DataChain: DBT for Unstructured Data
github.com
github.com
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!
My most common use cases involve getting PDFs or HTML files and I have to parse the metadata to store along with the embedding.
Would I have to run a process to extract file metadata into JSONs for every embedding/chunk? Would keys created based off document be title+chunk_no?
Very interested in this because documents from clients are subject to random changes and I don’t have very robust systems in place.
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...
Could your metadata come from something like a Postgres sql statement? Or an iceberg view?
Just connect from your Python code (like the lambda in the example) to DB and extract the necessary data.
Forgive my ignorance, but what is "json-pair"?
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.
Hopefully, @jerednel can add more details.
My retriever functions will typically use metadata in combination with the similarity search to do impart some sort of influence or for reranking.
It doesn't really replace any of the tooling we use to wrangle data at scale (like prefect or dagster or temporal) but as a local library it seems to be excellent, I think what confused me most was the comparison to dbt.
I like the from_* utils and the magic of the Column class operator overloading and how chains can be used as datasets. Love how easy checkpointing is too. Will give it a go
Try it out - looking forward to your feedback!
How does one wrangle terabytes of data on a local machine?
(that's is different from let's say DVC - that does copy files into a local cache, always)
This way, you might end up downloading just 1% of your data, as defined by the metadata filter.
I’m not sure if this term postmodern data stack was invented for the purposes of this copy. Probably not. But terms like this don’t really engender a lot of faith that this isn’t yet another piece of the now decades long hype cycle data engineering products face
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 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.
Just dug through the datachain codebase to understand a little more. I think while both projects have a Dataframe interface, they're very different projects!
Datachain seems to operate more on the orchestration layer, running Python libraries such as PIL and requests (for making API calls) and relying on an external database engine (SQLite or BigQuery/Clickhouse) for the actual compute.
Daft is an actual data engine. Essentially, it's "multimodal BigQuery/Clickhouse". We've built out a lot of our own data system functionality such as custom Rust-defined multimodal data structures, kernels to work on multimodal types, a query optimizer, distributed joins etc.
In non-technical terms, I think this means that Datachain really is more of a "DBT" which orchestrates compute over an existing engine, whereas Daft is the actual compute/data engine that runs the workload. A project such as Datachain could actually run on top of Daft, which can handle the compute and I/O operations necessary to execute the requested workload.