NoSQL
Types Of NoSQL Databases | MongoDB
Here are the four main types of NoSQL databases:
Document Databases
A document database stores data in JSON, BSON , or XML documents (not Word documents or Google docs, of course). In a document database, documents can be nested. Particular elements can be indexed for faster querying.
Comparing MongoDB vs PostgreSQL
Key-Value Stores
The simplest type of NoSQL database is a key-value store . Every data element in the database is stored as a key value pair consisting of an attribute name (or "key") and a value. In a sense, a key-value store is like a relational database with only two columns: the key or attribute name (such as state) and the value (such as Alaska).
Use cases include shopping carts, user preferences, and user profiles.
Column-Oriented Databases
While a relational database stores data in rows and reads data row by row, a column store is organized as a set of columns. This means that when you want to run analytics on a small number of columns, you can read those columns directly without consuming memory with the unwanted data. Columns are often of the same type and benefit from more efficient compression, making reads even faster. Columnar databases can quickly aggregate the value of a given column (adding up the total sales for the year, for example). Use cases include analytics.
Unfortunately there is no free lunch, which means that while columnar databases are great for analytics, the way in which they write data makes it very difficult for them to be strongly consistent as writes of all the columns require multiple write events on disk. Relational databases don't suffer from this problem as row data is written contiguously to disk.
Graph Databases
A graph database focuses on the relationship between data elements. Each element is stored as a node (such as a person in a social media graph). The connections between elements are called links or relationships. In a graph database, connections are first-class elements of the database, stored directly. In relational databases, links are implied, using data to express the relationships.
A graph database is optimized to capture and search the connections between data elements, overcoming the overhead associated with JOINing multiple tables in SQL.
Very few real-world business systems can survive solely on graph queries. As a result graph databases are usually run alongside other more traditional databases.
Use cases include fraud detection, social networks, and knowledge graphs.
As you can see, despite a common umbrella, NoSQL databases are diverse in their data structures and their applications.
