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|
%global _empty_manifest_terminate_build 0
Name: python-piicatcher
Version: 0.20.2
Release: 1
Summary: Find PII data in databases
License: Apache 2.0
URL: https://tokern.io/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/c6/4c/c4557ff1c8d7fc52a0706ce71154f2e84b428688c5b48f323b24aa347375/piicatcher-0.20.2.tar.gz
BuildArch: noarch
Requires: python3-pyyaml
Requires: python3-click
Requires: python3-json-logger
Requires: python3-commonregex
Requires: python3-dbcat
Requires: python3-typer
Requires: python3-tabulate
Requires: python3-dataclasses
Requires: python3-great-expectations
Requires: python3-acryl-datahub
Requires: python3-tqdm
Requires: python3-catalogue
%description
[](https://github.com/tokern/piicatcher/actions/workflows/ci.yml)
[](https://pypi.python.org/pypi/piicatcher)
[](https://pypi.org/project/piicatcher/)
[](https://pypi.org/project/piicatcher/)
[](https://hub.docker.com/r/tokern/piicatcher)
# PII Catcher for Databases and Data Warehouses
## Overview
PIICatcher is a scanner for PII and PHI information. It finds PII data in your databases and file systems
and tracks critical data. PIICatcher uses two techniques to detect PII:
* Match regular expressions with column names
* Match regular expressions and using NLP libraries to match sample data in columns.
Read more in the [blog post](https://tokern.io/blog/scan-pii-data-warehouse/) on both these strategies.
PIICatcher is *batteries-included* with a growing set of plugins to scan column metadata as well as metadata.
For example, [piicatcher_spacy](https://github.com/tokern/piicatcher_spacy) uses [Spacy](https://spacy.io) to detect
PII in column data.
PIICatcher supports incremental scans and will only scan new or not-yet scanned columns. Incremental scans allow easy
scheduling of scans. It also provides powerful options to include or exclude schema and tables to manage compute resources.
There are ingestion functions for both [Datahub](https://datahubproject.io) and [Amundsen](https://amundsen.io) which will tag columns
and tables with PII and the type of PII tags.
## Resources
* [AWS Glue & Lake Formation Privilege Analyzer](https://tokern.io/blog/lake-glue-access-analyzer/) for an example of how piicatcher is used in production.
* [Two strategies to scan data warehouses](https://tokern.io/blog/scan-pii-data-warehouse/)
## Quick Start
PIICatcher is available as a docker image or command-line application.
### Installation
Docker:
alias piicatcher='docker run -v ${HOME}/.config/tokern:/config -u $(id -u ${USER}):$(id -g ${USER}) -it --add-host=host.docker.internal:host-gateway tokern/piicatcher:latest'
Pypi:
# Install development libraries for compiling dependencies.
# On Amazon Linux
sudo yum install mysql-devel gcc gcc-devel python-devel
python3 -m venv .env
source .env/bin/activate
pip install piicatcher
# Install Spacy plugin
pip install piicatcher_spacy
### Command Line Usage
# add a sqlite source
piicatcher catalog add_sqlite --name sqldb --path '/db/sqldb'
# run piicatcher on a sqlite db and print report to console
piicatcher detect --source-name sqldb
╭─────────────┬─────────────┬─────────────┬─────────────╮
│ schema │ table │ column │ has_pii │
├─────────────┼─────────────┼─────────────┼─────────────┤
│ main │ full_pii │ a │ 1 │
│ main │ full_pii │ b │ 1 │
│ main │ no_pii │ a │ 0 │
│ main │ no_pii │ b │ 0 │
│ main │ partial_pii │ a │ 1 │
│ main │ partial_pii │ b │ 0 │
╰─────────────┴─────────────┴─────────────┴─────────────╯
### API Usage
```python3
from dbcat.api import open_catalog, add_postgresql_source
from piicatcher.api import scan_database
# PIICatcher uses a catalog to store its state.
# The easiest option is to use a sqlite memory database.
# For production usage check, https://tokern.io/docs/data-catalog
catalog = open_catalog(app_dir='/tmp/.config/piicatcher', path=':memory:', secret='my_secret')
with catalog.managed_session:
# Add a postgresql source
source = add_postgresql_source(catalog=catalog, name="pg_db", uri="127.0.0.1", username="piiuser",
password="p11secret", database="piidb")
output = scan_database(catalog=catalog, source=source)
print(output)
# Example Output
[['public', 'sample', 'gender', 'PiiTypes.GENDER'],
['public', 'sample', 'maiden_name', 'PiiTypes.PERSON'],
['public', 'sample', 'lname', 'PiiTypes.PERSON'],
['public', 'sample', 'fname', 'PiiTypes.PERSON'],
['public', 'sample', 'address', 'PiiTypes.ADDRESS'],
['public', 'sample', 'city', 'PiiTypes.ADDRESS'],
['public', 'sample', 'state', 'PiiTypes.ADDRESS'],
['public', 'sample', 'email', 'PiiTypes.EMAIL']]
```
## Plugins
PIICatcher can be extended by creating new detectors. PIICatcher supports two scanning techniques:
* Metadata
* Data
Plugins can be created for either of these two techniques. Plugins are then registered using an API or using
[Python Entry Points](https://packaging.python.org/en/latest/specifications/entry-points/).
