Expanded readme

README.md

@@ -50,6 +50,22 @@ run_validation(
 See the documentation of `dq_suite.validation.run` for what other parameters can be passed.
 
 
+# Other functionalities
+## Export the schema from Unity Catalog to the Input Form
+In order to output the schema from Unity Catalog, use the following commands (using the required schema name):
+```
+schema_output = dq_suite.schema_to_json_string('schema_name', spark, *table)
+print(schema_output)
+```
+Copy the string to the Input Form to quickly ingest the schema in Excel. The "table" parameter is optional, it gives more granular results.
+## Validate the schema of a table
+It is possible to validate the schema of an entire table to a schema definition from Amsterdam Schema in one go. This is done by adding two fields to the "dq_rules" JSON when describing the table (See: https://github.com/Amsterdam/dq-suite-amsterdam/blob/main/dq_rules_example.json). 
+You will need:
+- validate_table_schema: the id field of the table from Amsterdam Schema
+- validate_table_schema_url: the url of the table or dataset from Amsterdam Schema
+The schema definition is converted into column level expectations (ExpectColumnValuesToBeOfType) on run time.
+
+
 # Known exceptions / issues
 - The functions can run on Databricks using a Personal Compute Cluster or using a Job Cluster. 
 Using a Shared Compute Cluster will result in an error, as it does not have the permissions that Great Expectations requires.