diff --git a/docs/reference/offline-stores/bigquery.md b/docs/reference/offline-stores/bigquery.md
index 0e286d78c49..b7607abf595 100644
--- a/docs/reference/offline-stores/bigquery.md
+++ b/docs/reference/offline-stores/bigquery.md
@@ -7,6 +7,9 @@ The BigQuery offline store provides support for reading [BigQuerySources](../dat
* All joins happen within BigQuery.
* Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to BigQuery as a table (marked for expiration) in order to complete join operations.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[gcp]'`. You can get started by then running `feast init -t gcp`.
+
## Example
{% code title="feature_store.yaml" %}
diff --git a/docs/reference/offline-stores/mssql.md b/docs/reference/offline-stores/mssql.md
index bec0c8deb82..e352b3dd2aa 100644
--- a/docs/reference/offline-stores/mssql.md
+++ b/docs/reference/offline-stores/mssql.md
@@ -6,6 +6,9 @@ The MsSQL offline store provides support for reading [MsSQL Sources](../data-sou
* Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[azure]'`. You can get started by then following this [tutorial](https://github.com/feast-dev/feast/blob/master/docs/tutorials/azure/README.md).
+
## Disclaimer
The MsSQL offline store does not achieve full test coverage.
@@ -34,26 +37,26 @@ offline_store:
The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Spark offline store.
-| | MsSql |
-| :-------------------------------- | :-- |
-| `get_historical_features` (point-in-time correct join) | yes |
-| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
-| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
-| `offline_write_batch` (persist dataframes to offline store) | no |
-| `write_logged_features` (persist logged features to offline store) | no |
+| | MsSql |
+| :----------------------------------------------------------------- | :---- |
+| `get_historical_features` (point-in-time correct join) | yes |
+| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
+| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
+| `offline_write_batch` (persist dataframes to offline store) | no |
+| `write_logged_features` (persist logged features to offline store) | no |
Below is a matrix indicating which functionality is supported by `MsSqlServerRetrievalJob`.
-| | MsSql |
-| --------------------------------- | --- |
-| export to dataframe | yes |
-| export to arrow table | yes |
-| export to arrow batches | no |
-| export to SQL | no |
-| export to data lake (S3, GCS, etc.) | no |
-| export to data warehouse | no |
-| local execution of Python-based on-demand transforms | no |
-| remote execution of Python-based on-demand transforms | no |
-| persist results in the offline store | yes |
+| | MsSql |
+| ----------------------------------------------------- | ----- |
+| export to dataframe | yes |
+| export to arrow table | yes |
+| export to arrow batches | no |
+| export to SQL | no |
+| export to data lake (S3, GCS, etc.) | no |
+| export to data warehouse | no |
+| local execution of Python-based on-demand transforms | no |
+| remote execution of Python-based on-demand transforms | no |
+| persist results in the offline store | yes |
To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/offline-stores/postgres.md b/docs/reference/offline-stores/postgres.md
index 506666fc378..094ab4885f4 100644
--- a/docs/reference/offline-stores/postgres.md
+++ b/docs/reference/offline-stores/postgres.md
@@ -10,6 +10,9 @@ The PostgreSQL offline store provides support for reading [PostgreSQLSources](..
The PostgreSQL offline store does not achieve full test coverage.
Please do not assume complete stability.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[postgres]'`. You can get started by then running `feast init -t postgres`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -42,29 +45,29 @@ The full set of configuration options is available in [PostgreSQLOfflineStoreCon
The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the PostgreSQL offline store.
-| | Postgres |
-| :-------------------------------- | :-- |
-| `get_historical_features` (point-in-time correct join) | yes |
-| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
-| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
-| `offline_write_batch` (persist dataframes to offline store) | no |
-| `write_logged_features` (persist logged features to offline store) | no |
+| | Postgres |
+| :----------------------------------------------------------------- | :------- |
+| `get_historical_features` (point-in-time correct join) | yes |
+| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
+| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
+| `offline_write_batch` (persist dataframes to offline store) | no |
+| `write_logged_features` (persist logged features to offline store) | no |
Below is a matrix indicating which functionality is supported by `PostgreSQLRetrievalJob`.