To create a new detector, simply create a new class that inherits from [`MetadataDetector`](https://github.com/tokern/piicatcher/blob/master/piicatcher/detectors.py)
or [`DatumDetector`](https://github.com/tokern/piicatcher/blob/master/piicatcher/detectors.py).
In the new class, define a function `detect` that will return a [`PIIType`](https://github.com/tokern/dbcat/blob/main/dbcat/catalog/pii_types.py)
If you are detecting a new PII type, then you can define a new class that inherits from PIIType.
For detailed documentation, check [piicatcher plugin docs](https://tokern.io/docs/piicatcher/detectors/plugins).
## Supported Databases
PIICatcher supports the following databases:
1. **Sqlite3** v3.24.0 or greater
2. **MySQL** 5.6 or greater
3. **PostgreSQL** 9.4 or greater
4. **AWS Redshift**
5. **AWS Athena**
6. **Snowflake**
## Documentation
For advanced usage refer documentation [PIICatcher Documentation](https://tokern.io/docs/piicatcher).
## Survey
Please take this [survey](https://forms.gle/Ns6QSNvfj3Pr2s9s6) if you are a user or considering using PIICatcher.
The responses will help to prioritize improvements to the project.
## Contributing
For Contribution guidelines, [PIICatcher Developer documentation](https://tokern.io/docs/piicatcher/development).
%package -n python3-piicatcher
Summary: Find PII data in databases
Provides: python-piicatcher
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-piicatcher
[](https://github.com/tokern/piicatcher/actions/workflows/ci.yml)
[](https://pypi.python.org/pypi/piicatcher)
[](https://pypi.org/project/piicatcher/)
[](https://pypi.org/project/piicatcher/)
[](https://hub.docker.com/r/tokern/piicatcher)
# PII Catcher for Databases and Data Warehouses
## Overview
PIICatcher is a scanner for PII and PHI information. It finds PII data in your databases and file systems
and tracks critical data. PIICatcher uses two techniques to detect PII:
* Match regular expressions with column names
* Match regular expressions and using NLP libraries to match sample data in columns.
Read more in the [blog post](https://tokern.io/blog/scan-pii-data-warehouse/) on both these strategies.
PIICatcher is *batteries-included* with a growing set of plugins to scan column metadata as well as metadata.
For example, [piicatcher_spacy](https://github.com/tokern/piicatcher_spacy) uses [Spacy](https://spacy.io) to detect
PII in column data.
PIICatcher supports incremental scans and will only scan new or not-yet scanned columns. Incremental scans allow easy
scheduling of scans. It also provides powerful options to include or exclude schema and tables to manage compute resources.
There are ingestion functions for both [Datahub](https://datahubproject.io) and [Amundsen](https://amundsen.io) which will tag columns
and tables with PII and the type of PII tags.
## Resources
* [AWS Glue & Lake Formation Privilege Analyzer](https://tokern.io/blog/lake-glue-access-analyzer/) for an example of how piicatcher is used in production.
* [Two strategies to scan data warehouses](https://tokern.io/blog/scan-pii-data-warehouse/)
## Quick Start
PIICatcher is available as a docker image or command-line application.
### Installation
Docker:
alias piicatcher='docker run -v ${HOME}/.config/tokern:/config -u $(id -u ${USER}):$(id -g ${USER}) -it --add-host=host.docker.internal:host-gateway tokern/piicatcher:latest'
Pypi:
# Install development libraries for compiling dependencies.
# On Amazon Linux
sudo yum install mysql-devel gcc gcc-devel python-devel
python3 -m venv .env
source .env/bin/activate
pip install piicatcher
# Install Spacy plugin
pip install piicatcher_spacy
### Command Line Usage
# add a sqlite source
piicatcher catalog add_sqlite --name sqldb --path '/db/sqldb'
