Each database type has been optimized for specific use cases.
In a ๐ฟ๐ฒ๐น๐ฎ๐๐ถ๐ผ๐ป๐ฎ๐น ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ, data is organized in rows and columns where a row represents a record and its data fields are stored in columns. They are ideal for when ACID compliance is required, and a predefined schema can be created.
With ๐ฐ๐ผ๐น๐๐บ๐ป๐ฎ๐ฟ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐, records are stored as columns rather than rows. This makes them very performant for analytical purposes where complex queries are run across large datasets; especially those that contain aggregate functions.
In a ๐ฑ๐ผ๐ฐ๐๐บ๐ฒ๐ป๐ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ, data is stored in a semi-structured format such as JSON. They offer a flexible and schema-less approach which makes them a great choice for data with complex or continually changing structures.
๐๐ฟ๐ฎ๐ฝ๐ต ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ are optimized for storing and querying highly connected data. Records are represented as nodes and relationships as edges. Under the hood, they use graph theory to traverse relationships between nodes to power performant queries.
๐๐ฒ๐-๐๐ฎ๐น๐๐ฒ ๐๐๐ผ๐ฟ๐ฒ๐ is a simple form of storage where values are inserted, updated, and retrieved using a unique key. They are more commonly used for small datasets and often temporary purposes such as caching or session management.
๐ง๐ถ๐บ๐ฒ-๐๐ฒ๐ฟ๐ถ๐ฒ๐ ๐ฑ๐ฎ๐๐ฎ๐ฏ๐ฎ๐๐ฒ๐ are ideal for time-stamped data that are queried and analyzed in relation to time. They provide built-in time-based functions that assist in analyzing large datasets over time.
