Relational Database vs Non Relational Database with Special Reference to Data science

Relational Database vs Non Relational Database

When you are doing any project , Choosing Right Technologies is really not easy . I mean , You have to go for so many research work and you have to read so many blogs ,Books etc. Specially when you are dealing with Data Science .In Data Science , You have to make so many queries for Analytics . This can make your System slow . So Choosing the right Database is very very important . The reason behind this is most of  the application goes slow at Database end . Now the question get up “Relational Database vs Non Relational Database“.

Don’t worry! Here in this Article , You will get all the required information to answer “Relational Database vs Non Relational Database”.If you are dealing with computer science , You must know the basics of Data Base .In early days when we say Data base, By default we were saying  Relation Database but in this time , this definition is changing very fast . Now if you simply mention Database word , there will be a reverse question for you  “Relational Database vs Non Relational Database”.Without wasting time , I will directly Introduce you with Non Relational Database .

Relational Database vs Non Relational Database

What is Non Relational Database ?-

This is similar question to “What is NoSql Database ?”.  The Database where we retrieve or insert in non tabular fashion is basically Non Relational Database. Basically here , Data Model is different from Relational Data model  . You must have heard about MongoDB. This is NoSql Database . How can I forget to tell you ! This type of Databases are not following SQL protocols . That is why these are called NoSql databases. I am really sorry that I have used NoSql Database too many times earlier without telling to you that Non relational Database is same as NoSql Database . Anyways I am going to mention few others NoSql Databases –

  1. Apache Cassandra.
  2. Neo4j
  3. Redis
  4. Apache Hbase

These are Popular NoSql Database . I think we should explore some more on their popularity . Here is a Google Trend report on Popularity on Non Relational Database over the time Please check it out .

Non relation database popularity chart
relational database vs non relational database

This graph shows clearly MongoDB is most Popular with Time . I think till now you have completely  understood all Introductory information about NoSql Database or Non relational Database . Now we can go for “Relational Database vs Non Relational Database”.Before it as it is mentioned that we are going to compare “Relational Database vs Non Relational Database”  with special reference to Data Science . I know most of you basics in Data science and Machine Learning.

For those who does not have clear understanding about Data Science .They go for the article How to become a Data Scientist : What required ?

Relational Database vs Non Relational Database-

If we start any comparison  , First task is to decide the comparison ground . I mean those factors or Matrix which is used in comparison . So Lets go one by one on them and explore it –

1.Difference in Data Modeling-

In relational Databases ,We store data in TABULAR FORMAT , I mean Row Column is must . Here each Row holds a record and column is attribute of any entity  .Where In opposite , If are dealing with NoSql Databases ,There are four different Data Model –

1.Document Databases.

2.Graph Stores.

3.Key Stores.

4. Wide Column Stores.

These different Data Modeling favors Unstructured Data  Manipulation in NoSql Database   or Non Relational Database.

2.Flexibility –

In relational database no  row should be blank . We have to pre decide the scheme . I mean what attribute is going to be in use ,It is to be decided on prior basis . In run time we can not handle it . Where in No SQL , Schema remain Dynamic . It indicates that you can have change in schema at run time .

3. Scalibilty Cost-

When you want to scale a Relational Database System . It is more difficult and costly . Where NoSql is easy to scale because of its distributive nature . These days every application is playing with humongous data . Number of users are increasing for every application whether it  is Google or Facebook . In that Scenario scalability is most important concern which goes Higher with NoSql .

4. Data Base ACID Property-

ACID means Atomic , concurrency , Isolation and Durability basically these are the properties of Transaction .See SQL databases are quite strict than NoSql . When you have to increase scalability and response time you have to trade off some where .

So these were the factors which makes difference between these two  type of Databases . I think now you have clear understanding on Relational Database vs Non Relational Database .Before Winding up this article , I will discuss one more thing about uses of NoSql Database for Unstructured Data .

NoSql Database for Unstructured Data-

If you look around  , Unstructured data is every where .Whether Facebook or Twitter every Top Application is based on completely Unstructured data . If we start dealing these application with Oracle or any RDBMS what will be the result, you can  imagine with the this example  . if you define comment in String then you can not insert any image on this .It is because You have to pre decide the schema . This Dynamic Schema Feature helps NoSql Databases to integrate with real world Application involving unstructured data .

Popular Relational Databases-

These three four lines are dedicated for those  who are still confuse about Relational Databases and their examples. I will mention few popular Relational Databases  . This will improve your understanding and you can recall your prior knowledge about Relational Databases.

1.Oracle DataBase.

2 MySql Database .

3. IBM DB2 .

Among all relational Databases Oracle and MySql comes at top . These are having tremendous strength of documentations . Which helps to manipulate application with these database   .

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Meet Abhishek ( Chief Editor) , a data scientist with major expertise in NLP and Text Analytics. He has worked on various projects involving text data and have been able to achieve great results. He is currently manages, where he and his team share knowledge and help others learn more about data science.
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