Database design in DBMS for High Performance Analytics Application

Database design in DBMS for High Performance Analytics Application

Database design in DBMS is one of the major factor for application performance and scalability .Lets make this discussion more interesting and practical , I think , I should start with your Favorite Software product . I know you love FaceBook  Right ! Have you experienced the performance of Today’s FaceBook  . Its Amazing now . Even there are videos , So many High Resolution Images and much more . If we go three year back , when people used to use facebook with images and text only  .You must have found  Facebook had improved a lot in its performance while there were extra functionalities now . How is this magic possible? Get the answer in coming section –

Database design in DBMS for High Performance Analytics Application

Importance of Database design in DBMS for High Performance –

The answer of above asked question  is not straight. I mean the performance of any application is not completely database dependent . There so many component like – UI , back end layer and third party API etc which contributes for features an performance in any application .We will only focus on Database design in DBMS in this article . If the design is not good , Integration of new module is really a big headache for performance .Lets go back to our example , If the Database design in DBMS were not good , It would be  very difficult to add video features by FaceBook .  Data Architect is responsible for Database design in DBMS in most of the organization . Here are some tips for Database design in DBMS for faster applications-

1.Non relational Database are best for data science –

While designing keep in mind , If you are making any Analytics application and dealing with Unstructured data . Go for NoSql databases . This will make you work with dynamic schema .You should refer our article comparison of relational database with non relational database .This article will help you in choosing the best NoSql database for your Data Science Project  .

2. Generic Logical design-

More generic more flexible. In fact , there would  be more ease to add more attribute .  I know you are facing some issue to understand this point . Here Generic mean , The data types should not be strictly declared  for example float in generic and int is specialized . You can easily caste INT into FLOAT but vice versa is not easy .

3. Loosely coupled Relations –

Data should be stored in such a way that it can easily be twisted . This segment  mostly covers relational database design .  If you have experience SQL you must have faced Integrity Constraint in Data base .If you want to explore more on Integrity Constraint read the blog content  Integrity Constraint 

4.  Joining is performed at Design in NoSql-

Previously we were dealing with SQL or relational databases .Here when we need to join two tables. We do it on queries . Now we know that in NoSql Databases Join operation is rare . So we have to handle it in design stages .See If I am explicitly mentioning this , It means you have to take care of data flow specially in complex scenario. So when you are designing the NoSql databases you have to take care of all these.

How does Data Scientist Interact with Data ?

Up to now , We have discussed the importance of Database design in DBMS. Along with it we have also gone through few important things that we should keep in mind for designing database in DBMS specially in reference of Data Science .Its time to focus on role of a data scientist or big data engineer over the data and database design .

Suppose  , If you are a data scientist and You are working on a project to make a predictive system for next purchase item for  your client . In this stage your task are –

  1. Fetch the historical data .
  2. Clean the data .
  3. Work on some features and design featured matrix .
  4. Train the Machine learning Model .
  5. Cross validate the model .
  6. Make prediction .
  7. Check your real time accuracy .
  8. Deploy the model if it has reached at your accuracy expectation or repeat from step 4 .

Find the most Critical Area where Performance effects due to Database –

Here in stage 1 , When you are fetching the data . It can be third party source or your own portal data . Now you have to check

1.Is database is designed in the way that It can be processed many time without loosing the performance ? If the answer is in your favor , There is no need to do anything otherwise you have to options . One option is right in case data is not too big , So you can go with In memory database . In second option, You can select new faster NoSQL database where you can drop the data which are going to use frequently .

It will trade off between your requirement and performance . While designing this meta database , You should keep in mind about the principal that we have mentioned about Database design in DBMS .

2. For the second stages , You have to again update you database . So if database is not properly designed , It will effect the performance .In the similar fashion , in other stages you must need this designing approach to make your prediction faster and accurate .

End Notes –

I hope this article has cleared you on : Importance of Database design in DBMS for High performance applications . If you are a data science beginner  , I will advice to read the article How to become a data scientist : A complete guide. This article will show you the complete path to become a Data scientist . See if you choose the wrong path , You will reach a wrong place .If you don’t want to go deep in Data Science , Just want to explore more about Machine Learning . Do not worry , Go the the article What is Machine Learning ?

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Data Science Learner Team

 
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