There are various types of databases available, each designed to serve different purposes and cater to specific data management needs. Here are some of the commonly used databases:

1. Relational Databases (RDBMS): Relational databases store data in structured tables with predefined schemas. They use SQL (Structured Query Language) for data manipulation and retrieval. Examples include MySQL, Oracle Database, Microsoft SQL Server, and PostgreSQL.
   NoSQL Databases: NoSQL (Not Only SQL) databases are non-relational databases that provide flexible schemas and scalability. They are suitable for handling unstructured or semi-structured data. Types of NoSQL databases include:
   Document Databases: Store and retrieve data as JSON-like documents. Examples include MongoDB and Couchbase.
   Key-Value Stores: Store data as key-value pairs. Examples include Redis, Riak, and Amazon DynamoDB.
   Columnar Databases: Store data in columns rather than rows, which is beneficial for analytical workloads. Examples include Apache Cassandra and Apache HBase.
   Graph Databases: Designed to store and analyze relationships between data entities. Examples include Neo4j and Amazon Neptune.
2. In-Memory Databases: In-memory databases reside primarily in the computer's memory for faster data access and processing. Examples include Redis (which also falls under the key-value store category) and SAP HANA.
3. Time-Series Databases: Specifically optimized for storing and retrieving time-stamped or time-series data, such as sensor data, financial data, or log data. Examples include InfluxDB, Prometheus, and TimescaleDB.
4. Object-Oriented Databases: Designed to work with object-oriented programming languages and manage complex data models. Examples include db4o and ObjectDB.
5. NewSQL Databases: Combine the benefits of traditional SQL databases (ACID compliance, strong consistency) with scalability and distributed architectures. Examples include CockroachDB and Google Spanner.
6. Spatial Databases: Geospatial databases designed for storing and querying spatial data, such as maps, coordinates, and geographic information. Examples include PostGIS (an extension for PostgreSQL) and Oracle Spatial.
7. Cloud Databases: Databases offered as a service in the cloud, providing scalability, availability, and managed infrastructure. Examples include Amazon RDS, Microsoft Azure SQL Database, and Google Cloud Spanner.
8. Distributed Databases: Designed to manage large-scale distributed data across multiple nodes or servers. Examples include Apache Cassandra, Apache Hadoop (HDFS), and Apache Kafka.

These are just a few examples of the many types of databases available, each with its own strengths and suitable use cases. The choice of database depends on factors such as the nature of the data, scalability requirements, performance needs, and budget considerations.