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Batch processing is a pervasive workload pattern, expressed by a distinct application organization and execution model. It is found across virtually every industry, applied to such tasks as statement generation, bank postings, risk evaluation, credit score calculation, inventory management, portfolio optimization, and on and on. Nearly any bulk processing task from any business sector is a candidate for batch processing. Batch processing is typified by bulk-oriented, non-interactive, background execution. Frequently long- running, it may be data or computationally intensive, execute sequentially or in parallel, and may be initiated through various invocation models, including ad hoc, scheduled, and on-demand. Batch applications have common requirements, including logging, checkpointing, and parallelization. Batch workloads have common requirements, especially operational control, which allow for initiation of, and interaction with, batch instances; such interactions include stop and restart.

rapiddweller 'Benerator' is a software solution to generate, anonymize, pseudonymize and migrate data for development, testing and training purposes. rapiddweller 'Benerator' is easy to learn, integrates into CI Tools like Jenkins and Gitlab CI and allows to create data models on the most abstract level. rapiddweller 'Benerator' allows creating realistic and valid high-volume test data, used for testing (unit/integration/load) and showcase setup. Metadata constraints are imported from systems and/or configuration files. Data can imported from and exported to files and systems, anonymized or generated from scratch. Domain packages provide reusable generators for creating domain-specific data as names and addresses internationalizable in language and region. It is strongly customizable with plugins and configuration options. rapiddweller 'Benerator' is build for Java 1.8 and Java 11. The development is ongoing for Java 11.