diff --git a/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres.py b/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres.py
deleted file mode 100644
index 9a3df40c03..0000000000
--- a/sdk/python/feast/infra/offline_stores/contrib/postgres_offline_store/postgres.py
+++ /dev/null
@@ -1,541 +0,0 @@
-import contextlib
-from dataclasses import asdict
-from datetime import datetime
-from typing import (
-    Any,
-    Callable,
-    ContextManager,
-    Iterator,
-    KeysView,
-    List,
-    Optional,
-    Tuple,
-    Union,
-)
-
-import pandas as pd
-import pyarrow as pa
-from jinja2 import BaseLoader, Environment
-from psycopg2 import sql
-from pydantic.typing import Literal
-from pytz import utc
-
-from feast.data_source import DataSource
-from feast.errors import InvalidEntityType
-from feast.feature_view import DUMMY_ENTITY_ID, DUMMY_ENTITY_VAL, FeatureView
-from feast.infra.offline_stores import offline_utils
-from feast.infra.offline_stores.offline_store import (
-    OfflineStore,
-    RetrievalJob,
-    RetrievalMetadata,
-)
-from feast.infra.utils.postgres.connection_utils import (
-    _get_conn,
-    df_to_postgres_table,
-    get_query_schema,
-)
-from feast.infra.utils.postgres.postgres_config import PostgreSQLConfig
-from feast.on_demand_feature_view import OnDemandFeatureView
-from feast.registry import Registry
-from feast.repo_config import RepoConfig
-from feast.saved_dataset import SavedDatasetStorage
-from feast.type_map import pg_type_code_to_arrow
-
-from .postgres_source import PostgreSQLSource
-
-
-class PostgreSQLOfflineStoreConfig(PostgreSQLConfig):
-    type: Literal["postgres"] = "postgres"
-
-
-class PostgreSQLOfflineStore(OfflineStore):
-    @staticmethod
-    def pull_latest_from_table_or_query(
-        config: RepoConfig,
-        data_source: DataSource,
-        join_key_columns: List[str],
-        feature_name_columns: List[str],
-        timestamp_field: str,
-        created_timestamp_column: Optional[str],
-        start_date: datetime,
-        end_date: datetime,
-    ) -> RetrievalJob:
-        assert isinstance(data_source, PostgreSQLSource)
-        from_expression = data_source.get_table_query_string()
-
-        partition_by_join_key_string = ", ".join(_append_alias(join_key_columns, "a"))
-        if partition_by_join_key_string != "":
-            partition_by_join_key_string = (
-                "PARTITION BY " + partition_by_join_key_string
-            )
-        timestamps = [timestamp_field]
-        if created_timestamp_column:
-            timestamps.append(created_timestamp_column)
-        timestamp_desc_string = " DESC, ".join(_append_alias(timestamps, "a")) + " DESC"
-        a_field_string = ", ".join(
-            _append_alias(join_key_columns + feature_name_columns + timestamps, "a")
-        )
-        b_field_string = ", ".join(
-            _append_alias(join_key_columns + feature_name_columns + timestamps, "b")
-        )
-
-        query = f"""
-            SELECT
-                {b_field_string}
-                {f", {repr(DUMMY_ENTITY_VAL)} AS {DUMMY_ENTITY_ID}" if not join_key_columns else ""}
-            FROM (
-                SELECT {a_field_string},
-                ROW_NUMBER() OVER({partition_by_join_key_string} ORDER BY {timestamp_desc_string}) AS _feast_row
-                FROM ({from_expression}) a
-                WHERE a."{timestamp_field}" BETWEEN '{start_date}'::timestamptz AND '{end_date}'::timestamptz
-            ) b
-            WHERE _feast_row = 1
-            """
-
-        return PostgreSQLRetrievalJob(
-            query=query,
-            config=config,
-            full_feature_names=False,
-            on_demand_feature_views=None,
-        )
-
-    @staticmethod
-    def get_historical_features(
-        config: RepoConfig,
-        feature_views: List[FeatureView],
-        feature_refs: List[str],
-        entity_df: Union[pd.DataFrame, str],
-        registry: Registry,
-        project: str,
-        full_feature_names: bool = False,
-    ) -> RetrievalJob:
-        @contextlib.contextmanager
-        def query_generator() -> Iterator[str]:
-            table_name = None
-            if isinstance(entity_df, pd.DataFrame):
-                table_name = offline_utils.get_temp_entity_table_name()
-                entity_schema = df_to_postgres_table(
