Before starting with Normalization, we need to understand the concept of anomalies, why do data anomalies occur in a database.

Many kinds of anomalies occur during updation, insertion, deletion.

Updation Anamolies occur when one of the data fields is updated, which causes the previous value to be deleted.


A database built using a relational model is a relational database. A relational database management system manages relational databases. A relational database has ACID properties for its transactions.

A transaction in a relational database is atomic, which means it is just one activity that cannot be divided into smaller activities.

If a user transfers money from one account to another, this activity either does not happen or happens completely.

If the money is deductedfrom one account, the other account must receive the money.

A: Atomicity- Every Transaction is atomic. It either happens completely or not at all.

C: Consistent- All the three properties makes the data in a relational database consistent.

I: Isolation- Any two transactions cannot take place at the same time.

D: Durability- If the transaction is completed, changes made to the database due to that transaction remains.


In relational models, every entity and relation of an E-R model is a table. These tables are relations in a relational modelbecause all the tables are interconnected through common attributes.

A relational model stores data in the form of a table or a relation. …


Entity-Relationship models are useful for both database designers and business users. ER models help business users understand whether or not a database is designed according to the business requirements. ER models help us answering following questions:

  • What are entities and attributes?
  • What is a relation in an ER model?
  • What…

Data management is the ability of an organization to manage its data using various technologies. It also ensures that only authorized users can access the data within an organization. …


Clustering is an unsupervised learning technique where we try to find patterns based on similarities in the data.

There are two most commonly used types of clustering algorithms — K-Means Clustering and Hierarchical Clustering.

In unsupervised learning, we are not interested in prediction because we do not have a target…

Anuradha Mohanty

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