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docs: Add references to the extras users need to install and quickstarts (#3249)
docs: Add references to the extras users need to install and quickstart templates Signed-off-by: Danny Chiao <[email protected]> Signed-off-by: Danny Chiao <[email protected]>
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‎docs/reference/offline-stores/bigquery.md‎

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@@ -7,6 +7,9 @@ The BigQuery offline store provides support for reading [BigQuerySources](../dat
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* All joins happen within BigQuery.
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* 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.
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## Getting started
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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`.
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## Example
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{% code title="feature_store.yaml" %}

‎docs/reference/offline-stores/mssql.md‎

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@@ -6,6 +6,9 @@ The MsSQL offline store provides support for reading [MsSQL Sources](../data-sou
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* Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.
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## Getting started
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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).
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## Disclaimer
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The MsSQL offline store does not achieve full test coverage.
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The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
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Below is a matrix indicating which functionality is supported by the Spark offline store.
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| | MsSql |
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| :-------------------------------- | :-- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | no |
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| `write_logged_features` (persist logged features to offline store) | no |
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| | MsSql |
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| :----------------------------------------------------------------- | :---- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | no |
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| `write_logged_features` (persist logged features to offline store) | no |
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Below is a matrix indicating which functionality is supported by `MsSqlServerRetrievalJob`.
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| | MsSql |
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| --------------------------------- | --- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | no |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | no |
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| local execution of Python-based on-demand transforms | no |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| | MsSql |
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| ----------------------------------------------------- | ----- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | no |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | no |
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| local execution of Python-based on-demand transforms | no |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).

‎docs/reference/offline-stores/postgres.md‎

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@@ -10,6 +10,9 @@ The PostgreSQL offline store provides support for reading [PostgreSQLSources](..
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The PostgreSQL offline store does not achieve full test coverage.
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Please do not assume complete stability.
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## Getting started
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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`.
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## Example
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{% code title="feature_store.yaml" %}
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The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
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Below is a matrix indicating which functionality is supported by the PostgreSQL offline store.
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| | Postgres |
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| :-------------------------------- | :-- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | no |
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| `write_logged_features` (persist logged features to offline store) | no |
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| | Postgres |
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| :----------------------------------------------------------------- | :------- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | no |
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| `write_logged_features` (persist logged features to offline store) | no |
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Below is a matrix indicating which functionality is supported by `PostgreSQLRetrievalJob`.
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| | Postgres |
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| --------------------------------- | --- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | yes |
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| export to data lake (S3, GCS, etc.) | yes |
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| export to data warehouse | yes |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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| | Postgres |
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| ----------------------------------------------------- | -------- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | yes |
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| export to data lake (S3, GCS, etc.) | yes |
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| export to data warehouse | yes |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).

‎docs/reference/offline-stores/redshift.md‎

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@@ -7,6 +7,9 @@ The Redshift offline store provides support for reading [RedshiftSources](../dat
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* All joins happen within Redshift.
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* 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.
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## Getting started
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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`.
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## Example
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{% code title="feature_store.yaml" %}
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The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
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Below is a matrix indicating which functionality is supported by the Redshift offline store.
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| | Redshift |
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| :-------------------------------- | :-- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | yes |
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| `write_logged_features` (persist logged features to offline store) | yes |
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| | Redshift |
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| :----------------------------------------------------------------- | :------- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | yes |
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| `write_logged_features` (persist logged features to offline store) | yes |
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Below is a matrix indicating which functionality is supported by `RedshiftRetrievalJob`.
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| | Redshift |
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| --------------------------------- | --- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | yes |
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| export to SQL | yes |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | yes |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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| | Redshift |
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| ----------------------------------------------------- | -------- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | yes |
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| export to SQL | yes |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | yes |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
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‎docs/reference/offline-stores/snowflake.md‎

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@@ -6,6 +6,13 @@ The [Snowflake](https://trial.snowflake.com) offline store provides support for
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* All joins happen within Snowflake.
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* 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.
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## Getting started
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In order to use this offline store, you'll need to run `pip install 'feast[snowflake]'`.
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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`)
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You can get started by then running `feast init -t snowflake`.
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## Example
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{% code title="feature_store.yaml" %}
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The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
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Below is a matrix indicating which functionality is supported by the Snowflake offline store.
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| | Snowflake |
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| :-------------------------------- | :-- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | yes |
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| `write_logged_features` (persist logged features to offline store) | yes |
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| | Snowflake |
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| :----------------------------------------------------------------- | :-------- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | yes |
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| `write_logged_features` (persist logged features to offline store) | yes |
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Below is a matrix indicating which functionality is supported by `SnowflakeRetrievalJob`.
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| | Snowflake |
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| --------------------------------- | --- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | yes |
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| export to data lake (S3, GCS, etc.) | yes |
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| export to data warehouse | yes |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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| | Snowflake |
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| ----------------------------------------------------- | --------- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | yes |
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| export to data lake (S3, GCS, etc.) | yes |
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| export to data warehouse | yes |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).

‎docs/reference/offline-stores/spark.md‎

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The Spark offline store does not achieve full test coverage.
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Please do not assume complete stability.
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## Getting started
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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`.
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## Example
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{% code title="feature_store.yaml" %}
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The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
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Below is a matrix indicating which functionality is supported by the Spark offline store.
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| | Spark |
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| :-------------------------------- | :-- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | no |
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| `write_logged_features` (persist logged features to offline store) | no |
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| | Spark |
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| :----------------------------------------------------------------- | :---- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | no |
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| `write_logged_features` (persist logged features to offline store) | no |
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Below is a matrix indicating which functionality is supported by `SparkRetrievalJob`.
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| | Spark |
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| --------------------------------- | --- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | no |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | no |
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| export as Spark dataframe | yes |
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| local execution of Python-based on-demand transforms | no |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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| | Spark |
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| ----------------------------------------------------- | ----- |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | no |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | no |
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| export as Spark dataframe | yes |
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| local execution of Python-based on-demand transforms | no |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | yes |
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| read partitioned data | yes |
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To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).

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