# run piicatcher on a sqlite db and print report to console
piicatcher detect --source-name sqldb
╭─────────────┬─────────────┬─────────────┬─────────────╮
│ schema │ table │ column │ has_pii │
├─────────────┼─────────────┼─────────────┼─────────────┤
│ main │ full_pii │ a │ 1 │
│ main │ full_pii │ b │ 1 │
│ main │ no_pii │ a │ 0 │
│ main │ no_pii │ b │ 0 │
│ main │ partial_pii │ a │ 1 │
│ main │ partial_pii │ b │ 0 │
╰─────────────┴─────────────┴─────────────┴─────────────╯
### API Usage
```python3
from dbcat.api import open_catalog, add_postgresql_source
from piicatcher.api import scan_database
# PIICatcher uses a catalog to store its state.
# The easiest option is to use a sqlite memory database.
# For production usage check, https://tokern.io/docs/data-catalog
catalog = open_catalog(app_dir='/tmp/.config/piicatcher', path=':memory:', secret='my_secret')
with catalog.managed_session:
# Add a postgresql source
source = add_postgresql_source(catalog=catalog, name="pg_db", uri="127.0.0.1", username="piiuser",
password="p11secret", database="piidb")
output = scan_database(catalog=catalog, source=source)
print(output)
# Example Output
[['public', 'sample', 'gender', 'PiiTypes.GENDER'],
['public', 'sample', 'maiden_name', 'PiiTypes.PERSON'],
['public', 'sample', 'lname', 'PiiTypes.PERSON'],
['public', 'sample', 'fname', 'PiiTypes.PERSON'],
['public', 'sample', 'address', 'PiiTypes.ADDRESS'],
['public', 'sample', 'city', 'PiiTypes.ADDRESS'],
['public', 'sample', 'state', 'PiiTypes.ADDRESS'],
['public', 'sample', 'email', 'PiiTypes.EMAIL']]
```
## Plugins
PIICatcher can be extended by creating new detectors. PIICatcher supports two scanning techniques:
* Metadata
* Data
Plugins can be created for either of these two techniques. Plugins are then registered using an API or using
[Python Entry Points](https://packaging.python.org/en/latest/specifications/entry-points/).
To create a new detector, simply create a new class that inherits from [`MetadataDetector`](https://github.com/tokern/piicatcher/blob/master/piicatcher/detectors.py)
or [`DatumDetector`](https://github.com/tokern/piicatcher/blob/master/piicatcher/detectors.py).
In the new class, define a function `detect` that will return a [`PIIType`](https://github.com/tokern/dbcat/blob/main/dbcat/catalog/pii_types.py)
If you are detecting a new PII type, then you can define a new class that inherits from PIIType.
For detailed documentation, check [piicatcher plugin docs](https://tokern.io/docs/piicatcher/detectors/plugins).
## Supported Databases
PIICatcher supports the following databases:
1. **Sqlite3** v3.24.0 or greater
2. **MySQL** 5.6 or greater
3. **PostgreSQL** 9.4 or greater
4. **AWS Redshift**
5. **AWS Athena**
6. **Snowflake**
## Documentation
For advanced usage refer documentation [PIICatcher Documentation](https://tokern.io/docs/piicatcher).
## Survey
Please take this [survey](https://forms.gle/Ns6QSNvfj3Pr2s9s6) if you are a user or considering using PIICatcher.
The responses will help to prioritize improvements to the project.
## Contributing
For Contribution guidelines, [PIICatcher Developer documentation](https://tokern.io/docs/piicatcher/development).
%package help
Summary: Development documents and examples for piicatcher
Provides: python3-piicatcher-doc
%description help
[](https://github.com/tokern/piicatcher/actions/workflows/ci.yml)
[](https://pypi.python.org/pypi/piicatcher)
[](https://pypi.org/project/piicatcher/)
[](https://pypi.org/project/piicatcher/)
[](https://hub.docker.com/r/tokern/piicatcher)
# PII Catcher for Databases and Data Warehouses
## Overview
PIICatcher is a scanner for PII and PHI information. It finds PII data in your databases and file systems
and tracks critical data. PIICatcher uses two techniques to detect PII:
* Match regular expressions with column names
* Match regular expressions and using NLP libraries to match sample data in columns.
Read more in the [blog post](https://tokern.io/blog/scan-pii-data-warehouse/) on both these strategies.
PIICatcher is *batteries-included* with a growing set of plugins to scan column metadata as well as metadata.
For example, [piicatcher_spacy](https://github.com/tokern/piicatcher_spacy) uses [Spacy](https://spacy.io) to detect
PII in column data.
PIICatcher supports incremental scans and will only scan new or not-yet scanned columns. Incremental scans allow easy
scheduling of scans. It also provides powerful options to include or exclude schema and tables to manage compute resources.
There are ingestion functions for both [Datahub](https://datahubproject.io) and [Amundsen](https://amundsen.io) which will tag columns
and tables with PII and the type of PII tags.
