![]() SQL Server creates a new table in that particular Verify table Filegroup. In SQL Server 2017, we can specify a particular Filegroup in which a new table should be created. We cannot specify a particular Filegroup until SQL Server 2016. It creates a table in default Filegroup only. In previous sections, we explored that we can create a new table and insert data into it using the SQL SELECT INTO statement from existing tables. However, we cannot insert data into existing tables using this method. ![]() We do not get these errors while inserting data using the SQL SELECT INTO statement. String or binary data would be truncated. If there is a mismatch between data types, you can get an error message like following.Ĭonversion failed when converting the varchar value ‘GG’ to data type int.Įrror 2: Msg 8152, Level 16, State 30, Line 2 If we want to create a table manually, we need to look at data type of each column and define datatype accordingly. We do not need to take care of defining data types for destination table. We can see you can join multiple tables together and creates output table using the SELECT INTO statement. We can verify records in this table by the select statement. Let us create another table with all columns in an Employee table with the following query. In the previous SQL SELECT INTO statement, we prepared a destination table (Employee_Demo) from a few columns in the source table (Employee). In the following screenshot, you can see that column datatype and their properties are similar. I copied the output of both tables in excel ( for selected columns). We can run sp_help ‘tablename’ command on both Employee and Employee_Demo table. Let us compare the source and destination table columns and their properties. ![]() We did not specify any column properties in the SQL SELECT INTO statement. We can access data from newly created table Employee_Demo and verify that it contains same records as of our earlier select statement. In the query, we selected top 10 records from an Employee table. You can note that the number of rows affected is 10. We get the following output message in Azure Data Studio. ![]()
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