List of SQL & NoSQL databases

SQL databases are relational and use structured query language (SQL) for defining and manipulating data.

Here's a tabular comparison of different types of SQL (Relational) databases:

Type	Description	Use Cases	Examples
Traditional RDBMS	Standard relational databases using structured schema.	General-purpose databases suitable for a wide range of applications.	MySQL, PostgreSQL, Oracle, SQL Server.
NewSQL Databases	Modern relational databases designed for cloud and scale.	High transactional workloads with scalability and cloud capabilities.	Google Spanner, CockroachDB, NuoDB.
In-Memory Databases	Store data in main memory for faster access.	Real-time processing of data, high-speed transactions, caching.	SAP HANA, Redis, MemSQL.
Data Warehousing	Optimized for analytics and reporting.	Business intelligence, analytics, large-scale reporting, and data mining.	Snowflake, Amazon Redshift, Google BigQuery.
Distributed SQL Databases	Extend RDBMS to operate in a distributed fashion.	Applications needing global distribution, high availability, and horizontal scalability.	YugabyteDB, Vitess.
Temporal Databases	Track and store all changes to data over time.	Applications requiring auditing, historical analysis, and time-sensitive data.	Teradata, Oracle Workspace Manager.

Additional Notes:

Traditional RDBMS: These are the most common SQL databases, offering robust transaction support, stability, and maturity. They are best suited for applications where data integrity and security are critical.
NewSQL Databases: These databases combine the scalable performance of NoSQL systems with the ACID (Atomicity, Consistency, Isolation, Durability) guarantees of traditional SQL databases. Ideal for businesses that need to process many transactions quickly and reliably.
In-Memory Databases: By storing data in RAM instead of on disk, these databases offer rapid data access, making them ideal for applications that require real-time access to data, like caching systems or real-time analytics.
Data Warehousing: These databases are designed for analyzing and reporting rather than for transaction processing. They are optimized for read-heavy workloads and are commonly used in business intelligence applications.
Distributed SQL Databases: They bring the advantages of SQL databases to a distributed, often cloud-native, environment. They are designed to provide high availability, fault tolerance, and horizontal scalability.
Temporal Databases: These are specialized SQL databases that store information relating to time instances. They are beneficial in scenarios where it's crucial to maintain and track historical data, such as in auditing or compliance tracking.

Each type of SQL database is tailored to specific needs, ensuring that there's a suitable option for various application requirements, from traditional data storage and processing to real-time analytics and global distribution.

NoSQL databases, which stand for "Not Only SQL" are designed to handle a wide variety of data models, including key-value, document, column, and graph formats. They are often used for large data sets and real-time web applications. NoSQL databases are particularly known for their ability to scale out and handle big data challenges.

Here's a tabular comparison of the different types of NoSQL databases:

Type	Description	Use Cases	Examples
Key-Value Stores	Simplest form, storing data as key-value pairs.	Session storage, user profiles, preferences, shopping carts.	Redis, Amazon DynamoDB, Riak.
Document Databases	Store data in document-like structures (JSON/BSON).	Content management, e-commerce applications, mobile app data management.	MongoDB, Couchbase, CouchDB.
Column-Family Stores	Store data in columns grouped in families.	Big Data analytics, recommendation engines, time-series data analysis.	Apache Cassandra, HBase, Google Bigtable.
Graph Databases	Designed for data best represented as a graph.	Social networks, fraud detection, graph-based search, network systems.	Neo4j, JanusGraph, Azure Cosmos DB's Gremlin API.
Multi-Model Databases	Combine different database models.	Enterprises handling various data types and formats, versatile applications.	OrientDB, ArangoDB.
Object-Oriented Databases	Store data in the form of objects.	Applications with complex data models and object-oriented programming languages.	db4o, ObjectDB.
Search Engines	Index and search large datasets, focus on text data search.	Website search functionality, indexing large datasets, log data analysis.	Elasticsearch, Solr.

This table provides a quick reference to understand the key characteristics, use cases, and examples of each NoSQL database type. The choice among these databases typically depends on the specific needs and characteristics of the application they are intended to support.