-| | Postgres |
-| --------------------------------- | --- |
-| export to dataframe | yes |
-| export to arrow table | yes |
-| export to arrow batches | no |
-| export to SQL | yes |
-| export to data lake (S3, GCS, etc.) | yes |
-| export to data warehouse | yes |
-| export as Spark dataframe | no |
-| local execution of Python-based on-demand transforms | yes |
-| remote execution of Python-based on-demand transforms | no |
-| persist results in the offline store | yes |
-| preview the query plan before execution | yes |
-| read partitioned data | yes |
+| | Postgres |
+| ----------------------------------------------------- | -------- |
+| export to dataframe | yes |
+| export to arrow table | yes |
+| export to arrow batches | no |
+| export to SQL | yes |
+| export to data lake (S3, GCS, etc.) | yes |
+| export to data warehouse | yes |
+| export as Spark dataframe | no |
+| local execution of Python-based on-demand transforms | yes |
+| remote execution of Python-based on-demand transforms | no |
+| persist results in the offline store | yes |
+| preview the query plan before execution | yes |
+| read partitioned data | yes |
To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/offline-stores/redshift.md b/docs/reference/offline-stores/redshift.md
index 2cdf49bdb9f..98092c44bec 100644
--- a/docs/reference/offline-stores/redshift.md
+++ b/docs/reference/offline-stores/redshift.md
@@ -7,6 +7,9 @@ The Redshift offline store provides support for reading [RedshiftSources](../dat
* All joins happen within Redshift.
* Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Redshift temporarily in order to complete join operations.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[aws]'`. You can get started by then running `feast init -t aws`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -32,30 +35,30 @@ The full set of configuration options is available in [RedshiftOfflineStoreConfi
The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Redshift offline store.
-| | Redshift |
-| :-------------------------------- | :-- |
-| `get_historical_features` (point-in-time correct join) | yes |
-| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
-| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
-| `offline_write_batch` (persist dataframes to offline store) | yes |
-| `write_logged_features` (persist logged features to offline store) | yes |
+| | Redshift |
+| :----------------------------------------------------------------- | :------- |
+| `get_historical_features` (point-in-time correct join) | yes |
+| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
+| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
+| `offline_write_batch` (persist dataframes to offline store) | yes |
+| `write_logged_features` (persist logged features to offline store) | yes |
Below is a matrix indicating which functionality is supported by `RedshiftRetrievalJob`.
-| | Redshift |
-| --------------------------------- | --- |
-| export to dataframe | yes |
-| export to arrow table | yes |
-| export to arrow batches | yes |
-| export to SQL | yes |
-| export to data lake (S3, GCS, etc.) | no |
-| export to data warehouse | yes |
-| export as Spark dataframe | no |
-| local execution of Python-based on-demand transforms | yes |
-| remote execution of Python-based on-demand transforms | no |
-| persist results in the offline store | yes |
-| preview the query plan before execution | yes |
-| read partitioned data | yes |
+| | Redshift |
+| ----------------------------------------------------- | -------- |
+| export to dataframe | yes |
+| export to arrow table | yes |
+| export to arrow batches | yes |
+| export to SQL | yes |
+| export to data lake (S3, GCS, etc.) | no |
+| export to data warehouse | yes |
+| export as Spark dataframe | no |
+| local execution of Python-based on-demand transforms | yes |
+| remote execution of Python-based on-demand transforms | no |
+| persist results in the offline store | yes |
+| preview the query plan before execution | yes |
+| read partitioned data | yes |
To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/offline-stores/snowflake.md b/docs/reference/offline-stores/snowflake.md
index e40ad7cd7a2..4ac7f164363 100644
--- a/docs/reference/offline-stores/snowflake.md
+++ b/docs/reference/offline-stores/snowflake.md
@@ -6,6 +6,13 @@ The [Snowflake](https://trial.snowflake.com) offline store provides support for
* All joins happen within Snowflake.
* Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to Snowflake as a temporary table in order to complete join operations.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[snowflake]'`.
