29 Sep Understanding Data Structuring
Anyone who handles massive amounts of data must be aware of data structure. Data structuring is a process that organizes data to allow it to be accessed and used by software programs. There are a variety of methods for structuring data however the most popular ones are linked lists, arrays and dictionary. Each one has its own strengths and weaknesses, so it is essential to fully understand each one is the best for your particular requirements.
Linear Data Structures
Depending on the programming language used, linear data structures can differ in their allocation of memory and internal structure as well as the way basic operations (traversal, retrieval, insertion, deletion) are performed. One good example of linear data structures is an array that stores its elements at multiple memory locations that are contiguous, where each element is accessible with an index beginning at 0. They are useful for algorithms that require access to data in a sequential fashion and can be useful for storing lists of data (like dates or addresses) and for performing mathematical calculations.
Non-Linear Data Structures
A tree is an unlinear structure that arranges data in an orderly manner. Each record in a tree has a parent-child relationship that connects it to one or more records below. Each child record may contain data or pointers that lead to other records in the tree. A good example of one is a family tree which has its principal data point at the top, and then branches out into children, grandchildren and the like.
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