1. Relational Database Relational databases are incredibly popular because of their structured nature, ability to manage large amounts of data, and well-established ecosystem! Data is organized into tables with columns of fixed data type. Relationships between rows are established using Foreign Keys (FKs). These databases are well-suited for structured data with well-defined relationships, maintaining data integrity, and constraints! Example: Postgres, MySQL, Oracle, etc. Some common use cases of Relational Databases are as follows ~ E-Commerce — Tracking customer data, orders, and inventory Finance — For managing transactions, account details, etc. Healthcare — Maintaining patient records, appointments, and billing information 2. Wide Column Database Wide-Column databases are NoSQL databases that store data in flexible columns that can be spread across multiple servers or database nodes. Though they might feel similar to relational databases, they are very different from them! Here, the name and format of the columns can vary across rows, even within the same table. Such databases offer low-latency querying speeds, high scalability, and a flexible data model. They are well-suited for cases where writes far exceed reads, data is rarely updated, and there is no need for joins or aggregate. Example: Cassandra, ScyllaDB, DynamoDB, etc. Some common use cases of Wide Column Databases are as follows ~ Big-data and analytics High-write throughput 3. Time-Series Database Time-Series databases (TSDB) are optimized for measurements sampled and aggregated over time. Examples of time-series data include server metrics, application performance monitoring, network data, sensor data, events, clicks, trade-in market, and many more! A TSDB is responsible for managing data life-cycle, summarisation, and large range scan of many records. They also support time-series aware queries. Example: InfluxDB, Prometheus, Kdb+, etc. Some common use cases of Time-Series Databases are as follows ~ Financial Trading Platforms Performance and Application Monitoring 4. Ledger Database Ledger databases are designed predominantly for accounting data. It can store events and the istorical value of a company’s financial data. While small companies can do away with other database technologies, large companies with high frequency and volumes of financial transactions require a purpose-built database like the Ledge database. Key features of ledger databases include immutability and cryptographically verifiable log of data changes. Transactions are validated by a central authority and stored using digital signatures. Example: Amazon Quantum Some common use cases of Ledger databases are as follows ~ Financial Applications Supply Chain Management Voting Systems 5. Graph Database As the name suggests, graph databases store data as nodes, relationships, and properties. Designed for structureless data, graph databases are good for things like social networks and geospatial data. Leveraging the graph structure, graph databases enable efficient traversal, querying, and analysis of interconnected data. Example: Neo4j, ArangoDB, Amazon Neptune, etc. Some common use cases of Graph Databases are as follows ~ Social Networks Knowledge Graphs Recommendation Systems 6. Object-Oriented Database (ODBMS) Object-oriented databases (ODBMS) draw their inspiration from OOP. They store data as objects, similar to how certain programming languages manage data. Data objects in ODBMS encapsulate complex data structures and their associated actions. Such databases can easily represent intricate data models without requiring multiple tables & joins. They heavily make use of inheritance and polymorphism. Example: ObjectDB, db4o, etc. Some common use cases of Object-Oriented Databases are as follows ~ Object-Oriented Applications Multi-Media Databases 7. Hierarchical Database A Hierarchical database is a DBMS that organizes data in a tree-like structure, with records connected by link. Each record has a single parent record but can have multiple children records. Hierarchical databases were commonly used in the early days of computing, where their tree-like structure was well-suited for organizing file systems with directories and files. However, over time, they have been largely supplanted by more flexible database models, such as relational and NoSQL databases, which provide better support for complex relationships and greater overall versatility. Example: IBM IMS, Windows Registry, etc. Some common use cases of Hierarchical Databases are as follows ~ File Systems 8. Document Database Document databases are used to store & query data as JSON-like documents. Flexible, semi-structured, and hierarchical, document database offers ease of development and performance at scale. Most of the web applications that communicate using JSON find it very natural to integrate document databases as the data format conversion is not required. Example: MongoDB, ArangoDB, CouchDB Some common use cases of Document Databases are as follows ~ Content Management