DTM Data Generator vs Metasyn
DTM Data Generator
sqledit.comMetasyn
github.comSide by side
| Figure | DTM Data Generator | Metasyn |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | Yes | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 10 | 1 |
| Spec score · Test Data Management Software | 8.2 (#6) | 7.6 (#15) |
| Intended users | The maker describes Standard as intended for individuals and small working groups, Professional for professionals and working groups handling large databases, and Enterprise for corporate solution developers. | The documentation identifies public organizations, research groups, and individual researchers who own sensitive datasets as the intended audience. |
| Purpose | DTM Data Generator creates data rows and schema objects for test database population, performance analysis, QA testing, and load testing. | Metasyn generates synthetic tabular data to help make sensitive datasets more accessible, reproducible, and reusable. |
| Support | Enterprise includes priority technical support, and the product has built-in help and online help. | Users can report bugs or ask technical questions through GitHub issues and can contact Raoul Schram. |
Plans
DTM Data Generator
- DTM Data Generator Standard — 1 license$149, paid once
- DTM Data Generator Professional — 1 license$299, paid once
- DTM Data Generator Standard — 3 licenses$299, paid once
- DTM Data Generator Enterprise — 1 license$439, paid once
- DTM Data Generator Standard — 5 licenses$479, paid once
- DTM Data Generator Professional — 3 licenses$599, paid once
- DTM Data Generator Professional — 5 licenses$899, paid once
- DTM Data Generator Enterprise — 3 licenses$979, paid once
- DTM Data Generator Enterprise — 5 licenses$1,399, paid once
- DTM Data Generator Enterprise — 10 licenses$2,499, paid once
In their own words
DTM Data Generator
DTM Data Generator creates database rows and schema objects for test population, performance analysis, quality assurance, and load testing. It examines existing schemas and resolves master-detail key relationships, then fills fields through fifteen generation methods, about 70 built-in functions, and an expression processor. Values can come from databases, CSV or text files, Excel, XML, JSON, web resources, and user-defined scripts. Connections use ODBC, OLE DB, or Oracle Call Interface; JDBC is available through the multiplatform runtime. Named systems include SQL Server, Oracle, IBM DB2, Sybase, Informix, MySQL, PostgreSQL, Interbase/Firebird, and SQLite. Results can load directly into databases or become SQL, text/CSV, XML, or JSON. XML and JSON output require Professional or Enterprise, while scrambling requires Enterprise. Standard permits 128 rules and 10,000,000 rows per rule; higher editions allow unlimited rules and higher limits. It runs self-hosted on Linux, macOS, and Windows.
Metasyn
Metasyn generates synthetic tabular data to help make sensitive datasets more accessible, reproducible, and reusable. Users fit a MetaFrame to a dataframe, save the model and settings as auditable JSON, then generate new rows. It handles varied data types, missing values, categories, unique key columns, and structured strings such as postcodes or identifiers. Metasyn supports pandas and Polars dataframes and uses Faker for localized names, emails, phone numbers, and other data. By default, its model excludes multivariate information to reduce disclosure risks. A disclosure-control plugin is available, but synthetic output still needs human review and is not guaranteed to be free of sensitive information. The plugin system allows users and organizations to add extensions. Generated dataframes can be exported to CSV, Parquet, Excel, and other formats. The free, MIT-licensed Python package is self-hosted and can be installed with pip from PyPI or directly from GitHub. The project also points users to versioned Docker containers and a command-line interface. Its intended users include public organizations, research groups, and individual researchers with sensitive datasets.