DataJig

DataJig is an agent-native data control plane that turns raw datasets into verified, versioned, training-ready inputs.

DataJig gives AI agents a safe, auditable path from raw data to model training. It wraps data work in explicit plans, deterministic identities, semantic checks, immutable revisions, and evidence of which records crossed a verified training adapter boundary.

Version 0.8.4 makes active changeset selectors consistent, publishes the full JSONL quality-policy schema and example, and allows guarded patch application on filesystems that explicitly do not support extended attributes.

raw source -> pin -> prepare/transform -> review -> seal -> export -> train
                `------ content-addressed evidence -------'  `-> receipt

DataJig is local-first and ships as a Python wheel containing its Rust core. It supports single files and deterministic recursive datasets in CSV, flat Parquet, or keyed JSONL, plus ImageFolder workflows.

Install

python -m pip install datajig
datajig --version
datajig capabilities
datajig tutorial ./datajig-tutorial

Add the exactly pinned DuckDB provider for bounded joins, projections, filters, and aggregations:

python -m pip install 'datajig[duckdb]'

Where it fits

DataJig does not replace Git, DVC, Oxen, lakeFS, DuckDB, Polars, Spark, PyTorch, or Hugging Face. It sits between those systems as an agent-facing control plane:

• pin exact upstream bytes and preserve provenance;
• authorize bounded preparation and SQL transform plans;
• scope agent edits and reject stale review evidence;
• seal content-addressed, immutable dataset revisions;
• export deterministic subsets, splits, and training shards;
• verify records at the training adapter boundary and write an at-least-once receipt for that boundary.

Every task, candidate, review, revision, subset, transform, and training bundle receives a deterministic identity. If data changes after review, a plan drifts, or an agent touches bytes outside its declared task, DataJig fails closed.

Agent-native interface

Commands return bounded JSON with actionable next_actions. Discovery exposes a deterministic agent_contract_id, machine-readable command descriptors, and artifact schemas. repository-install can install a version-matched Agent Skill, pre-commit guard, SHA-pinned GitHub Actions workflow, and content-addressed lock into another repository.

datajig repository-install --root .
datajig repository-check --root .

Typical workflow

For a complete local training-data delivery, install the DuckDB extra and use a single reviewed pipeline:

datajig artifact-schema pipeline-config
datajig artifact-schema jsonl-quality-policy
datajig pipeline plan --config pipeline.yaml --plan pipeline-plan.json
datajig pipeline apply pipeline-plan.json --accept-plan pipe_...
datajig pipeline info deliveries/my-training-data/pipeline-receipt.json --verify
datajig lineage pipe_... --state workspace/.datajig --format text

The plan binds source bytes, SQL, target paths, export splits, and external run IDs. Apply uses OS locks, a durable recovery journal, detached revision sealing, HEAD compare-and-swap, and a delivery commit marker. Interrupted work requires explicit --resume; an identical update still delivers a bundle and consumer plans without inventing a new revision.

lineage resolves committed pipeline receipts and related pipeline artifacts. For an atomic manual workflow, inspect its transform receipt and workspace log separately; a bare manual revision is not a pipeline lineage root.

An abbreviated configuration looks like this:

schema_version: 1
pipeline: {name: papers-train, provider: duckdb}
target: {dataset: data/papers.jsonl, state: workspace/.datajig, mode: create}
delivery: {output: deliveries/papers-train}
inputs:
  - {alias: papers, path: raw/papers.parquet, format: parquet}
transform:
  sql: SELECT paper_id AS id, title FROM papers ORDER BY id
  id_field: id
export: {split: [train=9, test=1], max_shard_records: 10000}
consumption_plan:
  - {consumer: huggingface, split: train, run_id: experiment-42}

Atomic commands remain available when a pipeline is not the right abstraction:

datajig inspect data/papers.parquet --id-field paper_id
datajig prepare-plan data/papers.parquet \
  --recipe recipes/papers.prepare.json \
  --output data/papers.jsonl \
  --plan artifacts/papers.prepare.plan.json
datajig prepare-apply artifacts/papers.prepare.plan.json \
  --accept-plan prep_...
datajig init data/papers.jsonl --id-field paper_id

The optional DuckDB provider follows the same plan-and-accept pattern. SQL is authorized by the Rust core before execution, input aliases are staged privately, resources are bounded, and canonical JSONL output is independently verified.

Project links

• Repository: https://github.com/liukejun7/DataJig
• Releases: https://github.com/liukejun7/DataJig/releases
• Changelog: https://github.com/liukejun7/DataJig/blob/main/CHANGELOG.md
• Security policy: https://github.com/liukejun7/DataJig/blob/main/SECURITY.md
• License: Apache-2.0