+
+If you're using a file based registry, then you'll also need to install the relevant cloud extra (`pip install 'feast[snowflake, CLOUD]'` where `CLOUD` is one of `aws`, `gcp`, `azure`)
+
+You can get started by then running `feast init -t snowflake`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -31,29 +38,29 @@ The full set of configuration options is available in [SnowflakeOfflineStoreConf
The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Snowflake offline store.
-| | Snowflake |
-| :-------------------------------- | :-- |
-| `get_historical_features` (point-in-time correct join) | yes |
-| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
-| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
-| `offline_write_batch` (persist dataframes to offline store) | yes |
-| `write_logged_features` (persist logged features to offline store) | yes |
+| | Snowflake |
+| :----------------------------------------------------------------- | :-------- |
+| `get_historical_features` (point-in-time correct join) | yes |
+| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
+| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
+| `offline_write_batch` (persist dataframes to offline store) | yes |
+| `write_logged_features` (persist logged features to offline store) | yes |
Below is a matrix indicating which functionality is supported by `SnowflakeRetrievalJob`.
-| | Snowflake |
-| --------------------------------- | --- |
-| export to dataframe | yes |
-| export to arrow table | yes |
-| export to arrow batches | no |
-| export to SQL | yes |
-| export to data lake (S3, GCS, etc.) | yes |
-| export to data warehouse | yes |
-| export as Spark dataframe | no |
-| local execution of Python-based on-demand transforms | yes |
-| remote execution of Python-based on-demand transforms | no |
-| persist results in the offline store | yes |
-| preview the query plan before execution | yes |
-| read partitioned data | yes |
+| | Snowflake |
+| ----------------------------------------------------- | --------- |
+| export to dataframe | yes |
+| export to arrow table | yes |
+| export to arrow batches | no |
+| export to SQL | yes |
+| export to data lake (S3, GCS, etc.) | yes |
+| export to data warehouse | yes |
+| export as Spark dataframe | no |
+| local execution of Python-based on-demand transforms | yes |
+| remote execution of Python-based on-demand transforms | no |
+| persist results in the offline store | yes |
+| preview the query plan before execution | yes |
+| read partitioned data | yes |
To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/offline-stores/spark.md b/docs/reference/offline-stores/spark.md
index f1ef1300bd7..ae5ea78071e 100644
--- a/docs/reference/offline-stores/spark.md
+++ b/docs/reference/offline-stores/spark.md
@@ -11,6 +11,9 @@ The Spark offline store provides support for reading [SparkSources](../data-sour
The Spark offline store does not achieve full test coverage.
Please do not assume complete stability.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[spark]'`. You can get started by then running `feast init -t spark`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -39,29 +42,29 @@ The full set of configuration options is available in [SparkOfflineStoreConfig](
The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Spark offline store.
-| | Spark |
-| :-------------------------------- | :-- |
-| `get_historical_features` (point-in-time correct join) | yes |
-| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
-| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
-| `offline_write_batch` (persist dataframes to offline store) | no |
-| `write_logged_features` (persist logged features to offline store) | no |
+| | Spark |
+| :----------------------------------------------------------------- | :---- |
+| `get_historical_features` (point-in-time correct join) | yes |
+| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
+| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
+| `offline_write_batch` (persist dataframes to offline store) | no |
+| `write_logged_features` (persist logged features to offline store) | no |
Below is a matrix indicating which functionality is supported by `SparkRetrievalJob`.
-| | Spark |
-| --------------------------------- | --- |
-| export to dataframe | yes |
-| export to arrow table | yes |
-| export to arrow batches | no |
-| export to SQL | no |
-| export to data lake (S3, GCS, etc.) | no |
-| export to data warehouse | no |
-| export as Spark dataframe | yes |
-| local execution of Python-based on-demand transforms | no |
-| remote execution of Python-based on-demand transforms | no |
-| persist results in the offline store | yes |
-| preview the query plan before execution | yes |
-| read partitioned data | yes |
+| | Spark |
+| ----------------------------------------------------- | ----- |
+| export to dataframe | yes |
+| export to arrow table | yes |
+| export to arrow batches | no |
+| export to SQL | no |
+| export to data lake (S3, GCS, etc.) | no |
+| export to data warehouse | no |
+| export as Spark dataframe | yes |
+| local execution of Python-based on-demand transforms | no |
+| remote execution of Python-based on-demand transforms | no |
+| persist results in the offline store | yes |
+| preview the query plan before execution | yes |
+| read partitioned data | yes |
To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/offline-stores/trino.md b/docs/reference/offline-stores/trino.md
index 8cc604248ff..446db620e32 100644
--- a/docs/reference/offline-stores/trino.md
+++ b/docs/reference/offline-stores/trino.md
@@ -10,6 +10,9 @@ The Trino offline store provides support for reading [TrinoSources](../data-sour
The Trino offline store does not achieve full test coverage.
