docs: Fix broken links (#2583)

adchia · web-flow · commit 047d469346cc · 2022-04-20T18:24:22.000-04:00
Signed-off-by: Danny Chiao &lt;danny@tecton.ai&gt;
diff --git a/docs/getting-started/faq.md b/docs/getting-started/faq.md
@@ -3,7 +3,7 @@
 {% hint style="info" %}
 **Don't see your question?**
 
-We encourage you to ask questions on [Slack](https://slack.feast.dev) or [Github](https://github.com/feast-dev/feast). Even better, once you get an answer, add the answer to this FAQ via a [pull request](../project/development-guide.md)!
+We encourage you to ask questions on [Slack](https://slack.feast.dev) or [GitHub](https://github.com/feast-dev/feast). Even better, once you get an answer, add the answer to this FAQ via a [pull request](../project/development-guide.md)!
 {% endhint %}
 
 ## Getting started
@@ -83,7 +83,7 @@ Yes. For example, the Postgres [connector](https://github.com/nossrannug/feast-p
 
 Yes. There are two ways to use S3 in Feast:
 
-* Using Redshift as a data source via Spectrum ([AWS tutorial](https://docs.aws.amazon.com/redshift/latest/dg/tutorial-nested-data-create-table.html)), and then continuing with the [Running Feast with GCP/AWS](../how-to-guides/feast-gcp-aws/) guide. See a [presentation](https://youtu.be/pMFbRJ7AnBk?t=9463) we did on this at our apply() meetup.
+* Using Redshift as a data source via Spectrum ([AWS tutorial](https://docs.aws.amazon.com/redshift/latest/dg/tutorial-nested-data-create-table.html)), and then continuing with the [Running Feast with Snowflake/GCP/AWS](../how-to-guides/feast-snowflake-gcp-aws/) guide. See a [presentation](https://youtu.be/pMFbRJ7AnBk?t=9463) we did on this at our apply() meetup.
 * Using the `s3_endpoint_override` in a `FileSource` data source. This endpoint is more suitable for quick proof of concepts that won't necessarily scale for production use cases.
 
 ### How can I use Spark with Feast?
diff --git a/docs/how-to-guides/running-feast-in-production.md b/docs/how-to-guides/running-feast-in-production.md
@@ -274,7 +274,7 @@ For seamless integration with Kubernetes (including services created by Feast He
 
 ## 5. Ingesting features from a stream source
 
-Recently Feast added functionality for [stream ingestion](../reference/alpha-stream-ingestion.md).
+Recently Feast added functionality for [stream ingestion](../reference/data-sources/push.md).
 Please note that this is still in an early phase and new incompatible changes may be introduced.
 
 ### 5.1. Using Python SDK in your Apache Spark / Beam pipeline
diff --git a/docs/reference/alpha-aws-lambda-feature-server.md b/docs/reference/alpha-aws-lambda-feature-server.md
@@ -8,7 +8,7 @@ To enable this feature, run **`feast alpha enable aws_lambda_feature_server`**
 
 ## Overview
 
-The AWS Lambda feature server is an HTTP endpoint that serves features with JSON I/O, deployed as a Docker image through AWS Lambda and AWS API Gateway. This enables users to get features from Feast using any programming language that can make HTTP requests. A [local feature server](feature-server.md) is also available. A remote feature server on GCP Cloud Run is currently being developed.
+The AWS Lambda feature server is an HTTP endpoint that serves features with JSON I/O, deployed as a Docker image through AWS Lambda and AWS API Gateway. This enables users to get features from Feast using any programming language that can make HTTP requests. A [local feature server](feature-servers/python-feature-server.md) is also available. A remote feature server on GCP Cloud Run is currently being developed.
 
 ## Deployment
 
diff --git a/docs/reference/feast-cli-commands.md b/docs/reference/feast-cli-commands.md
@@ -2,7 +2,7 @@
 
 ## Overview
 
-The Feast CLI comes bundled with the Feast Python package. It is immediately available after [installing Feast](../how-to-guides/feast-gcp-aws/install-feast.md).
+The Feast CLI comes bundled with the Feast Python package. It is immediately available after [installing Feast](../how-to-guides/feast-snowflake-gcp-aws/install-feast.md).
 
 ```text
 Usage: feast [OPTIONS] COMMAND [ARGS]...
@@ -54,7 +54,7 @@ feast apply
 **What does Feast apply do?**
 
 1. Feast will scan Python files in your feature repository and find all Feast object definitions, such as feature views, entities, and data sources.
-2. Feast will validate your feature definitions
+2. Feast will validate your feature definitions (e.g. for uniqueness of features)
 3. Feast will sync the metadata about Feast objects to the registry. If a registry does not exist, then it will be instantiated. The standard registry is a simple protobuf binary file that is stored on disk \(locally or in an object store\).
 4. Feast CLI will create all necessary feature store infrastructure. The exact infrastructure that is deployed or configured depends on the `provider` configuration that you have set in `feature_store.yaml`. For example, setting `local` as your provider will result in a `sqlite` online store being created. 
 
