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May 19, 2022
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chore: Local data inspection #2720

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12 changes: 10 additions & 2 deletions docs/getting-started/quickstart.md
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Expand Up @@ -125,8 +125,6 @@ driver_stats_fs = FeatureService(
{% endtab %}
{% endtabs %}

![Demo parquet data: data/driver\_stats.parquet](../.gitbook/assets/screen-shot-2021-08-23-at-2.35.18-pm.png)

The key line defining the overall architecture of the feature store is the **provider**. This defines where the raw data exists (for generating training data & feature values for serving), and where to materialize feature values to in the online store (for serving).

Valid values for `provider` in `feature_store.yaml` are:
Expand All @@ -139,6 +137,16 @@ Note that there are many other sources Feast works with, including Azure, Hive,

A custom setup can also be made by following [adding a custom provider](../how-to-guides/creating-a-custom-provider.md).

### Inspecting the raw data

The raw feature data we have in this demo is stored in a local parquet file. The dataset captures hourly stats of a driver in a ride-sharing app.

```python
import pandas as pd
pd.read_parquet("data/driver_stats.parquet")
```

![Demo parquet data: data/driver\_stats.parquet](../.gitbook/assets/screen-shot-2021-08-23-at-2.35.18-pm.png)

## Step 3: Register feature definitions and deploy your feature store

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