# Cross-Catalog & Cross-Schema Querying

* This feature enables you to combine and analyze data from different schemas and catalogs, providing a comprehensive view of your data landscape.
* To perform cross-schema queries, you need to fully qualify the path by providing the catalog name, schema name, and table name.

#### Usage Examples:

1. Cross-Schema Query: This query retrieves columns `column1` and `column2` from the table `table_name` within the specified `schema_name` and `catalog_name`.

```sql
SELECT column1, column2
FROM catalog_name.schema_name.table_name;
```

2. Cross-Catalog Query: This query retrieves columns `column1` and `column2` from the table `table_name` within the specified `schema_name` and `catalog_name`.

```sql
SELECT column1, column2
FROM catalog_name.schema_name.table_name;
```

#### Limitations:

* **Slight Increase in Parsing Time:** Due to the additional complexity introduced by cross-schema and cross-catalog querying, there may be a slight increase in parsing time for initial queries. However, subsequent queries benefit from query plan caching and performance optimizations.
* **Fully Qualified Path Requirement:** When performing cross-schema or cross-catalog queries, it is essential to provide the entire path, including the catalog name, schema name, and table name. 
  * Examples:
    * **Hive Catalog:**\
      `<catalog_name>.<db_name>.<table_name>`\
      `catalog_name` - name of catalog specified during catalog creation in e6data
    * **Glue Catalog:**\
      `<catalog_name>.<db_name>.<table_name>`\
      `catalog_name` - name of catalog specified during catalog creation in e6data
    * **Unity Metastore:**\
      `<unity_catalog_name>.<db_name>.<table_name>`\
      `unity_catalog_name` - name of catalog which is created under Unity in Databricks
* **Same Account and Region for Cross-Catalog Queries:** To query tables across catalogs, the catalogs must reside within the same account and region. This limitation ensures data security and optimal performance.