---
id: acryl-datahub-classify
version: "0.0.12"
license: Apache License 2.0
license_treatment: permissive
maintenance: active
---

# acryl-datahub-classify — [DEPRECATED] Library to predict info types for DataHub

License: permissive · Maintenance: active · Downloads: 191.4K/mo

## What it is and what it does

acryl-datahub-classify is a deprecated library that identifies sensitive information types in database columns by combining pattern matching, specialized validation libraries, and metadata analysis. It takes column metadata (name, description, datatype) and sample values, then scores each against configurable infotypes like phone numbers, email addresses, credit card numbers, and vehicle identification numbers. The package uses phonenumbers, schwifty, spacy, ipaddress, vininfo, and python-stdnum to validate candidate values and weights predictions across four factors: column name, description, datatype, and actual values. It returns confidence scores and debug information for each detected infotype.

The library is designed for integration with DataHub data catalogs to automate sensitive data discovery and classification. It supports 14 built-in infotypes out of the box and allows custom regex-based infotypes through configuration dictionaries. However, the package is explicitly marked as deprecated and no longer actively maintained, meaning it will not receive updates or bug fixes.

Use it for:

- Automatically tag sensitive columns in a DataHub catalog during data ingestion to flag PII that requires access controls.
- Scan database schemas to identify columns containing credit card or bank account numbers for compliance audits.
- Build a custom data classification pipeline that detects phone numbers, email addresses, and names in unstructured column samples.
- Validate that column metadata (names and descriptions) aligns with detected sensitive data types for data governance.
- Generate confidence-scored infotype proposals to assist manual data stewardship workflows without full automation.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Predicts sensitive data types (infotypes) in database columns by analyzing column metadata, descriptions, datatypes, and sample values using pattern matching and specialized libraries.

No. The package is explicitly deprecated and no longer maintained. While it has low install friction and permissive licensing, the lack of active maintenance means no security patches, bug fixes, or support for new Python versions or infotype requirements. For new projects, seek an actively maintained alternative; for existing deployments, plan migration away from this library.

## Install

pip install acryl-datahub-classify
uv add acryl-datahub-classify
poetry add acryl-datahub-classify

## Installing acryl-datahub-classify

License in practice: Licensed under Apache License 2.0 (permissive), allowing use, modification, and distribution with minimal restrictions.

Quickstart:

from acryl_datahub_classify import predict_infotypes

column_infos = [...]  # List of ColumnInfo objects with metadata and sample values
result = predict_infotypes(
    column_infos=column_infos,
    confidence_level_threshold=0.5,
    global_config={...}  # Infotype configuration dictionary
)

Requires at least 50 non-null column values when value prediction factor weight is non-zero; spacy model must be installed separately for NLP-based detection.

Verify before relying:

- Whether the package's deprecation status means it will receive security patches or if users should expect no further maintenance.
- Performance characteristics when processing large numbers of columns or high-cardinality datasets.
- Accuracy benchmarks for the supported infotypes under different data distributions.

## Package facts

- License: Apache License 2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 191.4K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

sensitive data classification, infotype prediction, pii detection columns, data catalog classification, column metadata analysis, personally identifiable information detection, datahub integration, deprecated, pii-detection, data-governance

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