Abstract: The systems and method described provide a dynamic and automated process for producing test data for cloud migrations. Metadata for transformation designs with attribute classification and relationship data can be used to generate an enriched design-driven data set. The test data can then be used to validate infrastructure provisioned in multi-cloud environments and accommodate most cloud providers. The system can avoid tedious testing cycles that use non-relevant test data (i.e., unrelated to the specific design requirements).

Abstract: An automated machine learning-based system and method of managing content for data migrations. The system can process bulk tabular datasets and real-time legacy application parameters. The data can then be segmented into segments based on an estimated time for execution of the data migration for the given dataset generated by a machine learning model. In some embodiments, the system can automatically generate a code that can perform the data migration based on the proposed segment segmentation. The proposed embodiments are effective in reducing downtime during migrations as well as limiting the impact of failure events on the process.

Abstract: The systems and method described provide a dynamic and automated process for producing test data for cloud migrations. Metadata for transformation designs with attribute classification and relationship data can be used to generate an enriched design-driven data set. The test data can then be used to validate infrastructure provisioned in multi-cloud environments and accommodate most cloud providers. The system can avoid tedious testing cycles that use non-relevant test data (i.e., unrelated to the specific design requirements).