Data Structures
Data Structures are the programmatic way of storing data so that data can be used efficiently.
Almost every enterprise application uses various types of data structures in one or the other way.
This tutorial will give you a great understanding on Data Structures needed to understand the
complexity of enterprise level applications and need of algorithms, and data structures.
The data structure is not any programming language like C, C++, java, etc. It is a set of
algorithms that we can use in any programming language to structure the data in the memory.
To structure the data in memory, 'n' number of algorithms were proposed, and all these
algorithms are known as Abstract data types. These abstract data types are the set of rules.
Data Definition
Data Definition defines a particular data with the following characteristics.
• Atomic − Definition should define a single concept.
• Traceable − Definition should be able to be mapped to some data element.
• Accurate − Definition should be unambiguous.
• Clear and Concise − Definition should be understandable.
Why to Learn Data Structure and Algorithms?
As applications are getting complex and data rich, there are three common problems that
applications face now-a-days.
• Data Search − Consider an inventory of 1 million(106) items of a store. If the
application is to search an item, it has to search an item in 1 million(106) items every
time slowing down the search. As data grows, search will become slower.
• Processor speed − Processor speed although being very high, falls limited if the data
grows to billion records.
• Multiple requests − As thousands of users can search data simultaneously on a web
server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized
in a data structure in such a way that all items may not be required to be searched, and the
required data can be searched almost instantly.
Types of Data Structures
There are two types of data structures:
o Primitive data structure
o Non-primitive data structure
Primitive Data structure
, The primitive data structures are primitive data types. The int, char, float, double, and pointer are
the primitive data structures that can hold a single value.
Non-Primitive Data structure
The non-primitive data structure is divided into two types:
o Linear data structure
o Non-linear data structure
Applications of Data Structure and Algorithms
Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a
certain order to get the desired output. Algorithms are generally created independent of
underlying languages, i.e. an algorithm can be implemented in more than one programming
language.
From the data structure point of view, following are some important categories of algorithms −
• Search − Algorithm to search an item in a data structure.
• Sort − Algorithm to sort items in a certain order.
• Insert − Algorithm to insert item in a data structure.
• Update − Algorithm to update an existing item in a data structure.
• Delete − Algorithm to delete an existing item from a data structure.
Data Structures are the programmatic way of storing data so that data can be used efficiently.
Almost every enterprise application uses various types of data structures in one or the other way.
This tutorial will give you a great understanding on Data Structures needed to understand the
complexity of enterprise level applications and need of algorithms, and data structures.
The data structure is not any programming language like C, C++, java, etc. It is a set of
algorithms that we can use in any programming language to structure the data in the memory.
To structure the data in memory, 'n' number of algorithms were proposed, and all these
algorithms are known as Abstract data types. These abstract data types are the set of rules.
Data Definition
Data Definition defines a particular data with the following characteristics.
• Atomic − Definition should define a single concept.
• Traceable − Definition should be able to be mapped to some data element.
• Accurate − Definition should be unambiguous.
• Clear and Concise − Definition should be understandable.
Why to Learn Data Structure and Algorithms?
As applications are getting complex and data rich, there are three common problems that
applications face now-a-days.
• Data Search − Consider an inventory of 1 million(106) items of a store. If the
application is to search an item, it has to search an item in 1 million(106) items every
time slowing down the search. As data grows, search will become slower.
• Processor speed − Processor speed although being very high, falls limited if the data
grows to billion records.
• Multiple requests − As thousands of users can search data simultaneously on a web
server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to rescue. Data can be organized
in a data structure in such a way that all items may not be required to be searched, and the
required data can be searched almost instantly.
Types of Data Structures
There are two types of data structures:
o Primitive data structure
o Non-primitive data structure
Primitive Data structure
, The primitive data structures are primitive data types. The int, char, float, double, and pointer are
the primitive data structures that can hold a single value.
Non-Primitive Data structure
The non-primitive data structure is divided into two types:
o Linear data structure
o Non-linear data structure
Applications of Data Structure and Algorithms
Algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a
certain order to get the desired output. Algorithms are generally created independent of
underlying languages, i.e. an algorithm can be implemented in more than one programming
language.
From the data structure point of view, following are some important categories of algorithms −
• Search − Algorithm to search an item in a data structure.
• Sort − Algorithm to sort items in a certain order.
• Insert − Algorithm to insert item in a data structure.
• Update − Algorithm to update an existing item in a data structure.
• Delete − Algorithm to delete an existing item from a data structure.
