Liquid DataMapper simplifies the process of database mapping, if you are looking for a fast and reliable solution, our data mapping tool allows you to graphically map between xml databases and then transform your data instantly.
What Is Data Mapping?
Database mapping is the process of creating data element mappings between two distinct data models, usually the source and the target. For example, the application which is to receive (read) the data is known as the target, and the data model that is to specify the layout of the input, is known as the source.
Data models usually include meta data, an atomic unit of data which carries a precise meaning in terms of semantics. A good example of data mapping would include moving the value from a ‘contact’ field in a sales database to a ‘name’ field in a customer database.
There are also many other terms for this depending on the operating platform and computer language being used, however data mapping is one of the most widely used.
Methods of Data Mapping
A number of different methods exist to carry out data mapping, from XSLT transforms, to procedural code to using a graphical mapping tool like Liquid DataMapper to automatically generate executable transformation programs.
Graphical database mapping is by far the earliest and most common form of data mapping, also known as transformation logic, and involves creating applications purely for data mapping. Some of the more advanced tools allow you to auto-connect the source and target, and almost all allow you to visually map your fields by drawing lines (manual data mapping) from fields in your source data to fields in your target data through a graphical interface.
Data driven mapping is a fairly new technique and allows data values in two different sources to be evaluated simultaneously, using heuristics and statistics to discover exceptions that transformation logic cannot discover, as well as case statements, substrings, concatenations and arithmetic.
Semantic mapping is pretty much comparable to the auto-connect feature of graphical data mappers, it utilises the metadata registry to lookup different data element synonyms, for example, in the case above where we mentioned ‘contact’ and ‘name’ field, the mapping would still be made if both elements were listed as synonyms in the registry.
However semantic mapping is constrained to exact matches between two columns of data and cannot find exceptions or transformation logic between columns.
Why Use Data Mapping?
Due to the onset of graphical mapping tools and the ease at which non technical types can now carry out mapping tasks, database mapping has increased in popularity with a projected yearly increase of 30%. Some common applications include data transformation, eg between source and target data, data consolidation eg parent company consolidating customer data with child company, and data masking eg concealing last digits of a credit card or national insurance number.
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Data Mapping Resources
• Data mapping tutorial (coming soon)
• Data mapping videos (coming soon)
• Data mapping books (coming soon)