## Resources
* [AWS Glue & Lake Formation Privilege Analyzer](https://tokern.io/blog/lake-glue-access-analyzer/) for an example of how piicatcher is used in production.
* [Two strategies to scan data warehouses](https://tokern.io/blog/scan-pii-data-warehouse/)
## Quick Start
PIICatcher is available as a docker image or command-line application.
### Installation
Docker:
alias piicatcher='docker run -v ${HOME}/.config/tokern:/config -u $(id -u ${USER}):$(id -g ${USER}) -it --add-host=host.docker.internal:host-gateway tokern/piicatcher:latest'
Pypi:
# Install development libraries for compiling dependencies.
# On Amazon Linux
sudo yum install mysql-devel gcc gcc-devel python-devel
python3 -m venv .env
source .env/bin/activate
pip install piicatcher
# Install Spacy plugin
pip install piicatcher_spacy
### Command Line Usage
# add a sqlite source
piicatcher catalog add_sqlite --name sqldb --path '/db/sqldb'
# run piicatcher on a sqlite db and print report to console
piicatcher detect --source-name sqldb
╭─────────────┬─────────────┬─────────────┬─────────────╮
│ schema │ table │ column │ has_pii │
├─────────────┼─────────────┼─────────────┼─────────────┤
│ main │ full_pii │ a │ 1 │
│ main │ full_pii │ b │ 1 │
│ main │ no_pii │ a │ 0 │
│ main │ no_pii │ b │ 0 │
│ main │ partial_pii │ a │ 1 │
│ main │ partial_pii │ b │ 0 │
╰─────────────┴─────────────┴─────────────┴─────────────╯
### API Usage
```python3
from dbcat.api import open_catalog, add_postgresql_source
from piicatcher.api import scan_database
# PIICatcher uses a catalog to store its state.
# The easiest option is to use a sqlite memory database.
# For production usage check, https://tokern.io/docs/data-catalog
catalog = open_catalog(app_dir='/tmp/.config/piicatcher', path=':memory:', secret='my_secret')
with catalog.managed_session:
# Add a postgresql source
source = add_postgresql_source(catalog=catalog, name="pg_db", uri="127.0.0.1", username="piiuser",
password="p11secret", database="piidb")
output = scan_database(catalog=catalog, source=source)
print(output)
# Example Output
[['public', 'sample', 'gender', 'PiiTypes.GENDER'],
['public', 'sample', 'maiden_name', 'PiiTypes.PERSON'],
['public', 'sample', 'lname', 'PiiTypes.PERSON'],
['public', 'sample', 'fname', 'PiiTypes.PERSON'],
['public', 'sample', 'address', 'PiiTypes.ADDRESS'],
['public', 'sample', 'city', 'PiiTypes.ADDRESS'],
['public', 'sample', 'state', 'PiiTypes.ADDRESS'],
['public', 'sample', 'email', 'PiiTypes.EMAIL']]
```
## Plugins
PIICatcher can be extended by creating new detectors. PIICatcher supports two scanning techniques:
* Metadata
* Data
Plugins can be created for either of these two techniques. Plugins are then registered using an API or using
[Python Entry Points](https://packaging.python.org/en/latest/specifications/entry-points/).
To create a new detector, simply create a new class that inherits from [`MetadataDetector`](https://github.com/tokern/piicatcher/blob/master/piicatcher/detectors.py)
or [`DatumDetector`](https://github.com/tokern/piicatcher/blob/master/piicatcher/detectors.py).
In the new class, define a function `detect` that will return a [`PIIType`](https://github.com/tokern/dbcat/blob/main/dbcat/catalog/pii_types.py)
If you are detecting a new PII type, then you can define a new class that inherits from PIIType.
For detailed documentation, check [piicatcher plugin docs](https://tokern.io/docs/piicatcher/detectors/plugins).
## Supported Databases
PIICatcher supports the following databases:
1. **Sqlite3** v3.24.0 or greater
2. **MySQL** 5.6 or greater
3. **PostgreSQL** 9.4 or greater
4. **AWS Redshift**
5. **AWS Athena**
6. **Snowflake**
## Documentation
For advanced usage refer documentation [PIICatcher Documentation](https://tokern.io/docs/piicatcher).
## Survey
Please take this [survey](https://forms.gle/Ns6QSNvfj3Pr2s9s6) if you are a user or considering using PIICatcher.
The responses will help to prioritize improvements to the project.
## Contributing
For Contribution guidelines, [PIICatcher Developer documentation](https://tokern.io/docs/piicatcher/development).
%prep
%autosetup -n piicatcher-0.20.2
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-piicatcher -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.20.2-1
- Package Spec generated
|