-                    config.offline_store, entity_df, table_name
-                )
-                df_query = table_name
-            elif isinstance(entity_df, str):
-                df_query = f"({entity_df}) AS sub"
-                entity_schema = get_query_schema(config.offline_store, df_query)
-            else:
-                raise TypeError(entity_df)
-
-            entity_df_event_timestamp_col = offline_utils.infer_event_timestamp_from_entity_df(
-                entity_schema
-            )
-
-            expected_join_keys = offline_utils.get_expected_join_keys(
-                project, feature_views, registry
-            )
-
-            offline_utils.assert_expected_columns_in_entity_df(
-                entity_schema, expected_join_keys, entity_df_event_timestamp_col
-            )
-
-            entity_df_event_timestamp_range = _get_entity_df_event_timestamp_range(
-                entity_df, entity_df_event_timestamp_col, config, df_query,
-            )
-
-            query_context = offline_utils.get_feature_view_query_context(
-                feature_refs,
-                feature_views,
-                registry,
-                project,
-                entity_df_event_timestamp_range,
-            )
-
-            query_context_dict = [asdict(context) for context in query_context]
-            # Hack for query_context.entity_selections to support uppercase in columns
-            for context in query_context_dict:
-                context["entity_selections"] = [
-                    f'''"{entity_selection.replace(' AS ', '" AS "')}\"'''
-                    for entity_selection in context["entity_selections"]
-                ]
-
-            try:
-                yield build_point_in_time_query(
-                    query_context_dict,
-                    left_table_query_string=df_query,
-                    entity_df_event_timestamp_col=entity_df_event_timestamp_col,
-                    entity_df_columns=entity_schema.keys(),
-                    query_template=MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN,
-                    full_feature_names=full_feature_names,
-                )
-            finally:
-                if table_name:
-                    with _get_conn(config.offline_store) as conn, conn.cursor() as cur:
-                        cur.execute(
-                            sql.SQL(
-                                """
-                                DROP TABLE IF EXISTS {};
-                                """
-                            ).format(sql.Identifier(table_name)),
-                        )
-
-        return PostgreSQLRetrievalJob(
-            query=query_generator,
-            config=config,
-            full_feature_names=full_feature_names,
-            on_demand_feature_views=OnDemandFeatureView.get_requested_odfvs(
-                feature_refs, project, registry
-            ),
-        )
-
-    @staticmethod
-    def pull_all_from_table_or_query(
-        config: RepoConfig,
-        data_source: DataSource,
-        join_key_columns: List[str],
-        feature_name_columns: List[str],
-        timestamp_field: str,
-        start_date: datetime,
-        end_date: datetime,
-    ) -> RetrievalJob:
-        assert isinstance(data_source, PostgreSQLSource)
-        from_expression = data_source.get_table_query_string()
-
-        field_string = ", ".join(
-            join_key_columns + feature_name_columns + [timestamp_field]
-        )
-
-        start_date = start_date.astimezone(tz=utc)
-        end_date = end_date.astimezone(tz=utc)
-
-        query = f"""
-            SELECT {field_string}
-            FROM {from_expression}
-            WHERE "{timestamp_field}" BETWEEN '{start_date}'::timestamptz AND '{end_date}'::timestamptz
-        """
-
-        return PostgreSQLRetrievalJob(
-            query=query,
-            config=config,
-            full_feature_names=False,
-            on_demand_feature_views=None,
-        )
-
-
-class PostgreSQLRetrievalJob(RetrievalJob):
-    def __init__(
-        self,
-        query: Union[str, Callable[[], ContextManager[str]]],
-        config: RepoConfig,
-        full_feature_names: bool,
-        on_demand_feature_views: Optional[List[OnDemandFeatureView]],
-        metadata: Optional[RetrievalMetadata] = None,
