How do you handle data persistence in IOS?

In iOS, there are several ways to handle data persistence. Some of the commonly used methods are:

1. User Defaults: User Defaults is a simple way to store small amounts of data such as user preferences, settings, or small configuration data. It uses the UserDefaults class to store data in key-value pairs and is suitable for storing simple data types like strings, numbers, and booleans.

2. Property Lists: Property lists, also known as plists, are XML or binary files that can be used to store structured data. They are suitable for storing more complex data structures like arrays and dictionaries. Property lists can be easily read and written using the PropertyListSerialization class.

3. Core Data: Core Data is a powerful framework provided by Apple for managing the model layer objects in an application. It provides an object-oriented approach to data persistence and allows you to store, retrieve, and manipulate data in a structured manner. Core Data uses SQLite as its default persistent store, but it also supports other persistent store types.

4. SQLite: SQLite is a lightweight and embedded relational database management system that can be used for data persistence in iOS applications. It provides a SQL interface for managing data and is suitable for applications that require complex data querying and manipulation.

5. File System: iOS provides a file system that allows you to read and write data to files. You can use file handling APIs like FileManager to create, read, write, and delete files. This method is suitable for storing large amounts of data or when you need more control over the file structure.

6. Network Services: If your application requires data to be stored on a remote server, you can use network services like RESTful APIs or web services to handle data persistence. This involves sending data to the server and retrieving it when needed.

The choice of data persistence method depends on the specific requirements of your application. It is common to use a combination of these methods based on the type and size of data, performance considerations, and the complexity of data manipulation required.