# Destination Schema

## Overview

Destination Schema metadata controls how source attributes map to Snowflake data types and identifies key characteristics for Dynamic Tables. Configure target schema here.

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## Snowflake Data Type Configuration

### Data Platform Data Type

**Data Platform Data Type (`DATA_PLATFORM_DATA_TYPE`)**

* Snowflake data type for this attribute
* Examples: `VARCHAR`, `VARCHAR(n)`, `NUMBER`, `NUMBER(p,s)`, `TIMESTAMP_NTZ`, `DATE`, `BOOLEAN`, `VARIANT`
* **Editable** - Override inferred type
* DataPancake infers from source data; override when inference is incorrect or you need specific types/precision

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### Numeric Type Configuration

**Data Platform Data Type Precision (`DATA_PLATFORM_DATA_TYPE_PRECISION`)**

* For numeric types, precision (total number of digits)
* **Editable** - Applies to `DECIMAL`, `DEC`, `NUMERIC`, `NUMBER`
* Example: For `NUMBER(10,2)`, precision is 10

**Data Platform Data Type Scale (`DATA_PLATFORM_DATA_TYPE_SCALE`)**

* For numeric types, scale (number of decimal places)
* **Editable** - Applies to `DECIMAL`, `DEC`, `NUMERIC`, `NUMBER`
* Example: For `NUMBER(10,2)`, scale is 2

**Note:** Set based on known data ranges; avoid over-sizing (wastes storage).

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### DateTime Configuration

**Data Platform Data Type DateTime Format (`DATA_PLATFORM_DATA_TYPE_DATETIMEFORMAT`)**

* Comma-separated list of valid Snowflake datetime formats
* Used for accurate datetime parsing
* **Editable** - Users can specify formats if inference is incorrect
* Example: `'MM/DD/YYYY', 'YYYY-MM-DD', 'MM-DD-YYYY HH24:MI:SS'`
* See [Snowflake datetime formats documentation](https://docs.snowflake.com/en/sql-reference/data-types-datetime#date-and-time-formats)

**Use DateTime Format (`USE_DATETIME_FORMAT`)**

* Boolean for using datetime formats in Dynamic Table SQL generation
* **Editable**
* `FALSE`: Uses `try_to_{data_type}` with `AUTO` detection
* `TRUE`: Uses `DATA_PLATFORM_DATA_TYPE_DATETIMEFORMAT` for parsing

**When to use formats:** Consistent formats, need precise control, `AUTO` unreliable.

**When to use AUTO:** Inconsistent/unknown formats, want Snowflake auto-detection.

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### Key Identification

**Is Primary Key (`IS_PRIMARY_KEY`)**

* Boolean indicating if attribute is part of primary key
* **Editable** - Multiple attributes can be marked (composite keys)
* Used for metadata table generation and relationship tracking

**Has All Unique Values (`HAS_ALL_UNIQUE_VALUES`)**

* Boolean indicating if all values in this version are unique
* **Editable** - Mark based on data analysis
* Useful for identifying potential primary keys

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### Data Quality Indicators

**Is Enum (`IS_ENUM`)**

* Boolean indicating if version contains only enum values
* **Editable** - Mark for categorical data (e.g., status codes)
* Useful for validation and documentation

**Has All Null Values (`HAS_ALL_NULL_VALUES`)**

* Boolean indicating if all values are null
* **Read-only** - Detected during scanning
* Useful for identifying unused attributes

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## Reference Fields (Read-only)

Shown for reference in Destination Schema tab:

* `ATTRIBUTE_PATH` - Attribute identification
* `POLYMORPHIC_ATTRIBUTE_NAME` - Version name
* `SOURCE_DATA_TYPE` - Source type reference
* `SAMPLE_VALUE` - Data content reference
* `ATTRIBUTE_FOREIGN_KEY_COLUMNS` - Configured in Foreign Keys section

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## Data Type Selection

**String types:**

* `VARCHAR` - Variable length without constraints
* `VARCHAR(n)` - Known maximum length
* `VARIANT` - Truly variable type, preserve original structure

**Numeric types:**

* Set precision/scale based on known data ranges
* Balance flexibility vs. storage efficiency
* Financial data: Use appropriate scale (typically 2-4 decimal places)

**DateTime types:**

* `DATE` - Date only
* `TIME` - Time only
* `TIMESTAMP_NTZ` - Timestamp without timezone
* `TIMESTAMP_LTZ` - Timestamp with local timezone
* `TIMESTAMP_TZ` - Timestamp with timezone

See [Snowflake data types documentation](https://docs.snowflake.com/en/sql-reference/data-types.html) for details.

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## Common Scenarios

**Correcting incorrect type inference:**

1. Change `DATA_PLATFORM_DATA_TYPE` to correct type
2. Set `DATA_PLATFORM_DATA_TYPE_PRECISION` and `DATA_PLATFORM_DATA_TYPE_SCALE` if numeric
3. Verify with sample data

**Configuring datetime formats:**

1. Check `SAMPLE_VALUE` for actual format
2. Set `DATA_PLATFORM_DATA_TYPE_DATETIMEFORMAT` with correct format(s)
3. Set `USE_DATETIME_FORMAT = TRUE`
4. Test with code generation

**Setting primary keys:**

1. Identify attributes that uniquely identify records
2. Set `IS_PRIMARY_KEY = TRUE` (multiple for composite keys)
3. Verify uniqueness with `HAS_ALL_UNIQUE_VALUES`

**Optimizing numeric types:**

1. Analyze actual data ranges
2. Set `DATA_PLATFORM_DATA_TYPE_PRECISION` to minimum needed
3. Set `DATA_PLATFORM_DATA_TYPE_SCALE` appropriately


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