[FSTORE-1795] Update api to use data source and deprecate direct storage connector usage

docs/user_guides/fs/data_source/usage.md

@@ -22,7 +22,7 @@ We retrieve a data source simply by its unique name.
     project = hopsworks.login()
     feature_store = project.get_feature_store()
     # Retrieve data source
-    connector = feature_store.get_storage_connector('data_source_name')
+    ds = feature_store.get_data_source('data_source_name')
     ```
 
 === "Scala"
@@ -119,17 +119,20 @@ Another important aspect of a data source is its ability to facilitate creation
 The `Connector API` relies on data sources behind the scenes to integrate with external datasource.
 This enables seamless integration with any data source as long as there is a data source defined.
 
-To create an external feature group, we use the `create_external_feature_group` API, also known as `Connector API`, and simply pass the data source created before to the `storage_connector` argument.
+
+To create an external feature group, we use the `create_external_feature_group` API, also known as `Connector API`, and simply pass the data source created before to the `data_source` argument.
 Depending on the external source, we should set either the `query` argument for data warehouse based sources, or the `path` and `data_format` arguments for data lake based sources, similar to reading into dataframes as explained in above section.
 
-Example for any data warehouse/SQL based external sources, we set the desired SQL to `query` argument, and set the `storage_connector` argument to the data source object of desired data source.
+Example for any data warehouse/SQL based external sources, we set the desired SQL to `query` argument, and set the `data_source` argument to the data source object of desired data source.
+
 === "PySpark"
     ```python
+    ds.query="SELECT * FROM TABLE"
+
     fg = feature_store.create_external_feature_group(name="sales",
         version=1
         description="Physical shop sales features",
-        query="SELECT * FROM TABLE",
-        storage_connector=connector,
+        data_source = ds,
         primary_key=['ss_store_sk'],
         event_time='sale_date'
     )
@@ -141,8 +144,8 @@ For more information on `Connector API`, read detailed guide about [external fea
 
 ## Writing Training Data
 
-Data Sources are also used while writing training data to external sources.
-While calling the [Feature View](../../../concepts/fs/feature_view/fv_overview.md) API `create_training_data` , we can pass the `storage_connector` argument which is necessary to materialise the data to external sources, as shown below.
+Data Sources are also used while writing training data to external sources. While calling the
+While calling the [Feature View](../../../concepts/fs/feature_view/fv_overview.md) API `create_training_data` , we can pass the `data_source` argument which is necessary to materialise the data to external sources, as shown below.
 
 === "PySpark"
     ```python
@@ -151,7 +154,7 @@ While calling the [Feature View](../../../concepts/fs/feature_view/fv_overview.m
         description = 'describe training data',
         data_format = 'spark_data_format', # e.g., data_format = "parquet" or data_format = "csv"
         write_options = {"wait_for_job": False},
-        storage_connector = connector
+        data_source = ds
     )
     ```
 

docs/user_guides/fs/feature_group/create.md

@@ -108,7 +108,7 @@ The currently support values are "HUDI", "DELTA", "NONE" (which defaults to Parq
 
 ##### Data Source
 
-During the creation of a feature group, it is possible to define the `storage_connector` parameter, this allows for management of offline data in the desired table format outside the Hopsworks cluster.
+During the creation of a feature group, it is possible to define the `data_source` parameter, this allows for management of offline data in the desired table format outside the Hopsworks cluster.
 Currently, [S3](../data_source/creation/s3.md) and [GCS](../data_source/creation/gcs.md) connectors and "DELTA" `time_travel_format` format is supported.
 
 ##### Online Table Configuration

docs/user_guides/fs/feature_group/create_external.md

@@ -22,7 +22,7 @@ To create an external feature group using the HSFS APIs you need to provide an e
 === "Python"
 
     ```python
-    connector = feature_store.get_storage_connector("data_source_name")
+    ds = feature_store.get_data_source("data_source_name")
     ```
 
 ### Create an External Feature Group
@@ -52,7 +52,7 @@ Once you have defined the metadata, you can
         version=1,
         description="Physical shop sales features",
         query=query,
-        storage_connector=connector,
+        data_source=ds,
         primary_key=['ss_store_sk'],
         event_time='sale_date'
     )
@@ -69,7 +69,7 @@ Once you have defined the metadata, you can
         version=1,
         description="Physical shop sales features",
         data_format="parquet",
-        storage_connector=connector,
+        data_source=ds,
         primary_key=['ss_store_sk'],
         event_time='sale_date'
     )
@@ -112,7 +112,7 @@ For an external feature group to be available online, during the creation of the
                 version=1,
                 description="Physical shop sales features",
                 query=query,
-                storage_connector=connector,
+                data_source=ds,
                 primary_key=['ss_store_sk'],
                 event_time='sale_date',
                 online_enabled=True)

docs/user_guides/fs/provenance/provenance.md

@@ -35,28 +35,28 @@ You can inspect the relationship between data sources and feature groups using t
 
     ```python
     # Retrieve the data source
-    snowflake_sc = fs.get_storage_connector("snowflake_sc")
+    ds = fs.get_data_source("snowflake_sc")
+    ds.query = "SELECT * FROM USER_PROFILES"
 
     # Create the user profiles feature group
     user_profiles_fg = fs.create_external_feature_group(
         name="user_profiles",
         version=1,
-        storage_connector=snowflake_sc,
-        query="SELECT * FROM USER_PROFILES"
+        data_source=ds
     )
     user_profiles_fg.save()
     ```
 
 ### Step 1, Using Python
 
 Starting from a feature group metadata object, you can traverse upstream the provenance graph to retrieve the metadata objects of the data sources that are part of the feature group.
-To do so, you can use the [`FeatureGroup.get_storage_connector_provenance`][hsfs.feature_group.FeatureGroup.get_storage_connector_provenance] method.
+To do so, you can use the [`FeatureGroup.get_data_source_provenance`][hsfs.feature_group.FeatureGroup.get_data_source_provenance] method.
 
 === "Python"
 
     ```python
     # Returns all data sources linked to the provided feature group
-    lineage = user_profiles_fg.get_storage_connector_provenance()
+    lineage = user_profiles_fg.get_data_source_provenance()
 
     # List all accessible parent data sources
     lineage.accessible
@@ -72,7 +72,7 @@ To do so, you can use the [`FeatureGroup.get_storage_connector_provenance`][hsfs
 
     ```python
     # Returns an accessible data source linked to the feature group (if it exists)
-    user_profiles_fg.get_storage_connector()
+    user_profiles_fg.get_data_source()
     ```
 
 To traverse the provenance graph in the opposite direction (i.e., from the data source to the feature group), you can use the [`StorageConnector.get_feature_groups_provenance`][hsfs.storage_connector.StorageConnector.get_feature_groups_provenance] method.