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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
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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
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 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

Cassandra vs MongoDB: Which NoSQL Databases to Choose?

Hari Bhutanadhu
Hari Bhutanadhu 31 Jul, 2023
7 min read

Introduction

In NoSQL databases, Cassandra and MongoDB stand out as versatile solutions for handling vast volumes of unstructured data. Businesses seeking real-time data management and agility are turning to these alternatives, leaving traditional RDBMS systems behind. This article delves into the strengths of Cassandra vs MongoDB, assisting users in making informed choices based on their specific application needs, workload patterns, and desired consistency levels.

This article was published as a part of the Data Science Blogathon.

Cassandra vs MongoDB
Source: Jelvix.com

Table of contents

What is Cassandra?

Facebook initially developed Cassandra, which is now maintained by the Apache Software Foundation. It remains popular for large data applications due to its high availability, scalability, and fault-tolerant distributed design with no single point of failure. Cassandra supports data replication across multiple servers, making it ideal for write-intensive applications.

What is MongoDB?

MongoDB, created by MongoDB Inc., is a document-oriented database system known for its scalability and flexibility. Handling unstructured data is effortless as it stores data in JSON-like documents with dynamic schemas. MongoDB simplifies data storage and retrieval without complex joins or schema changes. It optimizes read-intensive applications with automatic sharding for horizontal scaling.

Cassandra vs. MongoDB: Overview

FeatureCassandraMongoDB
Database TypeWide-column storeDocument-oriented store
Data ModelSchema-agnosticJSON-like documents with dynamic schemas
ScalabilityHighly scalable, designed for horizontal scalingHorizontally scalable with automatic sharding
Consistency ModelTunable consistency levels (from strong to eventual)Strong consistency within a single replica set
Read PerformanceOptimized for write-intensive applicationsOptimized for read-intensive applications
Write PerformanceHigh write throughput with efficient write operationsSupports efficient writes, but not as high as Cassandra
Data ReplicationMulti-master replication across multiple data centersReplica sets with automatic failover
Fault ToleranceHighly fault-tolerant with no single point of failureSupports automatic failover with replica sets
Query LanguageCQL (Cassandra Query Language)MongoDB Query Language (MQL)
IndexingSecondary indexes supportedSecondary indexes and compound indexes supported
JoinsNo support for traditional joinsNo support for traditional joins
Schema EvolutionSchema changes require data migration and planningFlexible schema with no need for data migration
Use CasesTime-series data, sensor data, IoT applicationsContent management systems, real-time analytics
Community SupportStrong open-source community supportWell-established community and commercial support

Cassandra vs. MongoDB: The NoSQL Databases

  • Data model: MongoDB uses a document data model where data is stored in documents, similar to JSON whereas Cassandra uses a column-family data model where data is stored in rows with columns grouped into column families.
  • Scalability: Both databases can manage massive data sets by adding more nodes to the group because they are highly scalable. However, Cassandra needs human partitioning and tuning, while MongoDB uses automatic scalability, making scale easier.
  • Consistency: Cassandra can accept some data errors in exchange for improved availability because it emphasizes texture. Reads always give the most recent write in MongoDB, which provides strong consistency by default.
  • Performance: MongoDB is optimized for read-heavy tasks, and Cassandra is optimized for mostly write-intensive tasks. The storage engine that Cassandra employs, the log-structured merge tree (LSM-tree), is efficient for writes but can be slow for reads. MongoDB uses a read- and write-optimized document-oriented storage engine.
  • Applications: Cassandra is often used for high-volume, high-speed applications that need scalability and quick writes, such as social networking sites and IoT devices. Applications with flexible data models and fast reads, such as content management systems and e-commerce websites, frequently use MongoDB.

Cassandra vs MongoDB: Data Model and Query Language

One of the most crucial parts of any database system is the query language, followed by the data model. These are some critical distinctions between Cassandra and MongoDB’s data schema and query language:

Cassandra vs MongoDB
Source: Data Flair
  • Data Model: Cassandra uses a column-family data model, where data is saved in rows with columns organized into column families, whereas MongoDB uses a document-based data model, where data is stored in documents. Every document in MongoDB is allowed to have a unique structure; a predefined schema is not required. On the other hand, the columns and column families that will be used to store the data must be defined in advance for Cassandra.
  • MongoDB has a flexible and potent query language called the MongoDB Query Language (MQL). Filtering, aggregating, and sorting are elements of MQL that facilitate extensive document queries. The Aggregation Framework, a secondary query language supported by MongoDB, enables more complex data processing and analysis.
  • Indexing:MongoDB offers a variety of indexing options, including single-field, multi-field, and geospatial indexes, to maximize query performance. While Cassandra does not support multi-field indexes and geospatial indexing, it does provide secondary indexes on column values.
  • MongoDB offers ACID (Atomicity, Consistency, Isolation, Durability) compliance at the document level, ensuring the consistency and longevity of each document. On the other hand, Cassandra offers eventual consistency, which means that modifications could take some time to spread among the cluster’s nodes.

