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Many different types including key-value stores, document databases, wide-column stores, and graph databases
Developed in 1970s to deal with first wave of data storage applications
Developed in 2000s to deal with limitations of SQL databases, particularly concerning scale, replication and unstructured data storage
MySQL, Postgres, Oracle Database
MongoDB, Cassandra, HBase, Neo4j
Data Storage Model
Individual records (e.g., "employees") are stored as rows in tables, with each column storing a specific piece of data about that record (e.g., "manager," "date hired," etc.), much like a spreadsheet. Separate data types are stored in separate tables, and then joined together when more complex queries are executed. For example, "offices" might be stored in one table, and "employees" in another. When a user wants to find the work address of an employee, the database engine joins the "employee" and "office" tables together to get all the information necessary.
Varies based on database type. For example, key-value stores function similarly to SQL databases, but have only two columns ("key" and "value"), with more complex information sometimes stored within the "value" columns. Document databases do away with the table-and-row model altogether, storing all relevant data together in single "document" in JSON, XML, or another format, which can nest values hierarchically.
Structure and data types are fixed in advance. To store information about a new data item, the entire database must be altered, during which time the database must be taken offline.
Typically dynamic. Records can add new information on the fly, and unlike SQL table rows, dissimilar data can be stored together as necessary. For some databases (e.g., wide-column stores), it is somewhat more challenging to add new fields dynamically.
Vertically, meaning a single server must be made increasingly powerful in order to deal with increased demand. It is possible to spread SQL databases over many servers, but significant additional engineering is generally required.
Horizontally, meaning that to add capacity, a database administrator can simply add more commodity servers or cloud instances. The database automatically spreads data across servers as necessary
Mix of open-source (e.g., Postgres, MySQL) and closed source (e.g., Oracle Database)
Yes, updates can be configured to complete entirely or not at all
In certain circumstances and at certain levels (e.g., document level vs. database level)
Specific language using Select, Insert, and Update statements, e.g. SELECT fields FROM table WHERE…
Through object-oriented APIs
Can be configured for strong consistency
Depends on product. Some provide strong consistency (e.g., MongoDB) whereas others offer eventual consistency (e.g., Cassandra)