Editing Web development (section)

==== Databases ====
[[Database management]] is crucial for [[Data storage|storing]], [[Data retrieval|retrieving]], and managing data in web applications. Various database systems, such as [[MySQL]], [[PostgreSQL]], and [[MongoDB]], play distinct roles in organizing and structuring data. Effective database management ensures the responsiveness and efficiency of data-driven web applications. There are 3 types of databases:

* '''[[Relational database]]s:''' Structured databases that use tables to organize and relate data. Common Examples include - [[MySQL]], [[PostgreSQL]] and many more.
* '''NoSQL databases:''' [[NoSQL|NoSQL databases]] are designed to handle [[Unstructured data|unstructured]] or [[Semi-structured data|semi-structured]] data and can be more flexible than [[relational database]]s. They come in various types, such as [[Document-oriented database|document-oriented]], [[Key–value database|key-value stores]], column-family stores, and [[graph database]]s. Examples: [[MongoDB]], [[Apache Cassandra|Cassandra]], [[ScyllaDB]], [[Apache CouchDB|CouchDB]], [[Redis]].
* '''Document stores:''' [[Document-oriented database|Document stores]] store data in a [[Semi-structured data|semi-structured]] format, typically using [[JSON]] or [[XML]] documents. Each document can have a different structure, providing flexibility. Examples''':''' [[MongoDB]], [[CouchDB]].
* '''Key-value stores:''' [[Key–value database|Key-value stores]] store data as pairs of keys and values. They are simple and efficient for certain types of operations, like [[Cache (computing)|caching]]. Examples: [[Redis]], [[Amazon DynamoDB|DynamoDB]].
* '''Column-family stores:''' [[Column-oriented DBMS|Column-family stores]] organize data into columns instead of rows, making them suitable for large-scale [[distributed systems]] and analytical workloads. Examples''':''' [[Apache Cassandra]], [[Apache HBase|HBase]].
* '''Graph databases:''' [[Graph database]]s are designed to represent and query data in the form of graphs. They are effective for handling relationships and network-type data. Examples: [[Neo4j]], [[Amazon Neptune]].
* '''In-memory databases:''' [[In-memory database]]s store data in the system's main memory ([[Random-access memory|RAM]]) rather than on disk. This allows for faster data access and retrieval.  Examples: [[Redis]], [[Memcached]].
* '''Time-series databases:''' [[Time series database|Time-series databases]] are optimized for handling time-stamped data, making them suitable for applications that involve tracking changes over time.  Examples: [[InfluxDB]], OpenTSDB.
* '''NewSQL databases:''' [[NewSQL]] databases aim to provide the scalability of NoSQL databases while maintaining the [[ACID]] properties (Atomicity, Consistency, Isolation, Durability) of traditional relational databases.  Examples: [[Spanner (database)|Google Spanner]], [[CockroachDB]].
* '''Object-oriented databases:''' [[Object-oriented databases]] store data in the form of objects, which can include both [[data]] and methods. They are designed to work seamlessly with object-oriented programming languages. Examples: [[db4o]], [[ObjectDB]].

The choice of a database depends on various factors such as the nature of the data, scalability requirements, performance considerations, and the specific use case of the application being developed. Each type of database has its strengths and weaknesses, and selecting the right one involves considering the specific needs of the project.