doc: Fix links (#5052)

Signed-off-by: Alexei Pertsev <[email protected]>

Author: a-pertsev

docs/README.md

@@ -56,7 +56,7 @@ Feast helps ML platform/MLOps teams with DevOps experience productionize real-ti
 
 ### Feast is not
 
-* **An** [**ETL**](https://en.wikipedia.org/wiki/Extract,\_transform,\_load) / [**ELT**](https://en.wikipedia.org/wiki/Extract,\_load,\_transform) **system.** Feast is not a general purpose data pipelining system. Users often leverage tools like [dbt](https://www.getdbt.com) to manage upstream data transformations. Feast does support some [transformations](getting-started/architecture/feature-transformetion.md).
+* **An** [**ETL**](https://en.wikipedia.org/wiki/Extract,\_transform,\_load) / [**ELT**](https://en.wikipedia.org/wiki/Extract,\_load,\_transform) **system.** Feast is not a general purpose data pipelining system. Users often leverage tools like [dbt](https://www.getdbt.com) to manage upstream data transformations. Feast does support some [transformations](getting-started/architecture/feature-transformation.md).
 * **A data orchestration tool:** Feast does not manage or orchestrate complex workflow DAGs. It relies on upstream data pipelines to produce feature values and integrations with tools like [Airflow](https://airflow.apache.org) to make features consistently available.
 * **A data warehouse:** Feast is not a replacement for your data warehouse or the source of truth for all transformed data in your organization. Rather, Feast is a lightweight downstream layer that can serve data from an existing data warehouse (or other data sources) to models in production.
 * **A database:** Feast is not a database, but helps manage data stored in other systems (e.g. BigQuery, Snowflake, DynamoDB, Redis) to make features consistently available at training / serving time

docs/getting-started/components/offline-store.md

@@ -8,7 +8,7 @@ Offline stores are primarily used for two reasons:
 1. Building training datasets from time-series features.
 2. Materializing \(loading\) features into an online store to serve those features at low-latency in a production setting.
 
-Offline stores are configured through the [feature\_store.yaml](../../reference/offline-stores/).
+Offline stores are configured through the [feature\_store.yaml](../../reference/feature-repository/feature-store-yaml.md).
 When building training datasets or materializing features into an online store, Feast will use the configured offline store with your configured data sources to execute the necessary data operations.
 
 Only a single offline store can be used at a time.