acryl-datahub-classify
[DEPRECATED] Library to predict info types for DataHub
Decision gist · record as of 2026-08-14
No. The package is explicitly deprecated and no longer maintained.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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- 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.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), allowing use, modification, and distribution with minimal restrictions.
last release 2026-02-19 (176 days) · last repo commit 2026-02-19 · 3 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 191,368 downloads/mo, #9,883 on PyPI
Alternatives
Verify before relying
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
)- 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.
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 on this page. Verify before relying on it.
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
acryl-datahub-classify on PyPI
Before you install
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.
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
)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 the current Python release >=3.7 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesphonenumbersschwiftyspacyipaddressvininfopython-stdnum |
| Maintenance | Actively maintained 176 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 191,368 / month, #9,883 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 7 - InactiveEnvironment :: ConsoleEnvironment :: MacOS XIntended Audience :: DevelopersIntended Audience :: Information TechnologyIntended Audience :: System AdministratorsLicense :: OSI ApprovedLicense :: OSI Approved :: Apache Software LicenseOperating System :: POSIX :: LinuxOperating System :: UnixProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Software Development |
Evidence: acryl_datahub_classify-0.0.12-py3-none-any.whl
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See also acryl-datahub · datahub · acryl-datahub-airflow-plugin · acryl-datahub-actions · acryl-datahub-dagster-plugin · acryl-executor · gliner2 · carelytics · scrubadub · dataclasses-avroschema