> For the complete documentation index, see [llms.txt](https://docs.datapancake.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datapancake.com/core-concepts/attribute-metadata/attributes-in-code-generation.md).

# Attributes in Code Generation

How active polymorphic versions become SQL columns in Dynamic Tables, including transformation application and array table generation.

## Dynamic Table Column Generation

For each active polymorphic version with `INCLUDE_IN_CODE_GEN = TRUE`, DataPancake generates a column:

1. **Column name:** Uses `ALIAS_NAME` if provided, otherwise `POLYMORPHIC_ATTRIBUTE_NAME` → `CODE_GENERATED_COLUMN_NAME`
2. **Type casting:** Uses `DATA_PLATFORM_DATA_TYPE` with precision/scale; applies datetime formats if `USE_DATETIME_FORMAT = TRUE`
3. **Value extraction:** Extracts from source using `ATTRIBUTE_PATH`; handles nested structures and arrays
4. **Transformation:**
   * If `TRANSFORMATION_TYPE = 'No Transformation'`: Applies `NULL_VALUE_EXPRESSION` (if provided)
   * If `TRANSFORMATION_TYPE = 'SQL Expression'`: Applies `TRANSFORMATION_EXPRESSION` (replaces `{attribute_name}` placeholder)
5. **Inclusion:** Only if `INCLUDE_IN_CODE_GEN = TRUE` AND `PARENT_INCLUDE_IN_CODE_GEN = TRUE` AND `VERSION_STATUS = 'active'`

***

## Example: Column Generation

**Attribute:** `customer.email` with polymorphic version `email_str`

**Configuration:**

* `DATA_PLATFORM_DATA_TYPE = 'VARCHAR(255)'`
* `TRANSFORMATION_TYPE = 'No Transformation'`
* `NULL_VALUE_EXPRESSION = 'COALESCE({attribute_name}, ''unknown@example.com'')'`
* `ALIAS_NAME = 'customer_email'`

**Generated SQL:**

```sql
COALESCE(json_data:"customer"."email"::VARCHAR(255), 'unknown@example.com') AS customer_email
```

**With transformation:**

* `TRANSFORMATION_TYPE = 'SQL Expression'`
* `TRANSFORMATION_EXPRESSION = 'UPPER({attribute_name})'`

**Generated SQL:**

```sql
UPPER(json_data:"customer"."email"::VARCHAR(255)) AS customer_email
```

***

## Array Table Generation

**Object arrays (`ARRAY_TYPE = 'object'`):**

* Separate Dynamic Table created for array contents
* Named using `CREATE_OBJECT_NAME_ALIAS_NAME` or default `{root_table_name}_array_object`
* Foreign key columns link array table to parent (configured in Foreign Keys section)

**Primitive arrays (`ARRAY_TYPE = 'primitive'`):**

* May be stored as VARIANT or ARRAY types in root table
* Or flattened into separate table depending on configuration
* `ARRAY_PRIMITIVE_TYPE` determines element handling

***

## ITDCs (Immutable Typed Derived Columns)

DataPancake uses explicit type casting (`::DATA_TYPE`) in generated SQL to prevent technical debt from implicit conversions.

**Example:**

```sql
-- DataPancake (explicit casting)
json_data:"customer"."email"::VARCHAR(255) AS customer_email

-- vs. implicit (technical debt)
json_data:"customer"."email" AS customer_email
```

***

## Code Generation Process

1. **Collect active attributes:** Query `vw_datasource_active_attributes` where `INCLUDE_IN_CODE_GEN = TRUE` and `PARENT_INCLUDE_IN_CODE_GEN = TRUE`
2. **Generate column statements:** For each active version, generate SQL with type casting and transformations
3. **Generate array tables:** Separate Dynamic Tables for object arrays with foreign key relationships
4. **Generate root table:** Combine all root-level columns with table-level settings
5. **Generate views:** Semantic layer views with `SEMANTIC_LAYER_EXPRESSION` and security policies

## Metadata Tracking

DataPancake can generate:

* **Streams:** Track changes to Dynamic Tables (change data capture)
* **Tasks:** Scheduled metadata synchronization
* **Metadata tables:** Track insert/update timestamps for audit and compliance


---

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