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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

Difference Between Non Relational Database and Relational Database

Ayushi Trivedi
Ayushi Trivedi Last Updated : 30 Jul, 2024
3 min read

Introduction

Just imagine that you are the head of café, which is under heavy attendance of the clients, and all of them are ordering their favorites and asking about the state of the preparation of their order frequently. For everything to be well coordinated you require tools that will effectively manage the orders, stocks and the customer relations. Likewise, when it comes to the process of working with large amounts of data, the choice between a non-relational database and a relational one is quite similar to choosing the right tools for the café. Both systems have their advantages and it is necessary to use one or another depending on the certain circumstances. In achieving the objective of this guide, you will have adequate knowledge to choose which data management system is most appropriate.

Difference Between Non Relational Database and Relational Database

Learning Outcomes

  • Understand the basic concepts of non-relational databases and relational database.
  • Identify the key differences between non-relational databases and relational database.
  • Learn the advantages and limitations of both systems.
  • Explore real-world examples and use cases of non-relational databases and relational database.
  • Gain insight into the selection criteria for choosing between non-relational databases and relational database.

Table of contents

What is a Non-Relational Database?

A non-relational database which is commonly termed as NoSQL is defined to store data in several models such as key-value, Document, Column-Family as well as Graph model. Contrary to relational databases, these bases do not have a fixed structure; that is to say that they are free-thinking and can expand. It is especially useful when working with the information that can be categorized as ‘text,’ ‘picture,’ or referred to as rich data containing interconnected relations.

What is a Relational database?

A Relational Database is a type of data base where information is stored in Rows and Columns. In an relational database, data is stored in table, and their associations can be defined with the help of primary/foreign keys. It allows for creating and using complicated SQL statements and gives information consistency by ACID basis properties. relational databases found application in all the areas that involve complicated and most credible transaction processing and data integrity standards.

Differences Between Non-Relational Databases and Relational Database

FeatureNon-Relational Database (NoSQL)Relational Database
Data StructureFlexible, various data models (key-value, document, graph, etc.)Structured, uses tables with rows and columns
SchemaSchema-less, allowing for dynamic and flexible data storageSchema-based, requires a predefined schema
Data RelationshipsManaged within application logicExplicitly supports relationships using primary and foreign keys
Query LanguageVaries by database type, often uses APIs or query languages specific to the databaseUses SQL (Structured Query Language)
ACID PropertiesMay not fully support ACID properties, depends on the specific NoSQL databaseFully supports ACID properties
Data IntegrityManaged by the application, less emphasis on enforcing constraintsEnforced through constraints and relationships
ScalabilityHighly scalable, supports horizontal scalingScales vertically, horizontal scaling is more complex
NormalizationLess emphasis on normalization, suitable for unstructured dataStrong emphasis on normalization to reduce redundancy
Complex QueriesLess efficient for complex queries, designed for specific use casesOptimized for complex queries and data manipulation
Transaction ManagementBasic or limited transaction support, varies by databaseRobust transaction management
PerformanceOptimized for high performance with large volumes of unstructured dataOptimized for performance with structured data and complex queries
ExamplesMongoDB, Cassandra, Redis, Neo4jMySQL, PostgreSQL, Oracle, Microsoft SQL Server
Backup and RecoveryVaries by database, often manual or with basic toolsAdvanced automated backup and recovery mechanisms
SecurityBasic security features, varies by databaseAdvanced security features, including user roles and permissions

Examples of Non-Relational Databases and Relational Database

Non-Relational Database Example (Document Model – MongoDB)

{
  "company": {
    "employees": [
      {"employeeId": 1, "name": "John Doe"},
      {"employeeId": 2, "name": "Jane Smith"},
      {"employeeId": 3, "name": "Jim Brown"}
    ],
    "departments": [
      {"departmentId": 101, "departmentName": "HR"},
      {"departmentId": 102, "departmentName": "IT"}
    ],
    "projects": [
      {"projectId": 201, "projectName": "Project A", "employeeId": 1},
      {"projectId": 202, "projectName": "Project B", "employeeId": 2}
    ]
  }
}

Relational Database Example

Employees Table:

EmployeeIDNameDepartmentID
1John Doe101
2Jane Smith102
3Jim Brown101

Departments Table:

DepartmentIDDepartmentName
101HR
102IT

Projects Table:

ProjectIDProjectNameEmployeeID
201Project A1
202Project B2

Conclusion

It is essential to consider the disparities of non-relational databases and relational database to define the appropriate database for an application. Relational databases involve structured data that has not changed or that possess awesome relational capabilities, while non-relational store unstructured information through flexibility and scalability. They are both appropriate for specific uses, and each is good at what it does. So based on these criteria it would be possible to make a right decision about which database system should be implemented at the organization.

Frequently Asked Questions

Q1. What is the main difference between non-relational databases and relational database?

A. Non-relational databases use various data models and are schema-less. While relational database uses a tabular structure with predefined schemas and relationships.

Q2. Which is better for handling complex queries, non-relational databases or relational database?

A.  Relational database is better for handling complex queries due to its support for SQL and relational capabilities.

Q3.  Do both non-relational databases and relational database support ACID properties?

A. Relational database fully supports ACID properties, whereas non-relational databases may not.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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