GitBook: [#322] Redis

docs/getting-started/quickstart.md

@@ -9,19 +9,19 @@ In this tutorial we will
 
 You can run this tutorial in Google Colab or run it on your localhost, following the guided steps below.
 
-![](../.gitbook/assets/colab_logo\_32px.png)[**Run in Google Colab**](https://colab.research.google.com/github/feast-dev/feast/blob/master/examples/quickstart/quickstart.ipynb)****
+![](../.gitbook/assets/colab\_logo\_32px.png)[**Run in Google Colab**](https://colab.research.google.com/github/feast-dev/feast/blob/master/examples/quickstart/quickstart.ipynb)****
 
 ## Overview
 
-In this tutorial, we use feature stores to generate training data and power online model inference for a ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow: 
+In this tutorial, we use feature stores to generate training data and power online model inference for a ride-sharing driver satisfaction prediction model. Feast solves several common issues in this flow: 
 
 1. **Training-serving skew and complex data joins:** Feature values often exist across multiple tables. Joining these datasets can be complicated, slow, and error-prone.
    * Feast joins these tables with battle-tested logic that ensures _point-in-time_ correctness so future feature values do not leak to models.
-   * _\*Upcoming_: Feast alerts users to offline / online skew with data quality monitoring. 
-2. **Online feature availability:** At inference time, models often need access to features that aren't readily available and need to be precomputed from other datasources. 
+   * _\*Upcoming_: Feast alerts users to offline / online skew with data quality monitoring. 
+2. **Online feature availability:** At inference time, models often need access to features that aren't readily available and need to be precomputed from other datasources. 
    * Feast manages deployment to a variety of online stores (e.g. DynamoDB, Redis, Google Cloud Datastore) and ensures necessary features are consistently _available_ and _freshly computed_ at inference time.
 3. **Feature reusability and model versioning:** Different teams within an organization are often unable to reuse features across projects, resulting in duplicate feature creation logic. Models have data dependencies that need to be versioned, for example when running A/B tests on model versions.
-   * Feast enables discovery of and collaboration on previously used features and enables versioning of sets of features (via _feature services_). 
+   * Feast enables discovery of and collaboration on previously used features and enables versioning of sets of features (via _feature services_). 
    * Feast enables feature transformation so users can re-use transformation logic across online / offline usecases and across models.
 
 ## Step 1: Install Feast
@@ -40,7 +40,7 @@ pip install feast
 
 ## Step 2: Create a feature repository
 
-Bootstrap a new feature repository using `feast init` from the command line. 
+Bootstrap a new feature repository using `feast init` from the command line. 
 
 {% tabs %}
 {% tab title="Bash" %}
@@ -117,7 +117,7 @@ driver_hourly_stats_view = FeatureView(
 {% endtab %}
 {% endtabs %}
 
-![Demo parquet data: data/driver_stats.parquet](../.gitbook/assets/screen-shot-2021-08-23-at-2.35.18-pm.png)
+![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).
 
@@ -127,7 +127,7 @@ Valid values for `provider` in `feature_store.yaml` are:
 * gcp: use BigQuery / Google Cloud Datastore
 * aws: use Redshift / DynamoDB
 
-A custom setup (e.g. using the built-in support for Redis) can be made by following Creating a custom provider
+To use a custom provider, see [adding a custom provider](../how-to-guides/creating-a-custom-provider.md). There are also several plugins maintained by the community: [Azure](https://github.com/Azure/feast-azure), [Postgres](https://github.com/nossrannug/feast-postgres), and [Hive](https://github.com/baineng/feast-hive). Note that the choice of provider gives sensible defaults but does not enforce those choices; for example, if you choose the AWS provider, you can use [Redis](../reference/online-stores/redis.md) as an online store alongside Redshift as an offline store.
 
 ## Step 3: Register feature definitions and deploy your feature store
 

docs/how-to-guides/feast-gcp-aws/install-feast.md

@@ -2,19 +2,24 @@
 
 Install Feast using [pip](https://pip.pypa.io):
 
-```text
+```
 pip install feast
 ```
 
-Install Feast with GCP dependencies \(required when using BigQuery or Firestore\):
+Install Feast with GCP dependencies (required when using BigQuery or Firestore):
 
-```text
+```
 pip install 'feast[gcp]'
 ```
 
-Install Feast with AWS dependencies \(required when using Redshift or DynamoDB\):
+Install Feast with AWS dependencies (required when using Redshift or DynamoDB):
 
-```text
+```
 pip install 'feast[aws]'
 ```
 
+Install Feast with Redis dependencies (required when using Redis, either through AWS Elasticache or independently):
+
+```
+pip install 'feast[redis]'
+```

docs/reference/online-stores/redis.md

@@ -5,7 +5,7 @@
 The [Redis](https://redis.io) online store provides support for materializing feature values into Redis.
 
 * Both Redis and Redis Cluster are supported
-* The data model used to store feature values in Redis is described in more detail [here](../../specs/online_store_format.md).
+* The data model used to store feature values in Redis is described in more detail [here](../../specs/online\_store\_format.md).
 
 ## Examples
 
@@ -36,4 +36,4 @@ online_store:
 ```
 {% endcode %}
 
-Configuration options are available [here](https://rtd.feast.dev/en/master/#feast.repo_config.RedisOnlineStoreConfig).
+Configuration options are available [here](https://rtd.feast.dev/en/master/#feast.infra.online\_stores.redis.RedisOnlineStoreConfig).