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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

Top 10 Databases to Use in 2025

Yana Khare
Yana Khare Last Updated : 21 Nov, 2024
12 min read

Introduction

Many database technologies in contemporary data management meet developers’ and enterprises’ complex and ever-expanding demands. From scalable, high-performance solutions for distributed systems to reliable transaction handling in business contexts, each system offers distinct benefits catered to different data processing requirements. Achieving the best data management results and choosing the appropriate solution for a given application requires understanding various databases’ strengths and use cases. This article explores the characteristics, functions, and applications of 10 well-known databases.

Overview:

  • Learn about various databases that a person could use in 2024.
  • Discover databases perfect for your suitability.

Table of contents

Let’s explore the top 10 databases to use in 2024!

1. PostgreSQL

Application

PostgreSQL is widely used in many sectors for its flexibility and extensive feature set. It is the foundation for sophisticated searches, data integrity, and sophisticated functionalities, including full-text search, JSON support, and geographic data management for enterprise-level applications. Because of its extensibility, users may add unique operators, types, and functions to make it more flexible to meet changing demands.

Use Cases

  • Enterprise Applications: PostgreSQL is perfect for financial apps, ERP, and CRM systems that need strong data management and transaction support.
  • Data Warehousing: PostgreSQL’s capacity to manage extensive datasets and facilitate intricate queries renders it appropriate for data warehousing and business intelligence applications.
  • Analytical Workloads: PostgreSQL is a suitable option for reporting and data analysis because of its sophisticated indexing strategies, which include GIN, B-tree, and GiST indexes, which maximize performance for analytical queries.

Key Features

  • ACID Compliance: ACID compliance ensures consistent and dependable transactions.
  • Robust SQL Support: Complies closely with SQL specifications and facilitates sophisticated queries.
  • Extensibility: Extremely flexible, allowing users to design their own operators, functions, and data types.
  • Sturdy Performance: Designed to handle massive data sets using various indexing and partitioning techniques.
  • Multi-Version Concurrency Control (MVCC): It improves performance by permitting several transactions to occur simultaneously without locking.
  • Full-Text Search: Enables robust text search across data sets using Full-Text Search.
  • Foreign Data Wrappers (FDW): Using FDW makes integrating data sources and databases easier.

2. MySQL

MySQL is a popular open-source RDMS. It is renowned for its stability, speed, and user-friendliness. MySQL AB originally designed it in 1995. Oracle Corporation eventually purchased it. MySQL is a web application and dynamic website mainstay because of its solid performance and ease of use. It offers many storage engines, including InnoDB, which complies with ACID standards and supports SQL for various use cases.

Application

Web-based applications and systems that need quick, dependable access to structured data tend to select MySQL. Developers have cemented its usage in web development by accepting it and ensuring its compatibility with web technologies such as PHP, Apache, and Linux (the LAMP stack). MySQL is adaptable to many situations because of its ecosystem, which provides tools for database administration, performance tweaking, and data replication.

Use Cases

  • Web Applications: Developers frequently use MySQL to store and manage data for online applications, including social networking platforms, forums, and content management systems like WordPress.
  • E-commerce Platforms: MySQL is a good choice for e-commerce applications that process transactions and manage inventories because it quickly handles complicated queries.
  • Logging: MySQL is useful for logging systems that store and retrieve vast amounts of log data because of its fast data insertion capabilities.

Key Features

  • High Performance: Designed for jobs involving a lot of reading and quick data access.
  • Scalability: Using replication and clustering techniques supports horizontal scaling.
  • Assistance with Complex Queries: Offers a potent SQL engine that can manage intricate joins, subqueries, and transactions.
  • Multiple Storage Engines: This option lets you choose the best storage engine (InnoDB, MyISAM, etc.) for your needs.
  • Replication: It provides group and master-slave replication for high availability and disaster recovery.
  • Easy to Use: It is well-known for its simple setup, integration with various development environments, and installation.

3. SQLite

Lightweight, serverless, and self-contained, SQLite is an RDBMS emphasizing ease of use and integration. Created by D. Richard Hipp in 2000, it doesn’t need a separate server process because it runs entirely inside an application. Because of its portability and negligible configuration requirements, it is the go-to database for embedded devices and small-scale applications.

Application

Developers frequently integrate SQLite into applications, prioritizing ease of use, low resource use, and deployment simplicity. Because of its design, it’s an excellent fit for applications on mobile devices, embedded systems, and tiny websites where a standard client-server database architecture could be too complex. SQLite makes backup and deployment easier by storing the whole database as a single file on the disk.

Use Cases

  • Mobile Apps: SQLite is included in the iOS and Android operating systems, offering local storage for mobile apps requiring offline functionality.
  • Small to Medium Websites: This choice is perfect for sites that don’t require the overhead of a full-fledged RDBMS and have moderate traffic and data storage requirements.
  • IoT Devices: Developers find SQLite a good fit for embedded systems and IoT devices that need local data storage because of its small footprint and simplicity of integration.

