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Data migration is the process of selecting, preparing, extracting, and transforming data and permanently transferring it from one computer storage system to another. . Additionally, the validation of migrated data for completeness and the decommissioning of legacy data storage are considered part of the entire data migrati
Schema migration allows for fixing mistakes and adapting the data as requirements change. They are an essential part of software evolution, especially in agile environments (see below). Applying a schema migration to a production database is always a risk. Development and test databases tend to be smaller and cleaner.
A dry run (or practice run) is a software testing process used to make sure that a system works correctly and will not result in severe failure. [1] For example, rsync, a utility for transferring and synchronizing data between networked computers or storage drives, has a "dry-run" option users can use to check that their command-line arguments are valid and to simulate what would happen when ...
Batch data transformation is the cornerstone of virtually all data integration technologies such as data warehousing, data migration and application integration. [1] When data must be transformed and delivered with low latency, the term "microbatch" is often used. [6]
The application of data virtualization to ETL allowed solving the most common ETL tasks of data migration and application integration for multiple dispersed data sources. Virtual ETL operates with the abstracted representation of the objects or entities gathered from the variety of relational, semi-structured, and unstructured data sources.
Database testing usually consists of a layered process, including the user interface (UI) layer, the business layer, the data access layer and the database itself. The UI layer deals with the interface design of the database, while the business layer includes databases supporting business strategies .