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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Types of database management system and their evolution

Tavish Srivastava
Tavish Srivastava Last Updated : 26 Jul, 2020
5 min read

Various researches have revealed that whenever we hear an object, we retrieve it using an image from our brain. For instance, if I ask you “Do you like apples?” . You won’t think of the alphabets “a”, “p”, “p”,”l”,”e”, rather you will recall all relevant information regarding apple using the image of an apple. This is how possibly our data processing in our brain work. Of course it is complicated and highly efficient. Now if ask you “What is a database?”, you will mostly think of a collection of tables related to each other. If the same question was posed to a person in 1990s, possibly he would have only thought of a single big table from which they retrieve all the information.But today we have really moved on from this simplistic definition of databases.

This article will help you understand what type of database exist in this industry currently. I also did an interesting exercise of plotting these database types in a tree structure with a time axis. This will help you visualize how database definition and application changed with time.

Evolution of Database Management System

We are too used to timelines on Facebook. I thought it will be a good idea to start this article on a similar line. Following is a tree which will help you map all types of popular database management system in a timeline:

DBs table

The timeline varies from 1980s to current date and is not exhaustive of all forms of data management systems. However, we will be able to cover most of the popular data management system.

Flat File Database 

This is probably the easiest to understand but at present rarely used. You can think of this as a single huge table. Such type of datasets were used long back in 1990s, when data was only used to retrieve information in case of concerns. Very primitive analytics were possible on these database.

Relational Database

Soon people starting realizing that such tables will be almost impossible to store on a longer run. The Flat File brought in a lot of redundant data at every entry.  For instance, if I want to make a single data-set with all products purchased at a grocery store with all information of the customer and product, we will have every single row consisting of all customer and product information. Wherever we have a repeat product or customer, we have repeat data. People thought of storing this as different tables and define a hierarchy to access all the data, which will be called as hierarchical database.

Hierarchical Database is very similar to your folder structure on the laptop. Every folder can contain sub-folder and each sub-folder can still hold more sub-folders. Finally in some folders we will store files. However, every child node (sub-folder) will have a single parent (folder or sub-folder). Finally, we can create a hierarchy of the dataset :

hierarchicalHierarchical databases, however can solve many purposes, its applications are restricted to one-to-one mapping data structures. For example, it will work well if you are using this data structure to show job profile hierarchy in a corporate. But the structure will fail if the reporting becomes slightly more complicated and a single employee reports to many managers. Hence, people thought of database structures which can have different kinds of relations. This type of structure should allow one-to-many mapping. Such table came to be known as Relational database management system  (RDBMS).

Following is an example RDBMS data structure :

RBDBMS

As you see from the above diagram, there are multiple keys which can help us merge different data sets in this data base. This kind of data storage optimizes disc space occupied without compromising on data details. This is the data base which is generally used by the analytics industry. However, when the data looses a structure, such data base will be of no help.

NoSQL Database

NoSQL is often known as “Not Only SQL”. When people realized that unstructured text carry tonnes of information which they are unable to mine using RDBMS, they started exploring ways to store such datasets. Anything which is not RDBMS today is loosely known as NoSQL. After social networks gained importance in the market, such database became common in the industry. Following is an example where it will become very difficult to store the data on RDBMS :

Facebook stores terabytes of additional data every day. Let’s try to imagine the structure in which this data can be structured :

FB strIn the above diagram, same color box fall into same category object. For example the user, user’s friends, who liked and Author of comments all are FB users. Now if we try to store the entire data in RDBMS, for executing a single query which can be just a response of opening home page, we need to join multiple tables with trillions of row together to find a combined table and then run algorithms to find the most relevant information for the user. This does not look to be a seconds job for sure. Hence we need to move from tabular understanding of data to a more flow (graph) based data structure. This is what brought NoSQL structures. We will discuss NoSQL databases in our next article. We will also compare various types of NoSQL databases to understand the fitment of these databases.

End Notes

Databases form the foundation of analytics industry. Even if we don’t know each one of them in detail, we should have an overview of the entire spectrum of databases. In this article we discussed the popular types of datasets and how the need of databases evolved with time. In the next article we will continue the same discussion and take it a step further by understanding types of NoSQL databases.

Did you find the article useful? Share with us your experiences with different types of databases. Do let us know your thoughts about this article in the box below.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

 

Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Nitin
Nitin

You guys are doing a great job, every time I visit your blog I am clear on few more things be it the career decisions or the actual analytic concepts. The blog posts are to the point and concise.

123

Sunil
Sunil

Good One....I liked this

123
Santhana
Santhana

I am an Analytics student and looking forward to your next article. Great for learning .. Appreciate if you could include more use cases and examples for various types of NOSql DBS. Thanks

123

Poorna
Poorna

Very informative...

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Silvia Evodi
Silvia Evodi

sure

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