datasentinel-saxon 0.1.0

Zero-config data quality monitoring and drift detection for pandas DataFrames, with optional Claude AI diagnosis and hosted dashboard sync.

DataSentinel

Zero-config data quality monitoring for pandas DataFrames. Catches drift, anomalies, and silent data breakage — locally, in seconds, no setup required.

pip install datasentinel

Quick start (fully local — no account needed)

from datasentinel import DataSentinel
import pandas as pd

df = pd.read_csv("orders.csv")

ds = DataSentinel()
report = ds.check(df)
print(report)

DataSentinel Report
  500 rows x 11 columns
  Overall: NONE

  First run — baseline established. Run check() again later to detect drift.

Run it again tomorrow on a new export of the same data, and DataSentinel compares it against the cached baseline automatically:

df_tomorrow = pd.read_csv("orders_tomorrow.csv")
report = ds.check(df_tomorrow)
print(report)

DataSentinel Report
  512 rows x 11 columns
  Overall: HIGH

  Flagged columns:
    [HIGH] discount_pct
      - Distribution shifted (PSI=0.342)
    [MEDIUM] country
      - Distinct value count changed from 7 to 11

With a hosted account (history, scheduling, Slack alerts, AI diagnosis)

ds = DataSentinel(api_key="ds_...")
report = ds.check(df, pipeline_name="Orders")  # profiles locally AND syncs to your dashboard

When synced, report.diagnosis contains a plain-English root-cause explanation generated by Claude, and report.pipeline_url links straight to the dashboard.

Get an API key by creating a free account at datasentinel-eight.vercel.app.

Connecting a live database (hosted only)

ds = DataSentinel(api_key="ds_...")
pipeline = ds.connect_postgres(
    dsn="postgresql://user:pass@host:5432/db",
    table="orders",
    pipeline_name="Orders Pipeline",
)
result = ds.run_pipeline(pipeline["id"])

This registers a scheduled pipeline identical to one created from the dashboard — it'll run automatically on its configured interval and alert you via Slack when something breaks.

What it checks

• Null rate drift — sudden spikes or drops in missing data
• Distribution drift (PSI) — numeric and categorical distributions shifting over time
• Cardinality drift — new or disappearing categories
• Volume drift — unexpected row count changes

Why DataSentinel

Most data quality tools are either too simple (just schema checks) or too heavy (enterprise platforms requiring a deployment team). DataSentinel sits in between: zero config to start, statistically rigorous under the hood, and — when synced — explains why something broke in plain English instead of just flagging that it did.

License

MIT