What is typed dataset ?

Strongly Typed Datasets. A Strongly Typed Dataset is a custom class and it consists of classes derived form DataSet, DataTable and DataRow. A typed class assumes all of the functionality of the DataSet class and can be used with methods that take an instance of a DataSet class as a parameter.

http://www.c-sharpcorner.com/article/typed-datasets-in-net/

Along with late bound access to values through weakly typed variables, the DataSet provides access to data through a strongly typed metaphor. Tables and columns that are part of the DataSet can be accessed using user-friendly names and strongly typed variables. A typed DataSet is a class that derives from a DataSet. As such, it inherits all the methods, events, and properties of a DataSet. Additionally, a typed DataSet provides strongly typed methods, events, and properties. This means you can access tables and columns by name, instead of using collection-based methods. Aside from the improved readability of the code, a typed DataSet also allows the Visual Studio .NET code editor to automatically complete lines as you type. Additionally, the strongly typed DataSet provides access to values as the correct type at compile time. With a strongly typed DataSet, type mismatch errors are caught when the code is compiled rather than at run time.

http://www.c-sharpcorner.com/article/typed-datasets-in-net/

Typed dataset is a dataset whose schema is predefined.

A typed dataSet is a class that derives from a dataSet.It inherits all the methods and events. Access to tables and columns in a typed dataset is also slightly faster at run time because access is determined at compile time.

A typed DataSet is a class that derives from a DataSet. As such, it inherits all the methods, events, and properties of a DataSet. Additionally, a typed DataSet provides strongly typed methods, events, and properties. This means you can access tables and columns by name, instead of using collection-based methods.

A typed dataset is very much similar to a normal dataset. But the only difference is that the sehema is already present for the same. Hence any mismatch in the column will generate compile time errors rather than runtime error as in the case of normal dataset. Also accessing the column value is much easier than the normal dataset as the column definition will be available in the schema.