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# rapiddweller-benerator-ce
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Welcome to [rapiddweller Benerator](https://www.benerator.de), a powerful software solution for data generation, obfuscation, and migration for development, testing, and training purposes. Our core goals are to provide model-driven test data generation that satisfies complex data validity requirements, anonymize production data for showcases and serious performance testing projects, efficiently generate large data volumes, and offer wide customizability and ease of use for non-developers.
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Welcome to [rapiddweller Benerator](https://www.benerator.de), a powerful
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software solution for data generation, obfuscation, and migration for
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development, testing, and training purposes.
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Our core goals are to provide model-driven test data generation that
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satisfies complex data validity requirements, anonymize production data for
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showcases and serious performance testing projects, efficiently generate large
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data volumes, and offer wide customization possibilities and ease of use for
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non-developers.
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## Quickstart ( use benerator version from releases )
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1. make sure you have installed **Java 11 JDK** or higher and **JAVA_HOME** environment variable is set correctly
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2. download the latest benerator version from [Releases](https://github.com/rapiddweller/rapiddweller-benerator-ce/releases)
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3. unzip .tar.gz to *choose/your/path*
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4.open a terminal (bash / PowerShell) and add environment variable BENERATOR_HOME=*choose/your/path* and add *choose/your/path*/bin to your PATH variable
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4. add environment variable BENERATOR_HOME=*choose/your/path* and add *choose/your/path*/bin to your PATH variable
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For example **(these environment variables are only set in your terminal session, read more about environment variables [here](https://en.wikipedia.org/wiki/Environment_variable))**
And that's it! You're ready to start using rapiddweller Benerator CE. We hope you find it as useful as we do. If you have any questions, please reach out to us on GitHub or visit our website www.benerator.de for more information.
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- Anonymization of production data for showcases and serious performance testing projects
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- Efficient generation of large data volumes, scaling up to companies with billions of customers and Big Data projects
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- Early applicability in projects
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- Efficiency by creating reuseable and extendable data models
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- Efficiency by creating reusable and extendable data models
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- Low Code approach to have more transparency
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- Little maintenance effort with ongoing implementation through configuration by exception
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- Wide and easy customizability
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- Wide and easy customization possibilities
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- Applicability by non-developers
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- Intuitive data definition format
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- Satisfying stochastic requirements on data
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- Extraction and anonymization of production data
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- Supporting distributed and heterogeneous applications
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- Establishing a common data generation platform for different business domains and software systems
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### Data Synthesization
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### Data Synthesis
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Performance test data can be completely synthesized. A basic setup can be imported e.g. from DbUnit files, CSV files and fixed column width files. A descriptor file configures how imported data should be processed and adds completely synthesized data. The processed or generated data finally is stored in the system under test.
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