What is the difference between a typed and an untyped dataset

Also that if you are getting errors in Typed DataSets they will most probably be compile time and not runtime so you can always fix them. IsAuthorNull ; As you can see above each column that is included in the Typed DataSet comes with a method which let's the user know that if the current row that he is accessing is null or not. This is very convenient for columns that can have null values. If you don't check for null values "Object reference not set to an instance" error will be fired.

Adding new columns in the Typed DataSets are also very easy. Just switch back to the xsd schema graphical user interface and add the column. Now after adding a new column just build the application and you are ready to use the new column. The coolest feature of using Typed DataSets is that you can convert the whole dataset schema into a class which can be used by other users.

You can easily do this by using a tool called XSD. Go to the Visual Studio. NET Command prompt. And type this command. There are many things you can do depending on the type of the escape sequence you apply.

Remember that this command must be run from the same directory where the xsd file is present. Once you the command it will create the class file with the same name as that of the xsd file. Now you schema has become portable and so others can easily use this. Many people are using Typed DataSets as business objects. I hope you enjoyed the article, happy programming! There are many interpretations to what the types of data are or should be and the DataFerrett Team has strived to portray and deliver to the user our best interpretation possible.

Demographics, poverty, income, geography, health insurance, etc. Below please find the 4 basic types of data that the DataFerrett application software takes into account when processing data for inclusion into the DataFerrett application.

MicroData: Microdata is data in which every record is at the unit of analysis level and all records must be added up to get the totals for each data item. An untyped dataset, in contrast, has no corresponding built-in schema.

As in a typed dataset, an untyped dataset contains tables, columns, and so on—but those are exposed only as collections. However, after you manually create the tables and other data elements in an untyped dataset, you can export the dataset's structure as a schema by using the dataset's WriteXmlSchema method. The class for a typed dataset has an object model in which its properties take on the actual names of the tables and columns. For example, if you are working with a typed dataset, you can reference a column by using code such as the following:.

In addition to being easier to work with, the syntax for the typed dataset provides type checking at compile time, greatly reducing the possibility of errors in assigning values to dataset members.

If you change the name of a column in your DataSet class and then compile your application, you receive a build error. By double-clicking the build error in the Task List , you can go directly to the line or lines of code that reference the old column name. In C you can pretty much assign any variable to any other variable without the compiler complaining, no matter the data types of the variables.

For example, C does not complain if you assign a char to an int. Visual Basic normally requires that the data types be compatible when they are assigned.

If you try to assign the string variable to a numeric variable the compiler will usually complain. However, Visual Basic is normally not strictly typed.

Strict typing means that you can only assign the string to a string and a numeric to another numeric of equal or greater scale. In Visual Basic you can turn on strict typing with an Option statement. When we talk about a strongly typed DataSet, we are talking about something a little different.

A strongly typed DataSet is a class that derives from a DataSet. It inherits all the properties, methods , and events of the DataSet class, plus it adds its own so that we can access its members by name instead of just by index. Suppose a typed DataSet has a table named Customers. We can access the Customers table by a property called Customers instead of by the Tables collection.

The advantage of this is that the Customers table already has all of the column objects and row objects defined. Check out this snippet of code:. Notice how we're able to address the tables and rows by name. This is similar to early and late binding in COM. Since the Customers DataTable property is derived from the DataTable, we can assign it to a variable that is declared as a DataTable or as the more restrictive type of Customers DataTable.

When we create a strongly typed DataSet, the wizard generates a new class derived from the DataSet class and by default names it DataSet1. It then looks at the results of the queries in the Data Adapter and creates DataTable objects and classes.

It then creates properties and methods for manipulating the classes directly. You can open this and look at the code, but do not modify it because this will confuse the system. If you have an XSD file from another source you can generate your VB module using a command-line utility. You can also do this programmatically using the emitter classes but this is beyond the scope of this book.

This utility is appropriately called xsd. You can use it to generate VB code or C code via command-line switches execute xsd. Let's create a screen similar to the screen we had in the previous project. This screen should contain a grid control, three buttons , and the search text box. In addition place two additional text boxes and another button on the next row.

The form should look like Figure 7. You can use the same form as we did in the previous chapter; just copy it into the new directory and edit your project. Make sure you set the startup project to be the new project, and then set the startup form to be the new form. We will use the Show button to demonstrate a little bit of what we've been talking about with typed DataSets.

Copy the following code into your project. Notice how we reference the objects in the DataSet by name. We cannot get an advantage of intelliSense. Performance is faster in case of Untyped dataset. Untyped datasets are easy to administer. Report abuse. Page details. Page updated.