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Fix Snowflake docs #2270

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Feb 2, 2022
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Fix Snowflake docs #2270

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5455ace
Fix Snowflake docs
felixwang9817 Feb 2, 2022
d36cb16
Small fixes
felixwang9817 Feb 2, 2022
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30 changes: 10 additions & 20 deletions docs/tutorials/driver-stats-using-snowflake.md
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Original file line number Diff line number Diff line change
@@ -1,31 +1,23 @@
---
description: >-
Initial demonstration of using Snowflake with Feast as both and Offline & Online store
using the snowflake demo template.
Initial demonstration of Snowflake as an offline store with Feast, using the Snowflake demo template.
---

# Drivers Stats using Snowflake

In the following steps below, we will setup a sample feast project that leverages Snowflake
as an Offline Store.
In the steps below, we will set up a sample Feast project that leverages Snowflake
as an offline store.

Starting with data in a Snowflake table, we will register that table to the feature store and
define features associated with the columns in that table. From there, we will generate historical
training data based on those feature definitions. We then will materialize the latest feature values
given our feature definitions into our online feature store. Lastly, we will then call
for those latest feature values.
Starting with data in a Snowflake table, we will register that table to the feature store and define features associated with the columns in that table. From there, we will generate historical training data based on those feature definitions and then materialize the latest feature values into the online store. Lastly, we will retrieve the materialized feature values.

Our template that you will leverage will generate new data related to driver statistics.
From there, we will show you code snippets that will call to the offline store for generating
training datasets, and then the code for calling the online store to serve you the
latest feature values to serve models in production.
Our template will generate new data containing driver statistics. From there, we will show you code snippets that will call to the offline store for generating training datasets, and then the code for calling the online store to serve you the latest feature values to serve models in production.

## Snowflake Offline/Online Store Example

#### Install feast-snowflake

```shell
pip install feast[snowflake]
pip install 'feast[snowflake]'
```

#### Get a Snowflake Trial Account (Optional)
Expand Down Expand Up @@ -54,9 +46,7 @@ The following files will automatically be created in your project folder:

#### Inspect `feature_store.yaml`

Here you will see the information that you entered. This template will look to use
Snowflake as both an Offline & Online store. The main thing to remember is by default,
Snowflake Objects have ALL CAPS names unless lower case was specified.
Here you will see the information that you entered. This template will use Snowflake as an offline store and SQLite as the online store. The main thing to remember is by default, Snowflake objects have ALL CAPS names unless lower case was specified.

{% code title="feature_store.yaml" %}
```yaml
Expand Down Expand Up @@ -98,7 +88,7 @@ fs.apply([driver, driver_stats_fv])
```
{% endcode %}

#### Create a dummy training dataframe, then call our Offline store to add additional columns
#### Create a dummy training dataframe, then call our offline store to add additional columns
{% code title="test.py" %}
```python
entity_df = pd.DataFrame(
Expand All @@ -123,14 +113,14 @@ training_df = fs.get_historical_features(
```
{% endcode %}

#### Materialize the latest feature values into our Online store
#### Materialize the latest feature values into our online store
{% code title="test.py" %}
```python
fs.materialize_incremental(end_date=datetime.now())
```
{% endcode %}

#### Retrieve the latest values from our Online store based on our Entity Key
#### Retrieve the latest values from our online store based on our entity key
{% code title="test.py" %}
```python
online_features = fs.get_online_features(
Expand Down