Please do not assume complete stability.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[trino]'`. You can then run `feast init`, then swap out `feature_store.yaml` with the below example to connect to Trino.
+
## Example
{% code title="feature_store.yaml" %}
@@ -36,29 +39,29 @@ The full set of configuration options is available in [TrinoOfflineStoreConfig](
The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Trino offline store.
-| | Trino |
-| :-------------------------------- | :-- |
-| `get_historical_features` (point-in-time correct join) | yes |
-| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
-| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
-| `offline_write_batch` (persist dataframes to offline store) | no |
-| `write_logged_features` (persist logged features to offline store) | no |
+| | Trino |
+| :----------------------------------------------------------------- | :---- |
+| `get_historical_features` (point-in-time correct join) | yes |
+| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
+| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
+| `offline_write_batch` (persist dataframes to offline store) | no |
+| `write_logged_features` (persist logged features to offline store) | no |
Below is a matrix indicating which functionality is supported by `TrinoRetrievalJob`.
-| | Trino |
-| --------------------------------- | --- |
-| export to dataframe | yes |
-| export to arrow table | yes |
-| export to arrow batches | no |
-| export to SQL | yes |
-| export to data lake (S3, GCS, etc.) | no |
-| export to data warehouse | no |
-| export as Spark dataframe | no |
-| local execution of Python-based on-demand transforms | yes |
-| remote execution of Python-based on-demand transforms | no |
-| persist results in the offline store | no |
-| preview the query plan before execution | yes |
-| read partitioned data | yes |
+| | Trino |
+| ----------------------------------------------------- | ----- |
+| export to dataframe | yes |
+| export to arrow table | yes |
+| export to arrow batches | no |
+| export to SQL | yes |
+| export to data lake (S3, GCS, etc.) | no |
+| export to data warehouse | no |
+| export as Spark dataframe | no |
+| local execution of Python-based on-demand transforms | yes |
+| remote execution of Python-based on-demand transforms | no |
+| persist results in the offline store | no |
+| preview the query plan before execution | yes |
+| read partitioned data | yes |
To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/cassandra.md b/docs/reference/online-stores/cassandra.md
index 48b7b73f439..e012ad250ae 100644
--- a/docs/reference/online-stores/cassandra.md
+++ b/docs/reference/online-stores/cassandra.md
@@ -8,7 +8,8 @@ The [Cassandra / Astra DB] online store provides support for materializing featu
* Each feature view is mapped one-to-one to a specific Cassandra table
* This implementation inherits all strengths of Cassandra such as high availability, fault-tolerance, and data distribution
-An easy way to get started is the command `feast init REPO_NAME -t cassandra`.
+## Getting started
+In order to use this online store, you'll need to run `pip install 'feast[cassandra]'`. You can then get started with the command `feast init REPO_NAME -t cassandra`.
### Example (Cassandra)
@@ -66,23 +67,23 @@ Storage specifications can be found at `docs/specs/online_store_format.md`.
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Cassandra online store.
