Babel Obfuscator vs. IBM InfoSphere Optim Data Privacy vs. QShield vs. python-sql Comparison Chart

IBM InfoSphere® Optim™ Data Privacy provides extensive capabilities to effectively mask sensitive data across non-production environments, such as development, testing, QA or training. To protect confidential data this single offering provides a variety of transformation techniques that substitute sensitive information with realistic, fully functional masked data. Examples of masking techniques include substrings, arithmetic expressions, random or sequential number generation, date aging, and concatenation. The contextually accurate masking capabilities help masked data retain a similar format to the original information. Apply a range of masking techniques on-demand to transform personally-identifying information and confidential corporate data in applications, databases and reports. Data masking features help you to prevent misuse of information by masking, obfuscating, and privatizing personal information that is disseminated across non-production environments.

python-sql is a library to write SQL queries in a pythonic way. Simple selects, select with where condition. Select with join or select with multiple joins. Select with group_by and select with output name. Select with order_by, or select with sub-select. Select on other schema and insert query with default values. Insert query with values, and insert query with query. Update query with values. Update query with where condition. Update query with from the list. Delete query with where condition, and delete query with sub-query. Provides limit style, qmark style, and numeric style.