Systems E-Commerce Platform 9. Key-Value Database Key-value stores are NoSQL database that stores data as a collection of key-value pairs. They are well-suited for applications that require fast response and serve high volumes of data. They are easy to scale and support flexible schema. Their most common use case is for caching. Example: Couchbase, DataStax, Redis Some common use cases of Key-Value databases are as follows ~ Application Level Caching Session Storages 10. Blob Database Blob databases are used for storing unstructured data in binary format. Such databases are most suited for storing media files and documents. Blob databases are optimized for storing large amounts of data that do not fit into standard database schemas. Example: Amazon S3 Some common use cases of Blob databases are as follows ~ Multi-media storage for applications Content Delivery Networks 11. In-Memory Database These are purpose-built databases that rely primarily on internal memory for data storage. They strive to achieve minimum response time by eliminating disk accesses. In-memory databases are most suited for applications that require microseconds response time or have large spikes in traffic. They offer low latency, high throughput, and high scalability. Example: Redis, Memcached, Apache Ignite, Aerospike, Hazlecast Some common use cases of In-Memory Databases are as follows ~ Caching Real-time bidding Gaming Leaderboard 12. Text Search Database Text Search databases are meant for storage, retrieval, and analysis of large volumes of textual data efficiently. They support complex text queries and inverted indexes. Example: Elastic Search Some common use cases of Text Search Databases are as follows ~ Web Searches Auto-Complete and Recommendations Filtering 13. Spatial Database Spatial Databases enhance traditional database functionality to manage complex spatial data types — like points, lines, polygons, and other geometric shapes — along with their related attributes and relationships. Example: PostGIS, Oracle Spatial, SpatiaLite Some common use cases of Spatial Databases are as follows ~ Geo-Information Systems Location Based Services Spatial Analysis 14. Vector Database Vector databases are used to store, index, and search high-dimensional data points called vectors. Vectors are used to represent several things from numerical features, embeddings from texts/images, and complex data like molecular structures. These databases use advanced indexing techniques for fast retrievals and similarity searches. They are often optimized for AI and machine learning use cases. Example: Pinecone, Chroma Some common use cases of Vector Databases are as follows ~ Image and Video Search Recommendation Systems 15. Embedded Database Embedded databases are lightweight, specialized databases built directly into software applications, offering seamless integration. Unlike traditional client-server databases that operate as separate processes, embedded databases run within the application itself, enabling faster data access, a smaller footprint, and easier deployment. These databases are especially valuable in environments with limited resources, where the complexity and overhead of a full client-server database would be unnecessary or impractical. Example: SQLite, RocksDB, BerkeleyDB Some common use cases of Embedded Databases are as follows ~ Desktop Applications Quick Proof-Of-Concepts That’s it! I hope this info is useful for you. 1. Relational Database 1. Relational Database Relational databases are incredibly popular because of their structured nature, ability to manage large amounts of data, and well-established ecosystem! Data is organized into tables with columns of fixed data type. Relationships between rows are established using Foreign Keys (FKs). These databases are well-suited for structured data with well-defined relationships, maintaining data integrity, and constraints! Example: Postgres, MySQL, Oracle, etc. Example: Postgres, MySQL, Oracle, etc. Example: Example: Postgres, MySQL, Oracle, etc. Some common use cases of Relational Databases are as follows ~ E-Commerce — Tracking customer data, orders, and inventory Finance — For managing transactions, account details, etc. Healthcare — Maintaining patient records, appointments, and billing information E-Commerce — Tracking customer data, orders, and inventory E-Commerce Finance — For managing transactions, account details, etc. Finance Healthcare — Maintaining patient records, appointments, and billing information Healthcare 2. Wide Column Database 2. Wide Column Database Wide-Column databases are NoSQL databases that store data in flexible columns that can be spread across multiple servers or database nodes. Though they might feel similar to relational databases, they are very different from them! Here, the name and format of the columns can vary across rows, even within the same table. Such databases offer low-latency querying speeds, high scalability, and a flexible data model. They are well-suited for cases where writes far exceed reads, data is