-    ):
-        if not isinstance(query, str):
-            self._query_generator = query
-        else:
-
-            @contextlib.contextmanager
-            def query_generator() -> Iterator[str]:
-                assert isinstance(query, str)
-                yield query
-
-            self._query_generator = query_generator
-        self.config = config
-        self._full_feature_names = full_feature_names
-        self._on_demand_feature_views = on_demand_feature_views
-        self._metadata = metadata
-
-    @property
-    def full_feature_names(self) -> bool:
-        return self._full_feature_names
-
-    @property
-    def on_demand_feature_views(self) -> Optional[List[OnDemandFeatureView]]:
-        return self._on_demand_feature_views
-
-    def _to_df_internal(self) -> pd.DataFrame:
-        # We use arrow format because it gives better control of the table schema
-        return self._to_arrow_internal().to_pandas()
-
-    def to_sql(self) -> str:
-        with self._query_generator() as query:
-            return query
-
-    def _to_arrow_internal(self) -> pa.Table:
-        with self._query_generator() as query:
-            with _get_conn(self.config.offline_store) as conn, conn.cursor() as cur:
-                conn.set_session(readonly=True)
-                cur.execute(query)
-                fields = [
-                    (c.name, pg_type_code_to_arrow(c.type_code))
-                    for c in cur.description
-                ]
-                data = cur.fetchall()
-                schema = pa.schema(fields)
-                # TODO: Fix...
-                data_transposed: List[List[Any]] = []
-                for col in range(len(fields)):
-                    data_transposed.append([])
-                    for row in range(len(data)):
-                        data_transposed[col].append(data[row][col])
-
-                table = pa.Table.from_arrays(
-                    [pa.array(row) for row in data_transposed], schema=schema
-                )
-                return table
-
-    @property
-    def metadata(self) -> Optional[RetrievalMetadata]:
-        return self._metadata
-
-    def persist(self, storage: SavedDatasetStorage):
-        pass
-
-
-def _get_entity_df_event_timestamp_range(
-    entity_df: Union[pd.DataFrame, str],
-    entity_df_event_timestamp_col: str,
-    config: RepoConfig,
-    table_name: str,
-) -> Tuple[datetime, datetime]:
-    if isinstance(entity_df, pd.DataFrame):
-        entity_df_event_timestamp = entity_df.loc[
-            :, entity_df_event_timestamp_col
-        ].infer_objects()
-        if pd.api.types.is_string_dtype(entity_df_event_timestamp):
-            entity_df_event_timestamp = pd.to_datetime(
-                entity_df_event_timestamp, utc=True
-            )
-        entity_df_event_timestamp_range = (
-            entity_df_event_timestamp.min(),
-            entity_df_event_timestamp.max(),
-        )
-    elif isinstance(entity_df, str):
-        # If the entity_df is a string (SQL query), determine range
-        # from table
-        with _get_conn(config.offline_store) as conn, conn.cursor() as cur:
-            cur.execute(
-                f"SELECT MIN({entity_df_event_timestamp_col}) AS min, MAX({entity_df_event_timestamp_col}) AS max FROM {table_name}"
-            ),
-            res = cur.fetchone()
-        entity_df_event_timestamp_range = (res[0], res[1])
-    else:
-        raise InvalidEntityType(type(entity_df))
-
-    return entity_df_event_timestamp_range
-
-
-def _append_alias(field_names: List[str], alias: str) -> List[str]:
-    return [f'{alias}."{field_name}"' for field_name in field_names]
-
-
-def build_point_in_time_query(
-    feature_view_query_contexts: List[dict],
-    left_table_query_string: str,
-    entity_df_event_timestamp_col: str,
-    entity_df_columns: KeysView[str],
-    query_template: str,
-    full_feature_names: bool = False,
-) -> str:
-    """Build point-in-time query between each feature view table and the entity dataframe for PostgreSQL"""
-    template = Environment(loader=BaseLoader()).from_string(source=query_template)
-
-    final_output_feature_names = list(entity_df_columns)
-    final_output_feature_names.extend(
-        [
-            (
-                f'{fv["name"]}__{fv["field_mapping"].get(feature, feature)}'
