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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

NoSQL Databases : Simplified

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

My father always hesitates while making big ticket transaction online. He is always scared of machine making an error. Just imagine that you transfer your entire financial asset from your one account to other and because of some data error, it gets transferred to someone else’s account. However, I do not hesitate even by for a second making any kind of transactions online. What has triggered such big difference in our perceptions. Probably the reason is the high accuracy in such transactions which I have witnessed since my early age. What makes these infinite number of transactions done in banks, with airlines, with E-commerce such accurate?

To a great extent this is because of RDBMS, which strictly adheres to ACID (atomicity, consistency, isolation, and durability) principles. But with time because of the high pressure of growing data, we have begun using NoSQL databases. NoSQL eliminates the need of a schema, hence pushing the data handling capacity by a huge margin by compromising on ACID. However, with accurate supporting applications in place, NoSQL provide a good combination of high data handling capacity with good accuracy. In this article we will discuss popular NoSQL databases.no sql databases

Image Credit : http://www.tomsitpro.com

Full form of NoSQL?

NoSQL actually has no full form or even an actual meaning. The term was coined accidentally at an event by Johan Oskarsson to discuss open-source distributed network. The “#NoSQL” was just a twitter hash tag to organize this meeting. Then a few database people at this meeting found this hashtag appropriate to name these non relational database. We really do not have any strict definition for NoSQL. However, following are some common features of NoSQL databases :

1. They are not relational

2. Mostly open-source

3. They are all cluster-friendly

4. They are schema less

5. They emerged out of 21st century Web world

However, we often refer to NoSQL as schema less, this does not mean that these database do not adhere to any kind of schema. For example consider the following NoSQL expression :

Tab1[“Revenue” ] * Tab1[“Total count”]

NoSQL has an implicit schema, which might not be constant throughout the database. Such thing is both a boon and a curse. The base thing is whenever we want to modify a field we need to understand this implicit schema. The good thing is that with a changing schema it asks for much lesser effort to append this database compared to RDBMS. Also RDBMS is not great with a distributed network which is not the case with NoSQL.

Types of NoSQL databases

However, in literature NoSQL has been broken down into 4 major types, I found a very interesting way suggested by Martin Fowler to categorize NoSQL. Based on the way NoSQL stores data, it is primarily of two types :

1. Aggregate based Database

2. Graph based Database

The primary difference between the two is that in aggregate type, database tries to store all the information for a particular ID (this can be an individual or transaction or product etc.) as a single object. Whereas graph type follows the exact opposite philosophy. Graph type database tries to cut the data into highly granular information and stores them with all the shared relations or edges. We will discuss the aggregate based databases, which are more common today, in this article.

Aggregate based Databases

Let’s take an example to understand this concept of aggregate based database. Following is how an article on analyticsvidhya looks like :

ArticleIf we were using RDBMS to store our data for Analytics Vidhya, we would have created relational tables. One can be for Author related information, other can be about the article related information and even other can correspond to each categories. But when we open an article, we need all these informations together. Hence, we are more interested in aggregate information and not such granular information. However, granular information could have given me a better scope of analysis, for this purpose I might be more interested in minimizing the loading time of webpage. Hence, we need a database in which this entire information is stored at a single place. This can be termed as aggregate oriented databases.

Key-value Databases / Document Databases :

Key-value Databases and Document Databases are very similar. You can think of Document Databases as a nested form of Key-value databases. Following is a simple key-value database :

keyvalue

Image Credit: http://nosql.rishabhagrawal.com

Now if I want to fetch information on Ipod 16 GB, I just need to provide the ket “13a2” to the database. Following is how a document database will look like :

document

Note that at every document we have aggregate information on that id. Link this back to our analyticsvidhya example. In our case we will have all author name, title name etc. as key-value pairs on every document. Now I can pull the entire document in one go, as it is stored at the same object.

Column-oriented Databases :

Imagine that you have a RDBMS orders table with 1 Million rows and 100 columns. Now you want to pull all the customer names with orders of more than $500. You essentially need command on only two columns, but to do this query you will essentially have to browse through all 100 columns. Column oriented databases give a solution to this problem. A detailed discussion on this type of database is out of scope for this article, but what you need to understand is just the underlying concept. Following is a simple example of row-oriented and column-oriented database :

column-oriented-database

Image Credit: www.timestored.com

Column-oriented database stores each column at separate location which enables us to only reach out to those columns which are of need to us. This makes queries much faster in search and filter operations.

End Notes

NoSQL databases are extending our limits to store and analyze data. With such schema less structures they also enable us to change variables/attributes at any point in time. They are exceptionally fast to execute queries on these aggregate entities. However,  this statement comes with a caption that in case the operation has to be done not on the the aggregate level of table, it becomes more complicated as compared to RDBMS. For instance, if we have data stored for each order in NoSQL aggregate database. Any query which has to do something with these order id will be exceptionally fast. But in case you need information on customer or product level, these tables might not be very efficient. For such processes you will need to write MapReduce queries. Hence, it all depends on the type of use as to which database will suit you more.

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

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column orientedDocument basedGraph databaseHBaseKey-Value databaseMongoDBNeo4JNoSQLRedis
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

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Gaurav
Gaurav

Nice Article Tavish... Thanks

123

Kumar Chinnakali
Kumar Chinnakali

Got the glimpse of NoSQL database. Interesting part is two classification of NoSQL with respect to storage. Aggregated(Key-Value, Document, Column) & Graph. It Helps !

123

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