Pointblank

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Introduction

Pointblank is a data validation library for Python. You describe what data should look like in a validation plan, Pointblank checks the data and returns an easy-to-read, shareable report, so data quality becomes something the whole team can discuss.

The same plan works on Polars and Pandas DataFrames, on database tables (DuckDB, PostgreSQL, Snowflake, BigQuery and others) and on PySpark. After validation you can inspect failing rows, split the table into passing and failing rows or act when too many rows fail. A free book, Pointblank: The Complete Guide, is being written.

Data tests belong in the same pull request as the model change, just as unit tests travel with application code. Start with a few checks on the tables your dashboards and machine learning features depend on, then widen coverage as incidents show you where data breaks.

The source code is at posit-dev/pointblank on GitHub under the MIT licence, with about 507 stars; the most recent commit was on 11 November 2026.

Features

1. Validation plans: Declarative checks.
2. Readable reports: Shareable results.
3. Many backends: Polars, Pandas, databases and PySpark.
4. Row-level results: Inspect and split failures.
5. Thresholds and actions: React to failure rates.

Pros

1. Free to use: Open source under the MIT licence, so the core tool costs nothing to use.
2. Actively maintained: The repository had commits as recent as 11 November 2026.

Cons

1. Python only: No SQL-only workflow.

Usage and Links (Example)

pip install pointblank

import pointblank as pb
validation = (
    pb.Validate(data=df)
    .col_vals_gt(columns="amount", value=0)
    .col_vals_not_null(columns="customer_id")
    .interrogate()
)
validation

Resources:

• Pointblank website

Pricing

Pointblank is free and open source under the MIT licence.

Recommended for?

1. Python data teams wanting readable validation reports.

Conclusion

Pointblank makes data validation results easy to share.

Use it in notebooks and pipelines alike.

On CheckOps, Pointblank is filed under the Unit testing and Release & CI gates stages. Related tools listed here include Great Expectations (GX), Pandera, Soda Core, Deequ, dbt tests and Hypothesis. Which one fits best depends mostly on your stack, your team’s size and skills, and whether you prefer to run tools yourself or pay for a managed service. This page reflects the official website, documentation and repository as they stood on 11 November 2026.