Snowflake targets

To configure a target model that will be written to Snowflake, reference the following sections.

Type & Format

Snowflake supports the following materialization types for target models. The type determines the underlying physical format of your target model.

Materialization type	Description
View (default)	Rebuilt as a view on each run. Views always reflect the latest source data but don’t store any data themselves.
Table	Rebuilt as a table on each run. Tables are fast to query but can take longer to build. Target tables support multiple write modes.
Ephemeral	Not built in the database. The model’s logic is inlined into downstream models using a common table expression (CTE). Use for lightweight transformations early in your DAG.

Location

Review the location where your model will be written. Any changes you make to the Overwrite location section will be reflected in the Location that Prophecy generates.

Location Parameter	Description	Advanced mode
Catalog	Catalog where the model will be created.	Yes
Schema	Schema inside the catalog where the model will be created.	Yes
Alias	Sets the name of the resulting table or view. Defaults to the model name if not specified.	No

Schema

Define the schema of the dataset and optionally configure additional properties.

The schema includes column names, column data types, and optional column metadata. When you expand a row in the Schema table, you can add a column description, apply column tags, and enable/disable quoting for column names.

Properties

Each property maps to a certain dbt configuration that may be generic to dbt or specific to a platform like Snowflake. If you do not add a property explicitly in the Schema tab, Prophecy uses the dbt default for that property.

Property	Description	Config type
Dataset Tags	Add tags to the dataset. These tags can be used as part of the resource selection syntax in dbt.	Generic
Contract Enforced	Enforce a contract for the model schema, preventing unintended changes.	Generic
Show Docs	Control whether or not nodes are shown in the auto-generated documentation website.	Generic
Enabled	Control whether the model is included in builds. When a resource is disabled, dbt will not consider it as part of your project.	Generic
Meta	Set metadata for the table using key-value pairs.	Generic
Group	Assign a group to the table.	Generic
Persist Docs Columns	Save column descriptions in the database.	Generic
Persist Docs Relations	Save model descriptions in the database.	Generic
Cluster By	Order and cluster the table by specified keys to reduce Snowflake’s automatic clustering work. Accepts a string or list of strings.	Snowflake
Transient	Write the table as a Snowflake transient table, which does not preserve history and can reduce costs. By default, all Snowflake tables created by dbt are transient.	Snowflake
Automatic Clustering	Enables automatic clustering if manual clustering is enabled for your Snowflake account. You do not need to use this property if automatic clustering is enabled by default on your Snowflake account.	Snowflake
Snowflake Warehouse	Override the Snowflake warehouse that is used for this target model.	Snowflake
Query Tag	Add query tags to your model.	Snowflake
Copy Grants	Enable to preserve any existing access control grants (like GRANT SELECT TO ROLE analyst) when rebuilding the table or view.	Snowflake
Secure	Create a secure view that hides underlying data from unauthorized users.	Snowflake
Target Lag	Defines how frequently the table should be automatically refreshed.	Snowflake

info

For more detailed information, see the dbt reference documentation.

SQL Query

Add a custom SQL query at the end of your target model using the Snowflake SQL dialect. This allows you to apply a final transformation step, which can be useful if you're importing an existing codebase and need to add conditions or filters to the final output. Custom queries support Jinja, dbt templating, and variable usage for your last-mile data processing.

You can reference any column present in the list of input ports beside the SQL query. You can only add additional input ports—the output port cannot be edited.

Write Options

The Write Options tab lets you determine how you will store your processed data and handle changes to the data over time.

Write Mode	Description
Overwrite (default)	Replaces all existing data with new data on each run. The incoming table must have the same schema as the existing table.
Append	Adds new rows to the existing table. Best used when unique keys aren’t required and duplicate records are acceptable. For key-based updates, use Merge instead.
Merge	Updates existing records and inserts new ones based on defined keys. Supports multiple merge strategies to handle changes accurately over time.

Merge approaches

When you select the Merge write mode, there are multiple merge approaches to choose from. To find an example use case for each strategy, see Merge approach examples.

info

For more detailed information about merge approaches in the backend, see Merge behavior in the dbt documentation.

Specify columns

Only update specified columns during the merge. All other columns remain unchanged.

Parameter	Description
Unique Key	The key used to match existing records in the target dataset for merging.
Use Predicate	Lets you add conditions that specify when to apply the merge.
Use a condition to filter data or incremental runs	Enables applying conditions for filtering the incoming data into the table.
On Schema Change	Specifies how schema changes should be handled during the merge process. ignore: Newly added columns will not be written to the model. This is the default option. fail: Triggers an error message when the source and target schemas diverge. append_new_columns: Append new columns to the existing table. sync_all_columns: Adds any new columns to the existing table, and removes any columns that are now missing. Includes data type changes. This option uses the output of the previous gem.

SCD2

Tracks historical changes by adding new rows instead of updating existing ones. Each record will include additional columns containing start and end timestamps to indicate when a record was valid.

Parameter	Description
Unique Key	The key used to match existing records in the target dataset for merging.
Invalidate deleted rows	When enabled, records that match deleted rows will be marked as no longer valid.
Determine new records by checking timestamp column	Recognizes new records by the time from the timestamp column that you define.
Determine new records by looking for differences in column values	Recognizes new records based on a change of values in one or more specified columns.

Use delete and insert

Update existing data in two steps. First, this process removes rows from the target table that match the unique key values present in the new (incremental) data. Then, it inserts the new rows into the table.

Parameter	Description
Unique Key	The key used to match existing records in the target dataset for merging.
Use Predicate	Lets you add conditions that specify when to apply the merge.
Use a condition to filter data or incremental runs	Enables applying conditions for filtering the incoming data into the table.
On Schema Change	Specifies how schema changes should be handled during the merge process. ignore: Newly added columns will not be written to the model. This is the default option. fail: Triggers an error message when the source and target schemas diverge. append_new_columns: Append new columns to the existing table. sync_all_columns: Adds any new columns to the existing table, and removes any columns that are now missing. Includes data type changes. This option uses the output of the previous gem.

Data Tests

A data test is an assertion you define about a dataset in your project. Data tests are run on target models to ensure the quality and integrity of the final data that gets written to the warehouse. Learn how to build tests in Data tests.