Relational databases store data with pre-defined schema and relationships between them, designed for supporting ACID transactions, maintaining referential integrity, and data consistency.
In-memory databases are used for applications that require real time access to data. By storing data directly in memory, these databases provide microsecond latency where millisecond latency is not enough.
Ledger databases are used when you need a centralized, trusted authority to maintain a scalable, complete and cryptographically verifiable record of transactions.
Key-value databases are optimized to store and retrieve key-value pairs in large volumes and in milliseconds, without the performance overhead and scale limitations of relational databases.
Graph databases are used for applications that need to enable millions of users to query and navigate relationships between highly connected, graph datasets with millisecond latency.
Document databases are designed to store semi-structured data as documents and are intuitive for developers to use because the data is typically represented as a readable document.
Time series databases are used to efficiently collect, synthesize, and derive insights from enormous amounts of data that changes over time (known as time-series data).