Difference Between DBMS and RDBMS

Parameter	DBMS	RDBMS
Acronym for	Data Base Management System	Relational Data Base Management System
Relation	Data is stored in flat file oriented System and no relations are used.	It provides facility to create relation between tables.
Dr. E.F. Codd Rules	Only 3 rules, Out of 12, are followed by DBMS. (from Rule 0 to Rule 12)	Minimum 6 rules, Out of 12, are followed by RDBS. (from Rule 0 to Rule 12)
Data Security	It can’t implement constraints in Tables and has less Security. The ACID (Atomicity, Consistency, Isolation and Durability) property of the database is ensured by Programmer.	It can implement the integrity constraint for the purpose of holding ACID (Atomicity, Consistency, Isolation and Durability) property of the database and is much securing than DBMS.
Generation	3GL	4GL
Users Supported	Single User database, Microsoft Access, Lotus Approach are the best examples of personal database management software.	Multi User Database. E.g. SQL Server can be used my many users on the same time.

Difference Between Database and Data Warehouse

Parameter	Database	Data Warehouse
Definition	Organized Collection of Data is called database.	Database of Databases is Called Data Warehouse.
Purpose	It is designed to allow the definition, creation, querying, update, administration of data (Transaction Processing).	It is a relational database that is designed for query and analysis rather than transaction processing.
Data Type	Data stored in Database is up to date.	Current and Historical Data is Stored in Data Warehouse.
Orientation	Transaction Oriented: e.g. Each Transaction of Bank Customer is written to database and is interpreted as Accounting Database.	Subject Oriented: The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.
Data Redundancy	Databases are designed to avoid redundancy.	Data Warehouse design is intended to introduce redundancy.

Difference Between Data Mining and Machine Learning

Parameter	Data Mining	Machine Learning
Definition	Data mining is a process to extract information from a data set and transform it into an understandable structure for further use.	It is a Technique that concerns the construction and study of systems that can learn from data.
Focus	It focuses on the discovery of unknown properties in the data.	It focuses on prediction, based on known properties learned from training data.
Database Size	It is an automatic or semi-automatic analysis that is performed on large quantities of data.	It is generally performed on small databases to increase accuracy.
Types	Association Rules Classification Clustering Sequential Patterns Sequence Similarity	Supervised Un Supervised Reinforcement

Database Management System (DBMS) Interfaces

DBA Interface: This interface is used by the database administrator to create databases, Users, to set system parameters for changing database schema etc. Fig. given below shows admin menu where DBA is can create new user and can assign privileges to him.

Graphical User Interface: This interface represents database

Classification of Database Management System

Database Management System can be classified on several DBMS criteria.

A) On the Basis of Data Models.

Data model is a collection of high level data description that hides many low level storage details. A data Model has following three Components:
1) A Structural Part that consist of a set of rules according to which database is constructed.
2) A Manipulating Part That defines type of operations that are allowed on data.
3) A set of integrity rules that ensures that data is correct.
The Data Models are further divided into Three Categories: 

I) Object Base Data Models: Object based data models defines a database in terms of objects, their properties, and their operations .Objects of same behavior and structure belongs to a class and classes are organized into hierarchies. The operations of each class are specified in terms of predefined procedures called methods. Commonly used object based data models are:
Entity Relationship, Object Oriented, Semantic, Functional

II) Physical Data Models: Physical data models include all data artifacts that are

DBMS Languages

 DBMS Support a variety of users and provides appropriate languages and interface or each category of users.Here are some languages that are provided by DBMS:
1) Data Definition Language (DDL): This Language is used define data structures and specially database schemas. these statements are used to create, alter, or drop data structures. ALTER ,CREATE ,DROP are some examples of DDL.

2) Storage Definition Language (SDL): This language is used to define internal schema. It defines that what will be the Physical structure of database, How many bites per field will be used, what will be the order of fields, and how records will be accesses etc.

3) View Definition Language (VDL): This language is used to specify user views and their mapping to conceptual schema. It defines the subset of records available to classes of users. It creates virtual tables and the view appears to users like conceptual level.It specifies user interfaces.

4) Data Manipulation Language (DML): It is used at conceptual level and external level and is used to perform operations like Query, Delete Update or Insert.Read Only Queries are also sometimes considered as component of  DML.It modifies the data but not schema or database objects. These languages are further divided into two types:

Database Normalization

Database Normalization is the process of organizing data in a database by eliminating redundancy and inconsistent dependency. it divides large tables into smaller tables ,which are less redundant , and defines relationships between them.The Main motive is to isolate data so that additions, deletions, and modifications of a field is made in just one table and then propagated through the rest of the database using the defined relationships.

Database normalization uses few rules . Each rule is called a “normal form.” If the first rule is observed, the database is said to be in “first normal form.” If the first three rules are observed, the database is considered to be in “third normal form.” Although other levels of normalization are possible, third normal form is considered the highest level necessary for most applications.

Normalization Form	Description	Example
First normal form (1NF)	It is a property of a relation in a relational database. A relation is in first normal form if the domain of each attribute contains only atomic values, and the value of each attribute contains only a single value from that domain	For example, to track an inventory item that may come from two possible sources, an inventory record may contain fields for Vendor Code 1 and Vendor Code 2.