rarely updated, and there is no need for joins or aggregate. Example: Cassandra, ScyllaDB, DynamoDB, etc. Example: Cassandra, ScyllaDB, DynamoDB, etc. Example: Example: Cassandra, ScyllaDB, DynamoDB, etc. Some common use cases of Wide Column Databases are as follows ~ Big-data and analytics High-write throughput Big-data and analytics Big-data and analytics High-write throughput High-write throughput 3. Time-Series Database 3. Time-Series Database Time-Series databases (TSDB) are optimized for measurements sampled and aggregated over time. Examples of time-series data include server metrics, application performance monitoring, network data, sensor data, events, clicks, trade-in market, and many more! A TSDB is responsible for managing data life-cycle, summarisation, and large range scan of many records. They also support time-series aware queries. Example: InfluxDB, Prometheus, Kdb+, etc. Example: InfluxDB, Prometheus, Kdb+, etc. Example: Example: InfluxDB, Prometheus, Kdb+, etc. Some common use cases of Time-Series Databases are as follows ~ Financial Trading Platforms Performance and Application Monitoring Financial Trading Platforms Financial Trading Platforms Performance and Application Monitoring Performance and Application Monitoring 4. Ledger Database 4. Ledger Database Ledger databases are designed predominantly for accounting data. It can store events and the istorical value of a company’s financial data. While small companies can do away with other database technologies, large companies with high frequency and volumes of financial transactions require a purpose-built database like the Ledge database. Key features of ledger databases include immutability and cryptographically verifiable log of data changes. Transactions are validated by a central authority and stored using digital signatures. Example: Amazon Quantum Example: Amazon Quantum Example: Example: Amazon Quantum Some common use cases of Ledger databases are as follows ~ Financial Applications Supply Chain Management Voting Systems Financial Applications Financial Applications Supply Chain Management Supply Chain Management Voting Systems Voting Systems 5. Graph Database 5. Graph Database As the name suggests, graph databases store data as nodes, relationships, and properties. Designed for structureless data, graph databases are good for things like social networks and geospatial data. Leveraging the graph structure, graph databases enable efficient traversal, querying, and analysis of interconnected data. Example: Neo4j, ArangoDB, Amazon Neptune, etc. Example: Neo4j, ArangoDB, Amazon Neptune, etc. Example: Example: Neo4j, ArangoDB, Amazon Neptune, etc. Some common use cases of Graph Databases are as follows ~ Social Networks Knowledge Graphs Recommendation Systems Social Networks Social Networks Knowledge Graphs Knowledge Graphs Recommendation Systems Recommendation Systems 6. Object-Oriented Database (ODBMS) 6. Object-Oriented Database (ODBMS) Object-oriented databases (ODBMS) draw their inspiration from OOP. They store data as objects, similar to how certain programming languages manage data. Data objects in ODBMS encapsulate complex data structures and their associated actions. Such databases can easily represent intricate data models without requiring multiple tables & joins. They heavily make use of inheritance and polymorphism. Example: ObjectDB, db4o, etc. Example: ObjectDB, db4o, etc. Example: Example: ObjectDB, db4o, etc. Some common use cases of Object-Oriented Databases are as follows ~ Object-Oriented Applications Multi-Media Databases Object-Oriented Applications Object-Oriented Applications Multi-Media Databases Multi-Media Databases 7. Hierarchical Database 7. Hierarchical Database A Hierarchical database is a DBMS that organizes data in a tree-like structure, with records connected by link. Each record has a single parent record but can have multiple children records. Hierarchical databases were commonly used in the early days of computing, where their tree-like structure was well-suited for organizing file systems with directories and files. However, over time, they have been largely supplanted by more flexible database models, such as relational and NoSQL databases, which provide better support for complex relationships and greater overall versatility. Example: IBM IMS, Windows Registry, etc. Example: IBM IMS, Windows Registry, etc. Example: Example: IBM IMS, Windows Registry, etc. Some common use cases of Hierarchical Databases are as follows ~ File Systems File Systems File Systems 8. Document Database 8. Document Database Document databases are used to store & query data as JSON-like documents. Flexible, semi-structured, and hierarchical, document database offers ease of development and performance at scale. Most of the web applications that communicate using JSON find it very natural to integrate document databases as the data format conversion is not required. Example: MongoDB, ArangoDB, CouchDB Example: MongoDB, ArangoDB, CouchDB Example: Example: MongoDB, ArangoDB, CouchDB Some