DB and extract the necessary data.

I guess, it involves splitting a file into smaller document snippets, getting page numbers and such, and calculating embeddings for each snippet—that’s the usual approach. Specific signals vary by use case.

Hopefully, @jerednel can add more details.

Exactly! DataChain does lazy compute. It will read metadata/json while applying filtering and only download a sample of data files (jpg) based on the filter.

This way, you might end up downloading just 1% of your data, as defined by the metadata filter.

DataChain has no assumptions about metadata format. However, some formats are supported out of the box: WebDataset, json-pair, openimage, etc.

Extract metadata as usual, then return the result as JSON or a Pydantic object. DataChian will automatically serialize it to internal dataset structure (SQLite), which can be exported to CSV/Parquet.

In case of PDF/HTML, you will likely produce multiple documents per file which is also supported - just `yield return my_result` multiple times from map().

Check out video: https://www.youtube.com/watch?v=yjzcPCSYKEo Blog post: https://datachain.ai/blog/datachain-unstructured-pdf-process...

Yes, it's not meant to replace data engineering tools like Prefect or Temporal. Instead, it serves as a transformation engine and ad-hoc analytics for images/video/text data. It's pretty much DBT use case for text and images in S3/GCS, though every analogy has its limits.

Try it out - looking forward to your feedback!

Yay! Excited to see DataChain on the front page :)

Maintainer and author here. Happy to answer any questions.

We built DataChain because our DVC couldn't fully handle data transformations and versioning directly in S3/GCS/Azure without data copying.

Analogy with "DBT for unstractured data" applies very well to DataChain since it transforms data (using Python, not SQL) inside in storages (S3, not DB). Happy to talk more!

Can this work statistically? For a giving number of attempts, you can ger a required number of successes to make sure it's a statistically meaningful result.

In theory, this approach could help address the non-determinism of LLMs.

Thank you!

Just shoot an email to support and mention HN. I’ll read and reply.

Right, DVC caches data for consistency and reproducibility.

If caching is not needed and streaming required, we've created a sister tool DataChain. It's even supports WebDataset and can stream from tar archives and filter images by metadata.

WebDataset example: https://github.com/iterative/datachain/blob/main/examples/mu...

Yes. And if you track transformations of the binaries or ml training

In this cases, you need DVC if:

1. File are too large for Git and Git LFS.

2. You prefer using S3/GCS/Azure as a storage.

3. You need to track transformations/piplines on the file - clean up text file, train mode, etc.

Otherwise, vanilla Git may be sufficient.

hi there! Maintainer and author here. Excited to see DVC on the front page!

Happy to answer any questions about DVC and our sister project DataChain https://github.com/iterative/datachain that does data versioning with a bit different assumptions: no file copy and built-in data transformations.

Hey! I'm one of the creators of DataChain.

DataChain works on your local machine and manages files in storage (like images and PDFs in S3 or GCP). Users can slice and dice their files using metadata. Example:

- Download only files labeled "Cats" instead of the whole dataset. Use json/parque to get labels.

- Use LLMs to generate metadata. E.g., "Are there more than 3 people in the image?".

- Add custom metadata to create a rich "DataFrame" of your files

The API of the data-frame is based on Python (Pydentic) but queries to Pythion objects are transpiled to database (SQLite). Or you can just convert all metadata into Pandas if you prefer.

WDYT? I’d love to hear your thoughts!

It uses SQLite in open-source. In SaaS - proprietary data warehouses where your engine can be integrated.

Bridging the gap between AI and data warehouses is crucial, but I’m not sure SQL is the best fit for AI engineers who mainly work with Python and AI APIs.

At DataChain, we are solving this by creating a Python API that translates to SQL under the hood, which is pretty easy now with Pydantic. https://github.com/iterative/datachain

WDYT?

Well... I need to rent a property with +1 bedroom to WFH. It is a substantial family spending.

> I do not treat Kubernetes as Cloud-Native... for end-users to achieve cloud native goals.

Do you mean that Cloud-Native is about applications that are built on top of K8S, not K8S itself?

What are the cloud native goals? Abstracting out end-users from any cloud?

I'd like to see a research of WFH impact to people's spendings. Many households have to pay extra for more space to have a home office while companies pay less :)

It would be helpful to see the difference with MLFlow. The deployment part as well as model registry part.

Yes, it' from DVC/CML team! We started TPI as a "computational backend" for CML project (CI/CD for ML). But then we realized that it can be useful as an independent tool.

Hey all, we are launching Terraform Provider Iterative (TPI).

It was designed for machine learning (ML/AI) teams and optimizes CPU/GPU expenses:

1. Spot instances auto-recovery (if an instance was evicted/terminated) with data and checkpoint synchronization

2. Auto-terminate instances when ML training is finished - you won't forget to terminate your expensive GPU instance for a week :)

3. Familiar Terraform commands and config (HCL)

The secret sauce is auto-recovery logic that is based on cloud auto-scaling groups and does not require any monitoring service to run (another cost-saving!). Cloud providers recover it for you. TPI just unifies auto-scaling groups for all the major cloud providers: AWS, Azure, GCP and Kubernetes. Yeah, it was tricky to unify all clouds :)

We'd love to hear your feedback!

In product companies, software development creates value that company sales.

DVC has metrics logger similar to other experiment management tool: https://github.com/iterative/dvclive/

Also, metrics & params section of the docs explains this (but yes, it is not perfect yet): https://dvc.org/doc/start/metrics-parameters-plots

How can I exit from Notebook? :) It is a whole new level.