-| | Cassandra |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | yes |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | no |
-| readable by Go | no |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | no |
-| support for ttl (time to live) at retrieval | no |
-| support for deleting expired data | no |
-| collocated by feature view | yes |
-| collocated by feature service | no |
-| collocated by entity key | no |
+| | Cassandra |
+| :-------------------------------------------------------- | :-------- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | yes |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | no |
+| readable by Go | no |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | no |
+| support for ttl (time to live) at retrieval | no |
+| support for deleting expired data | no |
+| collocated by feature view | yes |
+| collocated by feature service | no |
+| collocated by entity key | no |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/datastore.md b/docs/reference/online-stores/datastore.md
index 0867853f15d..761d246ba7e 100644
--- a/docs/reference/online-stores/datastore.md
+++ b/docs/reference/online-stores/datastore.md
@@ -4,6 +4,9 @@
The [Datastore](https://cloud.google.com/datastore) online store provides support for materializing feature values into Cloud Datastore. The data model used to store feature values in Datastore is described in more detail [here](../../specs/online_store_format.md#google-datastore-online-store-format).
+## Getting started
+In order to use this online store, you'll need to run `pip install 'feast[gcp]'`. You can then get started with the command `feast init REPO_NAME -t gcp`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -25,23 +28,23 @@ The full set of configuration options is available in [DatastoreOnlineStoreConfi
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Datastore online store.
-| | Datastore |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | no |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | no |
-| readable by Go | no |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | no |
-| support for ttl (time to live) at retrieval | no |
-| support for deleting expired data | no |
-| collocated by feature view | yes |
-| collocated by feature service | no |
-| collocated by entity key | no |
+| | Datastore |
+| :-------------------------------------------------------- | :-------- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | no |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | no |
+| readable by Go | no |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | no |
+| support for ttl (time to live) at retrieval | no |
+| support for deleting expired data | no |
+| collocated by feature view | yes |
+| collocated by feature service | no |
+| collocated by entity key | no |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/dynamodb.md b/docs/reference/online-stores/dynamodb.md
index 2f94c768199..344caccac1d 100644
--- a/docs/reference/online-stores/dynamodb.md
+++ b/docs/reference/online-stores/dynamodb.md
@@ -4,6 +4,9 @@
The [DynamoDB](https://aws.amazon.com/dynamodb/) online store provides support for materializing feature values into AWS DynamoDB.
+## Getting started
+In order to use this online store, you'll need to run `pip install 'feast[aws]'`. You can then get started with the command `feast init REPO_NAME -t aws`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -27,7 +30,7 @@ Feast requires the following permissions in order to execute commands for Dynamo
| ----------------------- | ----------------------------------------------------------------------------------- | ------------------------------------------------- |
| **Apply** | dynamodb:CreateTable
dynamodb:DescribeTable
dynamodb:DeleteTable
| arn:aws:dynamodb:\:\:table/\* |
| **Materialize** | dynamodb.BatchWriteItem | arn:aws:dynamodb:\:\:table/\* |
-| **Get Online Features** | dynamodb.BatchGetItem | arn:aws:dynamodb:\:\:table/\* |
+| **Get Online Features** | dynamodb.BatchGetItem | arn:aws:dynamodb:\:\:table/\* |
The following inline policy can be used to grant Feast the necessary permissions:
@@ -59,23 +62,23 @@ Lastly, this IAM role needs to be associated with the desired Redshift cluster.
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the DynamoDB online store.
-| | DynamoDB |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | no |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | no |
-| readable by Go | no |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | no |
-| support for ttl (time to live) at retrieval | no |
-| support for deleting expired data | no |
-| collocated by feature view | yes |
-| collocated by feature service | no |
-| collocated by entity key | no |
+| | DynamoDB |
+| :-------------------------------------------------------- | :------- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | no |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | no |
+| readable by Go | no |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | no |
+| support for ttl (time to live) at retrieval | no |
+| support for deleting expired data | no |
+| collocated by feature view | yes |
+| collocated by feature service | no |
+| collocated by entity key | no |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/mysql.md b/docs/reference/online-stores/mysql.md
index cb90c7561c5..cbc48457e13 100644
--- a/docs/reference/online-stores/mysql.md
+++ b/docs/reference/online-stores/mysql.md
@@ -6,6 +6,8 @@ The MySQL online store provides support for materializing feature values into a
* Only the latest feature values are persisted
+## Getting started
+In order to use this online store, you'll need to run `pip install 'feast[mysql]'`. You can get started by then running `feast init` and then setting the `feature_store.yaml` as described below.