Key Features

  • Lightweight: Requires little space and little resources.
  • Serverless: This does not require a separate server process; it functions directly within the application.
  • Self-Contained: It is simple to deploy and integrate since all functions are contained in a single library.
  • Simple API: Enables seamless integration by offering a straightforward API for several programming languages.
  • Zero-Configuration: It is suitable for apps that need quick installation and do not require complicated setup or management.
  • Portable: Facilitates the transfer of databases between platforms by storing data in a cross-platform file format.
  • Transactions: Provides consistency and dependability in data operations by supporting ACID-compliant transactions.

4. MongoDB

MongoDB is one of the most well-known NoSQL databases. It uses a document-oriented storage format called BSON, comparable to JSON. MongoDB Inc. initially released it in 2009. MongoDB was designed to extend horizontally across distributed systems and manage enormous volumes of unstructured data. Because MongoDB is schema-less, it can support dynamic data structures, making it appropriate for applications that must be flexible and iterate quickly.

Application

When the data format has to change quickly, and standard relational databases would be too restrictive, MongoDB shines. Its document model supports many kinds of intricate data. Hence, it is perfect for real-time analytics, Internet of Things applications, and contemporary online applications. Because of its distributed design, MongoDB offers high availability and redundancy through horizontal scalability.

Use Cases

  • Content Management Systems: Users can easily manage rich material, such as blogs, articles, and multimedia, using MongoDB’s flexible schema, which supports various content types and formats.
  • Real-Time Analytics: MongoDB suits social media analytics, recommendation engines, and monitoring applications well because it can manage high-throughput data intake and deliver real-time data analytics.
  • IoT Applications: MongoDB suits IoT applications that produce diverse and extensive data streams. This is because of its scalability and capacity to manage enormous amounts of unstructured data.

Key Features

  • Flexible Schema: Facilitates data structure modifications without requiring migrations by supporting dynamic schemas.
  • High Performance: Designed for applications requiring much work, this system is optimized for high read and write throughput.
  • Horizontal Scalability: High scalability and availability are achieved by distributing data across several nodes via sharding.
  • Document-Oriented Storage: Facilitates the representation of intricate hierarchical connections by storing data in BSON format.
  • Rich Query Language: An effective query language that facilitates geographic, aggregate, and indexing.
  • Built-In Replication: Replica sets are provided for high availability, and automated failover is provided.
  • Aggregation Framework: This framework functions similarly to SQL’s GROUP BY and JOIN procedures and permits sophisticated data processing and modification.

5. Microsoft SQL Server

Microsoft created Microsoft SQL Server, a powerful RDBMS. It accommodates many data management requirements, from tiny applications to massive business systems. Since its 1989 introduction, SQL Server has been a seamless part of the Microsoft ecosystem. Additionally, it also offers a full range of data management, analytics, and business intelligence tools.

Application

SQL Server is a widely used option in commercial environments. This is due to its interoperability with other Microsoft products, capability for complex transactional systems, and data warehousing features.
Its sophisticated security features, robust analytics, and reporting capabilities make it the business’s go-to option.

Use Cases

  • Enterprise-Grade Applications: These include CRM and ERP systems and other applications needing sophisticated transaction processing and high dependability.
  • Business Intelligence: SQLServer’s interaction with products like Power BI and SQL Server Reporting Services (SSRS) supports advanced analytics and data visualization.
  • Data Warehousing: SQL Server can handle large-scale data warehousing and big data analytics thanks to capabilities like PolyBase and column-store indexes.

Key Features

  • Integration with the Microsoft Ecosystem: Seamless communication with Visual Studio, Azure, Active Directory, and more Microsoft services and products.
  • Robust Analytics: Enables ETL processes using SQL Server Integration Services (SSIS) and data mining with SQL Server Analysis Services (SSAS) and other tools.
  • High Availability: This service offers assistance with disaster recovery and high availability features. It also includes Always-On availability groups, database mirroring, and clustering.
  • Scalability and Performance: Enhances performance with partitioning, compression, and in-memory OLTP features.
  • Comprehensive Management Tools: This package contains all-inclusive management tools, including SQL Server Management Studio (SSMS) for database management and oversight.

6. Oracle Database

Oracle Corporation created the very advanced RDBMS Oracle Database. It is renowned for its extensive feature set, excellent performance, and resilience. Since its launch in 1979, Oracle Database has dominated the database industry. It provides solutions for complex applications with incredible scalability, security, and reliability. It can do many different things, such as transaction processing and data warehousing.

