Glossary - K to N
Data Migration Glossary This data migration glossary has been split into sections for faster loading. You can access the complete glossary by following the link, or use the search engine below to find specific terms. |
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Key
- In a database a key is a field used to sort or speed up access to data. In a relational database records are normally identified by a unique combination of keys or fields.
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LINUX
- A UNIX-like operating system that was designed to provide personal computer users a free or very low-cost operating system comparable to traditional and usually more expensive Unix systems. Linux has a reputation as a very efficient and fast-performing system. Learn more about LINUX here.
Load
- the act of adding (or storing) data stored either in a flat file or computer memory into a database or software system.
Legacy Data
- The data contained within a legacy system. The data is usually current but restrictive to work on as it is likely to be in an old-fashioned format.
Legacy System
- Jargon for an AIS that is currently in use, and initially deployed many years ago, using a computing infrastructure that is several generations old. These systems tend to be critical to the business and cannot be easily replaced or cost-effectively maintained. They are approaching or have reached the end of their practical operational life span.
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Mapping
- A mapping is the relationship between source and target entities. It is an inclusive term than be applied at different levels, from a single field to a complete system.
Mart
- A data warehouse combines databases across an entire enterprise, while data marts are usually smaller and more focussed.They are normally populated from the data warehouse and allow customised usage with faster execution than a data warehouse.
Metadata
Middleware
- Software designed to establish a relationship (including filtering and transformation) between systems.
Migration
- The process of transferring all or part of an AIS's data to another technical infrastructure. The original application may be upgraded or replaced. The business data (and its schema) usually are retained in a significant way.
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Normalise
- In relational database design the process of organising data to avoid duplication is called Normalisation. It involves dividing a database into two or more tables and defining relationships between them. The objective is to isolate data so that modifications of any field can be made just once and then propagated through the rest of the database via the defined relationships. This process reduces storage requirements and ensures data integrity.
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The following websites were useful when compiling this page.