## Query-based transformer

URL: https://docs.atlan.com/product/capabilities/build-apps/sdks/application-sdk/references/transformers/query-based-transformer

> Complete reference for QueryBasedTransformer that transforms metadata using SQL queries defined in YAML templates.

QueryBasedTransformer transforms raw metadata DataFrames into structured entities using SQL queries defined in YAML templates. It executes SQL transformations on raw dataframes using the daft engine and automatically creates nested structures from flat dataframes with dot notation. This transformer is ideal when you need SQL-based transformations with flexible template definitions.

## Default template mappings

Asset types are automatically mapped to YAML template files when using default templates:

| Asset Type | Template File | Location |
|------------|---------------|----------|
| `TABLE` | `table.yaml` | `application_sdk/transformers/query/templates/` |
| `COLUMN` | `column.yaml` | `application_sdk/transformers/query/templates/` |
| `DATABASE` | `database.yaml` | `application_sdk/transformers/query/templates/` |
| `SCHEMA` | `schema.yaml` | `application_sdk/transformers/query/templates/` |
| `FUNCTION` | `function.yaml` | `application_sdk/transformers/query/templates/` |
| `EXTRAS-PROCEDURE` | `extras-procedure.yaml` | `application_sdk/transformers/query/templates/` |

## Template structure

YAML templates define column mappings using nested dictionaries with dot notation for column paths.

### Template format

```yaml
table: Table
columns:
 attributes:
 name:
 source_query: table_name
 qualifiedName:
 source_query: concat(connection_qualified_name, '/', table_catalog, '/', table_schema, '/', table_name)
 source_columns: [connection_qualified_name, table_catalog, table_schema, table_name]
 customAttributes:
 table_type:
 source_query: table_type
 typeName:
 source_query: |
 CASE
 WHEN table_type = 'VIEW' THEN 'View'
 ELSE 'Table'
 END
 source_columns: [table_type]
 status:
 source_query: "'ACTIVE'"
```

### Column definition fields

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `source_query` | `str` | Yes | SQL expression or literal value |
| `source_columns` | `List[str]` | No | Source columns to validate before generating SQL query. If missing, column mapping is skipped with warning. |

### Column expression types

| Type | Example | Notes |
|------|---------|-------|
| Direct column reference | `source_query: table_name` | Direct column mapping |
| SQL expression | `source_query: concat(a, '/', b)` | SQL function or expression |
| String literal | `source_query: "'ACTIVE'"` | Quoted string value |
| Boolean/Number literal | `source_query: true` | Unquoted boolean or number |
| Conditional expression | `source_query: \| CASE WHEN ... END` | Multi-line SQL CASE statement |

## Default attributes

The `prepare_template_and_attributes` method adds these default attributes to all transformations:

| Attribute | Source | Type |
|-----------|--------|------|
| `connection_qualified_name` | `**kwargs` | `str` |
| `connection_name` | `**kwargs` | `str` |
| `tenant_id` | Initialization parameter | `str` |
| `workflow_id` | Method parameter | `str` |
| `workflow_run_id` | Method parameter | `str` |
| `connector_name` | Initialization parameter | `str` |

Default attributes are available in all SQL expressions within YAML templates.

## Nested structure creation

The `get_grouped_dataframe_by_prefix` method groups flat columns with dot notation into nested structs.

**Input format** (flat columns):
```
attributes.name
attributes.qualifiedName
attributes.description
customAttributes.table_type
typeName
status
```

**Output format** (nested structs):
```python
{
 "attributes": {
 "name": "...",
 "qualifiedName": "...",
 "description": "..."
 },
 "customAttributes": {
 "table_type": "..."
 },
 "typeName": "...",
 "status": "..."
}
```

**Column name handling**: Columns with dots are automatically quoted in SQL (for example, `attributes.name` becomes `"attributes.name"`).

## Error handling

| Error Type | Behavior |
|------------|----------|
| Template loading error | Raises exception, logs template path |
| Missing source column | Skips column mapping, logs warning |
| SQL execution error | Raises exception, logs SQL query and typename |
| Empty DataFrame | Returns `None` |

## Performance characteristics

| Aspect | Behavior |
|--------|----------|
| Evaluation | Lazy evaluation (daft DataFrames) |
| Column processing | Only processes columns that exist in DataFrame |
| Struct building | Uses daft expressions (not row-by-row) |
| Template loading | On-demand per transformation (consider caching for high-volume scenarios) |

## See also

- [Transformers](https://docs.atlan.com/llms/platform/build-apps/transformers/llms.txt): Overview of all transformers and the TransformerInterface
- [Atlas transformer](https://docs.atlan.com/llms/platform/build-apps/atlas-transformer/llms.txt): Convert metadata into Atlas entities using pyatlan library classes
- [Application SDK README](https://docs.atlan.com/llms/platform/build-apps/application-sdk/llms.txt): Overview of the Application SDK and its components

_Last updated: 15 May 2026._

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