common use cases of Document Databases are as follows ~ Content Management Systems E-Commerce Platform Content Management Systems Content Management Systems E-Commerce Platform E-Commerce Platform 9. Key-Value Database 9. Key-Value Database Key-value stores are NoSQL database that stores data as a collection of key-value pairs. They are well-suited for applications that require fast response and serve high volumes of data. They are easy to scale and support flexible schema. Their most common use case is for caching. Example: Couchbase, DataStax, Redis Example: Couchbase, DataStax, Redis Example: Example: Couchbase, DataStax, Redis Some common use cases of Key-Value databases are as follows ~ Application Level Caching Session Storages Application Level Caching Application Level Caching Session Storages Session Storages 10. Blob Database 10. Blob Database Blob databases are used for storing unstructured data in binary format. Such databases are most suited for storing media files and documents. Blob databases are optimized for storing large amounts of data that do not fit into standard database schemas. Example: Amazon S3 Example: Amazon S3 Example: Example: Amazon S3 Some common use cases of Blob databases are as follows ~ Multi-media storage for applications Content Delivery Networks Multi-media storage for applications Multi-media storage for applications Content Delivery Networks Content Delivery Networks 11. In-Memory Database 11. In-Memory Database These are purpose-built databases that rely primarily on internal memory for data storage. They strive to achieve minimum response time by eliminating disk accesses. In-memory databases are most suited for applications that require microseconds response time or have large spikes in traffic. They offer low latency, high throughput, and high scalability. Example: Redis, Memcached, Apache Ignite, Aerospike, Hazlecast Example: Redis, Memcached, Apache Ignite, Aerospike, Hazlecast Example: Example: Redis, Memcached, Apache Ignite, Aerospike, Hazlecast Some common use cases of In-Memory Databases are as follows ~ Caching Real-time bidding Gaming Leaderboard Caching Caching Real-time bidding Real-time bidding Gaming Leaderboard Gaming Leaderboard 12. Text Search Database 12. Text Search Database Text Search databases are meant for storage, retrieval, and analysis of large volumes of textual data efficiently. They support complex text queries and inverted indexes. Example: Elastic Search Example: Elastic Search Example: Example: Elastic Search Some common use cases of Text Search Databases are as follows ~ Web Searches Auto-Complete and Recommendations Filtering Web Searches Web Searches Auto-Complete and Recommendations Auto-Complete and Recommendations Filtering Filtering 13. Spatial Database 13. Spatial Database Spatial Databases enhance traditional database functionality to manage complex spatial data types — like points, lines, polygons, and other geometric shapes — along with their related attributes and relationships. Example: PostGIS, Oracle Spatial, SpatiaLite Example: PostGIS, Oracle Spatial, SpatiaLite Example: Example: PostGIS, Oracle Spatial, SpatiaLite Some common use cases of Spatial Databases are as follows ~ Geo-Information Systems Location Based Services Spatial Analysis Geo-Information Systems Geo-Information Systems Location Based Services Location Based Services Spatial Analysis Spatial Analysis 14. Vector Database 14. Vector Database Vector databases are used to store, index, and search high-dimensional data points called vectors. Vectors are used to represent several things from numerical features, embeddings from texts/images, and complex data like molecular structures. These databases use advanced indexing techniques for fast retrievals and similarity searches. They are often optimized for AI and machine learning use cases. Example: Pinecone, Chroma Example: Pinecone, Chroma Example: Example: Pinecone, Chroma Some common use cases of Vector Databases are as follows ~ Image and Video Search Recommendation Systems Image and Video Search Image and Video Search Recommendation Systems Recommendation Systems 15. Embedded Database 15. Embedded Database Embedded databases are lightweight, specialized databases built directly into software applications, offering seamless integration. Unlike traditional client-server databases that operate as separate processes, embedded databases run within the application itself, enabling faster data access, a smaller footprint, and easier deployment. These databases are especially valuable in environments with limited resources, where the complexity and overhead of a full client-server database would be unnecessary or impractical. Example: SQLite, RocksDB, BerkeleyDB Example: SQLite, RocksDB, BerkeleyDB Example: Example: SQLite, RocksDB, BerkeleyDB Some common use cases of Embedded Databases are as follows ~ Desktop Applications Quick Proof-Of-Concepts Desktop Applications Desktop Applications Quick Proof-Of-Concepts Quick Proof-Of-Concepts That’s it! I hope this info is useful for you.
