# Using an intermediate table in analyses
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After you populate an intermediate table and attach an analysis rule, you can reference it in queries just like a configured table.

**Discovering intermediate tables**

All collaboration members can view intermediate tables on the **Tables** tab of a collaboration. The table type is shown as "Intermediate table."

**Referencing in SQL**

Reference an intermediate table by name in your SQL query, just like you reference a configured table. For example:

```
SELECT * FROM my_intermediate_table
```

**Referencing in PySpark**

In a PySpark analysis template, reference an intermediate table through the context object:

```
context['referencedTables']['my_intermediate_table']
```

**Requirements at analysis time**

AWS Clean Rooms enforces the following requirements when you run an analysis that references an intermediate table:
+ The intermediate table schema status must be **Ready** (populated and analysis rule attached).
+ The analysis rule is validated against inherited constraints from base tables.
+ Deferred controls are enforced, including disallowedOutputColumns and allowedResultReceivers.

**Using in ML workflows**

You can reference intermediate tables in ML workflows through the dataSource.protectedQueryInputParameters configuration.

**Viewing the schema**

To view the columns available in an intermediate table, choose the **Schema** tab on the intermediate table details page. You can view the column name and type. Choose **Edit schema** to update column types.

**Viewing dependencies**

To view the base tables that an intermediate table depends on, choose the **Dependencies** tab. You can view the table name, owner, type, and parent type for each dependency.