## Example
@@ -31,23 +33,23 @@ The full set of configuration options is available in [MySQLOnlineStoreConfig](h
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Mys online store.
-| | Mys |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | no |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | no |
-| readable by Go | no |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | no |
-| support for ttl (time to live) at retrieval | no |
-| support for deleting expired data | no |
-| collocated by feature view | yes |
-| collocated by feature service | no |
-| collocated by entity key | no |
+| | Mys |
+| :-------------------------------------------------------- | :--- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | no |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | no |
+| readable by Go | no |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | no |
+| support for ttl (time to live) at retrieval | no |
+| support for deleting expired data | no |
+| collocated by feature view | yes |
+| collocated by feature service | no |
+| collocated by entity key | no |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/postgres.md b/docs/reference/online-stores/postgres.md
index 083c0006359..3885867dd26 100644
--- a/docs/reference/online-stores/postgres.md
+++ b/docs/reference/online-stores/postgres.md
@@ -8,6 +8,9 @@ The PostgreSQL online store provides support for materializing feature values in
* sslmode, sslkey_path, sslcert_path, and sslrootcert_path are optional
+## Getting started
+In order to use this online store, you'll need to run `pip install 'feast[postgres]'`. You can get started by then running `feast init -t postgres`.
+
## Example
{% code title="feature_store.yaml" %}
@@ -37,23 +40,23 @@ The full set of configuration options is available in [PostgreSQLOnlineStoreConf
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Postgres online store.
-| | Postgres |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | no |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | no |
-| readable by Go | no |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | no |
-| support for ttl (time to live) at retrieval | no |
-| support for deleting expired data | no |
-| collocated by feature view | yes |
-| collocated by feature service | no |
-| collocated by entity key | no |
+| | Postgres |
+| :-------------------------------------------------------- | :------- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | no |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | no |
+| readable by Go | no |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | no |
+| support for ttl (time to live) at retrieval | no |
+| support for deleting expired data | no |
+| collocated by feature view | yes |
+| collocated by feature service | no |
+| collocated by entity key | no |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/redis.md b/docs/reference/online-stores/redis.md
index 80e90348c55..2078ee16b95 100644
--- a/docs/reference/online-stores/redis.md
+++ b/docs/reference/online-stores/redis.md
@@ -7,6 +7,15 @@ The [Redis](https://redis.io) online store provides support for materializing fe
* 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).
+## Getting started
+In order to use this online store, you'll need to install the redis extra (along with the dependency needed for the offline store of choice). E.g.
+- `pip install 'feast[gcp, redis]'`
+- `pip install 'feast[snowflake, redis]'`
+- `pip install 'feast[aws, redis]'`
+- `pip install 'feast[azure, redis]'`
+
+You can get started by using any of the other templates (e.g. `feast init -t gcp` or `feast init -t snowflake` or `feast init -t aws`), and then swapping in Redis as the online store as seen below in the examples.
+
## Examples
Connecting to a single Redis instance:
@@ -43,23 +52,23 @@ The full set of configuration options is available in [RedisOnlineStoreConfig](h
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Redis online store.
-| | Redis |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | no |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | yes |
-| readable by Go | yes |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | yes |
-| support for ttl (time to live) at retrieval | yes |
-| support for deleting expired data | yes |
-| collocated by feature view | no |
-| collocated by feature service | no |
-| collocated by entity key | yes |
+| | Redis |
+| :-------------------------------------------------------- | :---- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | no |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | yes |
+| readable by Go | yes |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | yes |
+| support for ttl (time to live) at retrieval | yes |
+| support for deleting expired data | yes |
+| collocated by feature view | no |
+| collocated by feature service | no |
+| collocated by entity key | yes |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/online-stores/snowflake.md b/docs/reference/online-stores/snowflake.md
index d114c87144a..6b6d107285c 100644
--- a/docs/reference/online-stores/snowflake.md
+++ b/docs/reference/online-stores/snowflake.md
@@ -16,6 +16,9 @@ The data model for using a Snowflake Transient Table as an online store follows
(This model may be subject to change when Snowflake Hybrid Tables are released)
+## Getting started
+In order to use this online store, you'll need to run `pip install 'feast[snowflake]'`. You can then get started with the command `feast init REPO_NAME -t snowflake`.
