> ## Documentation Index
> Fetch the complete documentation index at: https://docs.featureform.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Snowflake

> Featureform supports [Snowflake](https://www.snowflake.com/) as an Offline Store.

## Configuration

First we have to add a declarative Snowflake configuration in Python.

### Credentials with Account and Organization

```py snowflake_config.py theme={null}
import featureform as ff

ff.register_snowflake(
    name = "snowflake_docs",
    description = "Example offline store",
    username = snowflake_username,
    password = snowflake_password,
    account = snowflake_account,
    organization = snowflake_org,
    database = snowflake_database,
    schema = snowflake_schema,
    warehouse = snowflake_wh,
    catalog = ff.SnowflakeCatalog(
        external_volume = snowflake_external_volume,
        base_location = snowflake_base_location,
        table_config=ff.SnowflakeDynamicTableConfig(
            target_lag='1 days',
            refresh_mode=ff.RefreshMode.INCREMENTAL,
            initialize=ff.Initialize.ON_CREATE,
        ),
    )
)
```

### Legacy Credentials

[Older Snowflake accounts](https://docs.snowflake.com/en/user-guide/admin-account-identifier.html#using-an-account-locator-as-an-identifier) may have credentials that use an `Account Locator` rather than an `account` and `organization` to connect. Featureform provides a separate registration function to support these credentials.

```py snowflake_config.py theme={null}
import featureform as ff

ff.register_snowflake_legacy(
    name = "snowflake_docs",
    description = "Example offline store",
    username = snowflake_username,
    password =  snowflake_password,
    account_locator = snowflake_account_locator,
    database = snowflake_database,
    schema = snowflake_schema,
    warehouse = snowflake_wh,
    catalog = ff.SnowflakeCatalog(
        external_volume = snowflake_external_volume,
        base_location = snowflake_base_location,
        table_config=ff.SnowflakeDynamicTableConfig(
            target_lag='1 days',
            refresh_mode=ff.RefreshMode.INCREMENTAL,
            initialize=ff.Initialize.ON_CREATE,
        ),
    )
)
```

### Dynamic Apache Iceberg™ Tables

Featureform's Snowflake integration leverages [dynamic Apache Iceberg™ tables](https://docs.snowflake.com/en/user-guide/dynamic-tables-create-iceberg) to offer automated data transformation pipelines from SQL transformations to training sets.

This requires users to [create an external volume](https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-external-volume) *prior to* registering Snowflake as a provider. See the repo [`terraforming-snowflake-external-volumes`](https://github.com/featureform/terraforming-snowflake-external-volumes) for an example of creating an external volume backed by an AWS S3 bucket using Terraform.

Additionally, users *may* have to manually [enable change tracking](https://docs.snowflake.com/en/user-guide/dynamic-tables-create#enable-change-tracking) on source tables for incremental refreshes to work.

The name of the external volume, as well as a folder name in the bucket (i.e. `base_location`), is required to instantiate `ff.SnowflakeCatalog`. Currently, the only option for catalog is to use [Snowflake as the Iceberg catalog](https://docs.snowflake.com/en/user-guide/tables-iceberg#label-tables-iceberg-catalog-options).

Additionally, users can set "provider-level" values for [target lag and refresh mode](https://docs.snowflake.com/en/user-guide/dynamic-tables-refresh) that will be used by all resources (i.e. SQL transformations, features, labels and training sets) *unless otherwise specified at the resource level*.

### Session Parameters

Users may optionally add [session parameters](https://docs.snowflake.com/en/sql-reference/parameters#session-parameters) when configuring Snowflake as an offline store. The parameters will be used when Featureform creates a connection with Snowflake.

For example, providing a value for `query_tag` at configuration ensures all queries issues by Featureform will have the value of `query_tag` applied to them, which will be visible in the Snowflake UI.

### Applying

Once our config file is complete, we can apply it to our Featureform deployment

```py theme={null}
featureform apply snowflake_config.py --host $FEATUREFORM_HOST
```

We can re-verify that the provider is created by checking the [Providers tab of the Feature Registry](/getting-started/exploring-the-feature-registry).

### Mutable Configuration Fields

Many of the values provided at the time of registration can be changed if users want to re-apply the same Python file (e.g. `snowflake_config.py`) again with different values. The following list of configurations fields are mutable:

* `description`

* `username`

* `password`

* `role`

* `warehouse`

* `database`

* `schema`

* `session_params`

## Implementation

### Datasets

#### Primary Datasets

Primary datasets (i.e. tables) are read-only and are only used as inputs in transformations, features and/or labels.

### Transformation Sources

SQL transformations are used to create a dynamic Apache Iceberg™ table. The refresh mode (i.e. full or incremental) depends on the user-defined SQL query, so users should be aware of query expressions, keywords, etc. that might prevent incremental refreshes.

### Offline to Inference Store Materialization

When a feature is registered, Featureform creates an internal transformation to get the newest value of every feature and its associated entity. A Kubernetes job is then kicked off to sync this up with the inference store.

### Training Set Generation

Training set are created from one or more features and a label. Label are registered separately as a dynamic table to ensure the latest label values are used in the training set. When users define a timestamp column for a feature and label combination, Featureform uses Snowflake's [`ASOF JOIN`](https://docs.snowflake.com/en/sql-reference/constructs/asof-join) to combine feature values with their labels.