/data-quality

name: data-quality
description: Use when validating a dataset or building quality checks into a pipeline. Covers profiling, schema and constraint validation, freshness and completeness checks, anomaly detection, and failing a pipeline correctly. Trigger on "this metric looks wrong", "add checks to the pipeline", "data is missing for yesterday", "validate this file before loading it". Do not use for cleaning or reshaping data in Python — see the pandas skill.
metadata:
  category: data
  version: 1.0.0
  tags: [data-quality, validation, profiling, pipelines, testing]

Data Quality

Purpose

Catch bad data before it reaches a dashboard, a model, or a customer. A pipeline that silently propagates corrupt data is worse than one that fails, because the failure is discovered downstream, later, by someone who trusts the number.

When to Use

• Ingesting data from a source you do not control.
• Building quality gates into a pipeline.
• Investigating a metric that looks wrong.
• Auditing a dataset before it is used for analysis or training.

Capabilities

• Profiling: distributions, cardinality, null rates, outliers.
• Schema validation and type enforcement.
• Constraint checks: uniqueness, referential integrity, ranges, formats.
• Freshness, completeness, and volume anomaly detection.
• Quarantine and alerting patterns.

Inputs

• The dataset and its expected schema.
• The business rules the data must satisfy.
• Historical volume and distribution, for anomaly baselines.

Outputs

• A profile of the data as it actually is, not as documented.
• Validation checks that run on every load.
• A quarantine path for rows that fail, and an alert when they do.

Workflow

1. **Profile before you trust** — Row count, null rate, cardinality, min/max, and the distribution of every column. The documented schema and the actual data disagree more often than not. 2. **Validate the schema at the boundary** — Column presence, types, and nullability, checked on ingest. A silently added column or a type change upstream is the most common pipeline break. 3. **Assert the business rules** — Uniqueness on keys, referential integrity, valid ranges, and formats. `total_cents >= 0` is a rule; assert it. 4. **Check freshness and volume** — Is the data recent, and is there roughly as much of it as usual? A pipeline that runs successfully on an empty file is the failure that is hardest to notice. 5. **Quarantine, do not drop** — Failing rows go to a quarantine table with the reason. Dropping them silently destroys the evidence needed to fix the source. 6. **Fail loudly and stop** — A pipeline that continues past a failed quality gate has published bad data. Stop, alert, and keep the previous good version live.

Best Practices

• A pipeline that succeeds on zero rows is the most dangerous kind of success. Always assert a minimum expected volume.
• Check the distribution, not just the schema. A column that is technically valid but where the null rate jumped from 2% to 60% overnight indicates a broken upstream, and no type check will catch it.
• Quality checks belong in the pipeline, not in a separate job that runs afterwards. By then, the data has been consumed.
• Fail the pipeline on a hard rule violation (duplicate primary keys); warn on a soft one (unusual distribution). Treating everything as fatal trains people to disable the checks.
• Version the expectations alongside the data schema. A rule that was correct last year may be wrong now, and it should be updated deliberately rather than deleted in frustration.
• Every quarantined row needs the reason attached. "Failed validation" without a reason is not actionable.

Examples

**Quality gate with hard and soft rules:**

import pandera as pa
from pandera.typing import Series

class OrderSchema(pa.DataFrameModel):
    order_id: Series[str]      = pa.Field(unique=True, str_matches=r"^ord_[0-9A-Z]{10}$")
    customer_id: Series[str]   = pa.Field(nullable=False)
    total_cents: Series[int]   = pa.Field(ge=0, le=100_000_000)   # no negatives, no absurd values
    currency: Series[str]      = pa.Field(isin=["USD", "EUR", "GBP"])
    created_at: Series[pa.DateTime] = pa.Field(nullable=False)

    class Config:
        strict = True          # an unexpected column is an error, not a shrug
        coerce = False         # a wrong type is an error, not a silent conversion


def load(df: pd.DataFrame) -> pd.DataFrame:
    try:
        valid = OrderSchema.validate(df, lazy=True)   # collect all failures, not just the first
    except pa.errors.SchemaErrors as e:
        failures = e.failure_cases                     # row index + column + reason
        quarantine.write(df.loc[failures["index"].dropna().unique()], reasons=failures)
        alert(f"{len(failures)} rows quarantined on orders load", failures.head(20))
        raise PipelineHalted("orders failed schema validation")   # do not publish

    return valid

**The check that catches the silent failure:**

# Volume and freshness. A pipeline that "succeeded" with 0 rows, or with
# yesterday's data, has failed in the way that is hardest to notice.
expected = baseline.median_rows(window_days=28)
actual = len(df)

if actual == 0:
    raise PipelineHalted("orders load produced 0 rows — upstream is likely broken")

if actual < expected * 0.5:
    alert(f"orders volume anomaly: {actual} rows vs a 28-day median of {expected}")

max_age = utcnow() - df["created_at"].max()
if max_age > timedelta(hours=6):
    raise PipelineHalted(f"orders data is stale: newest record is {max_age} old")

Notes

• The most valuable check in any pipeline is the volume anomaly check, because it catches the failures where every individual row is valid and the dataset as a whole is wrong.
• Great Expectations, Pandera, and dbt tests all do this job; the tool matters far less than whether the checks run on every load and actually stop the pipeline.
• Data quality is a contract with the upstream produc