Comparison of Data Replication and Consistency Models

Each database system’s performance and consistency are directly affected by its data replication and consistency models, which are essential components. This is a comparison of Cassandra and MongoDB’s data replication and consistency models:

  • Data Replication: For high reliability and fault tolerance, Cassandra and MongoDB both provide data replication. With Cassandra’s masterless architecture, data is replicated across numerous nodes in a ring topology. The number of copies of the data stored throughout the cluster depends on the replication factor, and each node is in charge of a specific data set. The master-slave architecture used by MongoDB designates one node as the primary node to which all writes are directed. One or more secondary nodes can be used for reading activities after the primary node replicates data.
  • Consistency Models: Cassandra and MongoDB employ many consistency model strategies. Tunable consistency is a feature of Cassandra that allows users to select the degree of consistency needed for each read or write operation. Consistency is broken down into four groups: quorum, all, one, and any. Most nodes must agree on the data in a quorum, the most common consistency level before a response can be given. Strong consistency is a feature that MongoDB, by default, offers, making writes immediately visible to all reads. Moreover, MongoDB supports eventual consistency, which is helpful for applications where high availability is more crucial than data freshness.
  • Resolution of Conflicts: Conflicts may occur when multiple nodes simultaneously change the same piece of data in distributed systems. The most recent update is given precedence in Cassandra’s last-write-wins conflict resolution system. MongoDB has various ways to solve errors, including using timestamps or version numbers to identify the most recent update.
NoSQL Databases
Source: Severalnines

Performance and Scalability Comparison

Performance:

  • Cassandra has quick write times and efficient data storage, making it perfect for tasks that include much writing. It uses a distributed architecture with a peer-to-peer architecture that allows fault tolerance and horizontal scaling.
  • MongoDB offers fast query rates and flexible machine learning, making it ideal for workloads involving much reading. It enables managing unstructured or primarily structured data easier by using a document-oriented data architecture that stores data in documents that resemble JSON.

Scalability:

  • Cassandra is designed to scale horizontally, allowing the addition of extra nodes to a cluster and the equitable distribution of data among them. This makes it a strong option for large-scale, fast-moving data tasks requiring high availability and fast writes.
  • Moreover, MongoDB offers sharding, which divides data among different servers and enables horizontal scaling. It could need more proper management and configuration to provide the best performance and scalability.

Choosing Between Cassandra vs MongoDB

The choice between Cassandra and MongoDB will be based on a number of factors, including the specific needs of your business application, the architecture of your data, your query patterns, and your need for scalability.

Best Practices:

  • Consider scalability when creating your data model, keeping in mind both the expansion of your data and the demands of your query patterns.
  • To improve query performance, use the appropriate indexing and partitioning techniques.
  • To ensure peak performance, regularly check the performance of your database and make any improvements.
  • Use the appropriate replication and backup techniques to ensure high availability and data durability.

Conclusion

In conclusion, Cassandra and MongoDB are popular NoSQL databases designed to handle a large amount of unstructured Data. And the choice between Cassandra and MongoDB depends on the application’s specific needs, including the type of data being stored, the query patterns, and the desired consistency level. For high-volume, high-velocity applications that need quick writes and scalability, Cassandra is frequently a preferable option, even though MongoDB may be more versatile in terms of the data type and query language.

Key takeaways

  • We have seen the definition and overview of Cassandra and MongoDB.
  • And the Key differences in Data Model and Query Language are also a comparison of Data Replication and Consistency Models.
  • Performance and Scalability Comparison of two and factors to Consider between two and Best Practices.

Frequently Asked Questions

Q1. Is MongoDB better than Cassandra?

A. The choice between MongoDB and Cassandra depends on the specific use case and requirements. MongoDB is better suited for flexible data models and complex queries, while Cassandra excels in high availability and scalability for distributed systems.

Q2. Why is Cassandra faster than MongoDB? 

A. Cassandra’s superior speed is attributed to its distributed architecture, which allows data to be distributed across multiple nodes, reducing read and write latencies. It also employs a decentralized approach, ensuring high performance in massive-scale deployments.

Q3. Is Cassandra the same as MongoDB?

A. No, Cassandra and MongoDB are two different NoSQL databases with distinct features and use cases. Cassandra is designed for scalability and fault tolerance in distributed systems, while MongoDB focuses on flexibility and ease of development.

Q4. Can MongoDB replace Cassandra? 

A. It depends on the specific requirements of the application. While MongoDB can serve as a replacement for Cassandra in certain scenarios, such as when the focus is on flexibility and simplicity, the decision should be based on the specific needs and demands of the project.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

ApplicationsblogathonCassandradata modelGuideMongoDBqueryreplicationuses
Hari Bhutanadhu
Hari Bhutanadhu 31 Jul, 2023

My self Bhutanadhu Hari, 2023 Graduated from Indian Institute of Technology Jodhpur ( IITJ ) . I am interested in Web Development and Machine Learning and most passionate about exploring Artificial Intelligence.

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