Data redundancy: All problem of it with their right solution

Data redundancy is a condition that occurs when the intention is to store the same piece of data in two separate places. It happens within the database or storage places of your computer.

You get multiple copies of the same data due to the abnormal behavior of the database. If you find yourself ticked off in such a situation, don’t worry.  In this article, I address all of the possible ways to recover your database back to efficient work.

Anomalies in a computer database:

Anomalies are problems that can occur in poorly planned non-standard databases, where all data store in an array (flat-file database)

The easiest way to avoid update anomalies is to refine the concepts of the devices represented by the data.

Prevention of Anomalies:

It is necessary to normalize the database by effectively organizing the data in a database to eliminate anomalies.

According to Edgar F. Codd, the inventor of relational databases, the goals of normalization are:

  • Remove some redundant (or iterative) data from the database.
  • Remove unwanted entries, updates, and distance dependencies.
  • The need to restructure the entire database every time new fields added.
  • Make the relationship between tables more helpful and understandable.

Normalization is an evergreen solution of Anomalies:

Normalization reduces the space in a database and ensures that data is stored efficiently. Without standardization, database systems can be inaccurate, slow, and inefficient. They may not provide the Data you expect.

It means that the database should modify to meet the following requirements:

  • Every table must have a primary key.
  • Each data record must have unique (atomic) value attributes/columns.
  • There shouldn’t be repetitive clusters of information.

Importance of keys over a database design creation:

Keys used to establish and identify relationships between tables and to uniquely identify a record or row of data in a table. A key can be a single attribute or a group of Attributes (a synthetic primary key), and the combination can act as a key. Using keys, we can identify each row of data.

4 Key fields to create a bug-free database:

data redundancy problems

There are four types of Key fields when designing a database:

  1. Primary key: The field chosen by the database creator to uniquely identify each element of a table. For example, each song in your music database might have a master key field called “song_id”.
  2. Alternate key: a field with unique values ​​that can be used as a primary key but not currently specify as a primary key; B. Artist_id.
  3. Foreign Key: A field that contains the values ​​of the primary key field from another table. Foreign keys use to indicate the relationship between different tables. For example, each song in your music database might have a foreign key field called “artist_id”, which links the song to a specific artist on an “artist table”.
  4. Composite key:  A combination of more than one field that uniquely identifies each record in a table. E.g. B. song_id and artist_id.

Insertion Anomaly (Solved!):

It is a difference in input that happens with specific features. It cannot include in the database without the presence of other attributes.

The solution of Insertion Anomaly (Deletion and Update anomalies) is Normalization.

What is exact normalization? Normalization breaks connections into well-structured relationships that allow users to insert, delete and update tuples without entering a database. Without normalization, many problems can arise when attempting to load an integrated conceptual model into DBSM. 

Besides, it can use to resolve the anomaly of update and deletion that I discuss further. Although, various types of normalization put in action to resolve the suitable anomaly. 

Deletion Anomaly (Solved!):

A discrepancy deletes when you delete an entry that may have attributes that are not possible to delete.

To avoid such update or edit issues, you should split the original table into several smaller tables, each with minimal overlap with other tables. It is another way to apply normalization in your database.

Update anomaly (Solved!):

An update discrepancy is a data inconsistency caused by data redundancy and a partial update.

Inside update anomaly. You may need to edit incorrect data, including changing many records, with the possibility that some changes may be inaccurate.

The problem that arises is an inaccurate update of information in the database. You again require to divide the table into separate portions that allow more accuracy to gauge or resolve the update bugs.