Select all the columns of a table except one column?

Methods to tackle bulky tables

Utilizing Dynamic SQL

Dynamic SQL affords you flexibility to build and execute SQL statements dynamically. This feature is particularly invaluable when dealing with large number of columns. Check this out:

DECLARE @sql NVARCHAR(MAX);

SELECT @sql = 
  STRING_AGG(QUOTENAME(column_name), ', ')
FROM 
  information_schema.columns
WHERE 
  table_name = 'your_table_name' AND
  column_name != 'column_to_exclude';

SET @sql = N'SELECT ' + @sql + N' FROM your_table_name';-- Kudos for simplifying SQL

EXEC sp_executesql @sql; -- Magic happens here
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Embrace Temporary Tables

When handling large tables, temporary tables can rescue you by simplifying data manipulation and storage. Here, the process follows:

1. Generate a temporary table with all columns.
2. Drop the undesired column from the new table.
3. Select the remaining columns.

Here's a sample code for the steps:

SELECT * INTO #TemporaryTable FROM table_name;
ALTER TABLE #TemporaryTable DROP COLUMN column_to_exclude;
SELECT * FROM #TemporaryTable; -- Looking good?
DROP TABLE #TemporaryTable; -- Clean-up crew is coming!
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Creating Views to Avoid Repetition

If repeating the same selection is your drill, creating a view can streamline your process:

CREATE VIEW view_name AS
SELECT col1, col2, ... -- only the cool guys
FROM table_name;
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Henceforth, use SELECT * FROM view_name; for the desired selection.

Deeper dive into dynamic alternatives

Querying System Tables for Column Information

Gateway to SQL meta-tables like sys.columns can avail column names without the need for manual entry, thereby dimsiss hard-coding column names:

SELECT c.name
FROM sys.columns c
JOIN sys.tables t ON c.object_id = t.object_id
WHERE t.name = 'table_name' AND c.name != 'column_to_exclude'; -- Guess who's left out?
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Using SQL Server Management Studio (SSMS)

Within SSMS, these steps would give you your selection:

1. Right-click on the table.
2. Fetch Script Table as > SELECT To > New Query Editor Window.
3. Eliminate the odd one out (the column you want to drop).

That was a handy eyeball-friendly method.

Problems and limitations

Alterations in Table Schemas

Frequent table schema changes can complicate manual query formation. Dynamic SQL or views can aid in auto-adjustments according to schema changes.

Worries over Performance

Usage of SELECT * is typically frowned upon for production queries due to data over-fetching and correspondingly worse performance. It's better to pick out and select only the necessary columns.

Security Concerns

Unsecured and improperly parameterized dynamic SQL can lead to SQL injection attacks. Therefore, always prefer using parameterized queries with sp_executesql.

Walking through complex scenarios

Regular Expressions and SQL Extensions

Advanced SQL variations or third-party tools offer Regular Expressions (RegEx) or enhanced SQL syntaxes for column exclusion. Check your SQL documentation for more on this.

Scripts for Complex Situations

Complex requirements or multiple SQL systems? Python scripting using libraries like Pandas, PyODBC, or SQLAlchemy can simplify column management in a more controllable and reusable manner.

Views: The Good, the Bad, and the Ugly

On creation, views persist on the server, thereby potentially cluttering up your space over time. Plus, views abide by the table's user permission system, and thus, lengthen the shadow of table security issues.