dqflow 0.1.1

Lightweight, contract-first data quality engine for modern data pipelines

dqflow

dqflow is a lightweight, contract-first data quality engine for modern data pipelines.

Define explicit expectations for your data (schema, validity, freshness) and fail fast when data breaks — before bad data reaches downstream systems.

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Why dqflow?

Data quality issues are inevitable — silent failures are not.

Most teams rely on ad-hoc checks, fragile assertions, or heavyweight frameworks that are hard to maintain. dqflow takes a different approach:

• Contracts over checks — expectations are explicit and versionable
• Pipeline-first — designed for ETL, ELT, and streaming workflows
• Lightweight & Pythonic — minimal API, easy to embed
• Fail fast — break pipelines intentionally, not silently

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Quick example

from dqflow import Contract, Column

orders = Contract(
    name="orders",
    columns={
        "order_id": Column(str, not_null=True),
        "amount": Column(float, min=0),
        "currency": Column(str, allowed=["USD", "EUR"]),
        "created_at": Column("timestamp", freshness_minutes=60),
    },
    rules=[
        "row_count > 1000",
        "null_rate(amount) < 0.01",
    ],
)

result = orders.validate(df)

if not result.ok:
    raise Exception(result.summary())

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Features (v0.1 scope)

• Contract-as-code (Python & YAML)

• Column-level checks

  • type validation
  • not null
  • min / max
  • allowed values

• Table-level checks

  • row count
  • freshness

• Structured validation results (JSON-friendly)

• Pandas engine

• CLI support

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CLI usage

dq validate contracts/orders.yaml data/orders.parquet

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Supported engines

• ✅ Pandas
• 🚧 PySpark (planned)
• 🚧 SQL tables (planned)

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Philosophy

• Explicit is better than implicit
• Bad data should break pipelines early
• Quality rules are part of your system design

dqflow is not a full data observability platform. It is a small, opinionated library meant to be embedded directly into pipelines.

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Roadmap

• PySpark engine
• dbt / dlt integrations
• Incremental & backfill-aware validation
• Metrics export (Prometheus-compatible)

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License

MIT

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Status

🚧 Early development (v0.1.0)

APIs may change. Feedback and contributions are welcome.