Application

Large-scale corporate settings prioritizing high performance, security, and dependability frequently employ Oracle databases. Large-scale ERP solutions, data warehousing, and financial systems may all benefit from its broad feature set and support for intricate, mission-critical applications.

Use Cases

  • Data Warehousing: Oracle’s robust analytics, partitioning, and parallel processing capabilities make it an excellent choice for large-scale data warehousing and business intelligence.

Key Features

  • High Reliability:  It offers features including Automatic Storage Management (ASM) for effective data management, Oracle Real Application Clusters (RAC) for high availability, and Oracle Data Guard for disaster recovery.
  • Security: Provides cutting-edge security features, including data masking, auditing, and encryption.
  • Extensive Feature Set: This includes advanced features like PL/SQL, Advanced Queueing, and support for many data formats (JSON, XML, and geographic data).
  • Performance Optimization: It uses technologies like the Automatic Workload Repository (AWR), Automatic Database Diagnostic Monitor (ADDM), and Oracle Exadata for optimal hardware integration and performance adjustment.
  • Scalability: Provides capabilities for managing massive datasets, such as partitioning and sharding, and supports vertical and horizontal scalability.
  • Integration with Cloud: Oracle Cloud Infrastructure (OCI) offers Oracle Autonomous Database integration and hybrid cloud deployment options, which enable automatic scaling and administration.
  • Multi-Model Support: This feature manages various data forms and kinds, including relational, document, key-value, and graph data.

7. Redis

Application

Redis is frequently used when high throughput and quick data access are essential. Its architecture allows it to handle millions of read-and-write requests per second. Hence, it is perfect for session management and real-time analytics caching. Redis’s simplicity and speed have made it a go-to solution for enhancing the performance of web applications by offloading tasks from primary databases.

Use Cases

  • Caching: Redis is widely used as a caching layer to store frequently requested data. This helps to improve application response times and lessens the strain on core databases.
  • Session management: It allows for fast retrieval of information updates by storing user session data in web applications.
  • Real-time Analytics: This database works well with recommendation engines, financial trading platforms, and monitoring systems, among other applications.

Key Features

  • Ultra-fast data access: In-memory storage offers high throughput and very low latency for both read and write operations.
  • Simple Key-Value Storage: Provides simple data storage by mapping keys to different data structures.
  • Support for Complex Data Structures: Redis can handle bitmaps, lists, sets, sorted sets, hashes, bitmaps, hyperloglogs, and geographic indexes in addition to essential key-value pairs.
  • Options for Persistence: To guarantee the longevity of data, append-only file (AOF) persistence and snapshotting are supported.
  • Pub/Sub Messaging: This feature allows real-time broadcast via a publish/subscribe messaging system.
  • Replication: Enables high availability and catastrophe recovery by allowing data replication across several nodes.
  • Atomic Operations: Guarantees data consistency without requiring transactions by offering atomic operations on supported data types.

8. Cassandra

With no single point of failure, Apache Cassandra is a distributed NoSQL database that is highly scalable and built to manage massive volumes of data over several commodity computers. First created at Facebook and released as an open-source project in 2008, Cassandra has gained renown for its fault tolerance, high availability, and horizontal scalability. Because it uses a columnar storage style, it is effective for workloads requiring a lot of writing on big datasets.

Application

Users employ Cassandra in settings where high write throughput and constant availability are required. Its design provides resilience against regional outages, enabling the dispersion of data across several data centers. Applications needing high-speed writes and the capacity to manage massive, dispersed data might benefit from Cassandra’s schema flexibility and time-series data support.

Use Cases

  • High-Write Throughput Workloads: Most effective for data stores that have a significant amount of writes, including messaging, recommendations, and real-time analytics.

Key Features

9. Elasticsearch

It can index and search data quickly, so it is frequently used in applications that involve processing large volumes of log and event data, search tools and functionalities, real-time analytics, and data visualization. Another aspect is the possibility of using Logstash and Kibana (part of the Elastic Stack) tools for data visualization and monitoring to enhance Elasticsearch’s capabilities.

Use Cases

Key Features

10. CockroachDB

CockroachDB provides an advanced distributed SQL foundation with high consistency, robust support for fault recovery, and dealer-backed functional solidity across geologically distributed workflows. Founded by Kyle Avers and Bruce McGilton in 2015, it aims to mimic the Relational SQL model but can offer a NoSQL database’s scalability and fault tolerance.

Application

Use Cases

Key Features

Conclusion

The diversity of database systems underscores the importance of choosing the right tool for specific application needs. Each database system’s unique features and applications make it indispensable for achieving specific business objectives. Thus, reflecting the evolving landscape of data management technologies.

Yana Khare
Yana Khare

A 23-year-old, pursuing her Master's in English, an avid reader, and a melophile. My all-time favorite quote is by Albus Dumbledore - "Happiness can be found even in the darkest of times if one remembers to turn on the light."

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