+
## Example
{% code title="feature_store.yaml" %}
```yaml
@@ -53,23 +56,23 @@ The full set of configuration options is available in [SnowflakeOnlineStoreConfi
The set of functionality supported by online stores is described in detail [here](overview.md#functionality).
Below is a matrix indicating which functionality is supported by the Snowflake online store.
-| | Snowflake |
-| :-------------------------------------------------------- | :-- |
-| write feature values to the online store | yes |
-| read feature values from the online store | yes |
-| update infrastructure (e.g. tables) in the online store | yes |
-| teardown infrastructure (e.g. tables) in the online store | yes |
-| generate a plan of infrastructure changes | no |
-| support for on-demand transforms | yes |
-| readable by Python SDK | yes |
-| readable by Java | no |
-| readable by Go | no |
-| support for entityless feature views | yes |
-| support for concurrent writing to the same key | no |
-| support for ttl (time to live) at retrieval | no |
-| support for deleting expired data | no |
-| collocated by feature view | yes |
-| collocated by feature service | no |
-| collocated by entity key | no |
+| | Snowflake |
+| :-------------------------------------------------------- | :-------- |
+| write feature values to the online store | yes |
+| read feature values from the online store | yes |
+| update infrastructure (e.g. tables) in the online store | yes |
+| teardown infrastructure (e.g. tables) in the online store | yes |
+| generate a plan of infrastructure changes | no |
+| support for on-demand transforms | yes |
+| readable by Python SDK | yes |
+| readable by Java | no |
+| readable by Go | no |
+| support for entityless feature views | yes |
+| support for concurrent writing to the same key | no |
+| support for ttl (time to live) at retrieval | no |
+| support for deleting expired data | no |
+| collocated by feature view | yes |
+| collocated by feature service | no |
+| collocated by entity key | no |
To compare this set of functionality against other online stores, please see the full [functionality matrix](overview.md#functionality-matrix).
diff --git a/docs/reference/providers/amazon-web-services.md b/docs/reference/providers/amazon-web-services.md
index 3135fedb740..68956a1be93 100644
--- a/docs/reference/providers/amazon-web-services.md
+++ b/docs/reference/providers/amazon-web-services.md
@@ -5,6 +5,11 @@
* Offline Store: Uses the **Redshift** offline store by default. Also supports File as the offline store.
* Online Store: Uses the **DynamoDB** online store by default. Also supports Sqlite as an online store.
+## Getting started
+In order to use this offline store, you'll need to run (Snowflake) `pip install 'feast[aws, snowflake]'` or (Redshift) `pip install 'feast[aws]'`.
+
+You can get started by then running `feast init -t snowflake` or `feast init -t aws`.
+
## Example
{% code title="feature_store.yaml" %}
diff --git a/docs/reference/providers/azure.md b/docs/reference/providers/azure.md
index 123bf087635..0e7206f076e 100644
--- a/docs/reference/providers/azure.md
+++ b/docs/reference/providers/azure.md
@@ -10,6 +10,9 @@
The Azure provider does not achieve full test coverage.
Please do not assume complete stability.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[azure]'`. You can get started by then following this [tutorial](https://github.com/feast-dev/feast/blob/master/docs/tutorials/azure/README.md).
+
## Example
{% code title="feature_store.yaml" %}
diff --git a/docs/reference/providers/google-cloud-platform.md b/docs/reference/providers/google-cloud-platform.md
index 713313d16be..96af3b6b2ff 100644
--- a/docs/reference/providers/google-cloud-platform.md
+++ b/docs/reference/providers/google-cloud-platform.md
@@ -5,6 +5,9 @@
* Offline Store: Uses the **BigQuery** offline store by default. Also supports File as the offline store.
* Online Store: Uses the **Datastore** online store by default. Also supports Sqlite as an online store.
+## Getting started
+In order to use this offline store, you'll need to run `pip install 'feast[gcp]'`. You can get started by then running `feast init -t gcp`.
+
## Example
{% code title="feature_store.yaml" %}