-                if full_feature_names
-                else fv["field_mapping"].get(feature, feature)
-            )
-            for fv in feature_view_query_contexts
-            for feature in fv["features"]
-        ]
-    )
-
-    # Add additional fields to dict
-    template_context = {
-        "left_table_query_string": left_table_query_string,
-        "entity_df_event_timestamp_col": entity_df_event_timestamp_col,
-        "unique_entity_keys": set(
-            [entity for fv in feature_view_query_contexts for entity in fv["entities"]]
-        ),
-        "featureviews": feature_view_query_contexts,
-        "full_feature_names": full_feature_names,
-        "final_output_feature_names": final_output_feature_names,
-    }
-
-    query = template.render(template_context)
-    return query
-
-
-# Copied from the Feast Redshift offline store implementation
-# Note: Keep this in sync with sdk/python/feast/infra/offline_stores/redshift.py:
-# MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN
-# https://github.com/feast-dev/feast/blob/master/sdk/python/feast/infra/offline_stores/redshift.py
-
-MULTIPLE_FEATURE_VIEW_POINT_IN_TIME_JOIN = """
-/*
- Compute a deterministic hash for the `left_table_query_string` that will be used throughout
- all the logic as the field to GROUP BY the data
-*/
-WITH entity_dataframe AS (
-    SELECT *,
-        {{entity_df_event_timestamp_col}} AS entity_timestamp
-        {% for featureview in featureviews %}
-            {% if featureview.entities %}
-            ,(
-                {% for entity in featureview.entities %}
-                    CAST("{{entity}}" as VARCHAR) ||
-                {% endfor %}
-                CAST("{{entity_df_event_timestamp_col}}" AS VARCHAR)
-            ) AS "{{featureview.name}}__entity_row_unique_id"
-            {% else %}
-            ,CAST("{{entity_df_event_timestamp_col}}" AS VARCHAR) AS "{{featureview.name}}__entity_row_unique_id"
-            {% endif %}
-        {% endfor %}
-    FROM {{ left_table_query_string }}
-),
-
-{% for featureview in featureviews %}
-
-"{{ featureview.name }}__entity_dataframe" AS (
-    SELECT
-        {% if featureview.entities %}"{{ featureview.entities | join('", "') }}",{% endif %}
-        entity_timestamp,
-        "{{featureview.name}}__entity_row_unique_id"
-    FROM entity_dataframe
-    GROUP BY
-        {% if featureview.entities %}"{{ featureview.entities | join('", "')}}",{% endif %}
-        entity_timestamp,
-        "{{featureview.name}}__entity_row_unique_id"
-),
-
-/*
- This query template performs the point-in-time correctness join for a single feature set table
- to the provided entity table.
-
- 1. We first join the current feature_view to the entity dataframe that has been passed.
- This JOIN has the following logic:
-    - For each row of the entity dataframe, only keep the rows where the `timestamp_field`
-    is less than the one provided in the entity dataframe
-    - If there a TTL for the current feature_view, also keep the rows where the `timestamp_field`
-    is higher the the one provided minus the TTL
-    - For each row, Join on the entity key and retrieve the `entity_row_unique_id` that has been
-    computed previously
-
- The output of this CTE will contain all the necessary information and already filtered out most
- of the data that is not relevant.
-*/
-
-"{{ featureview.name }}__subquery" AS (
-    SELECT
-        "{{ featureview.timestamp_field }}" as event_timestamp,
-        {{ '"' ~ featureview.created_timestamp_column ~ '" as created_timestamp,' if featureview.created_timestamp_column else '' }}
-        {{ featureview.entity_selections | join(', ')}}{% if featureview.entity_selections %},{% else %}{% endif %}
-        {% for feature in featureview.features %}
-            "{{ feature }}" as {% if full_feature_names %}"{{ featureview.name }}__{{featureview.field_mapping.get(feature, feature)}}"{% else %}"{{ featureview.field_mapping.get(feature, feature) }}"{% endif %}{% if loop.last %}{% else %}, {% endif %}
-        {% endfor %}
-    FROM {{ featureview.table_subquery }} AS sub
-    WHERE "{{ featureview.timestamp_field }}" <= (SELECT MAX(entity_timestamp) FROM entity_dataframe)
-    {% if featureview.ttl == 0 %}{% else %}
-    AND "{{ featureview.timestamp_field }}" >= (SELECT MIN(entity_timestamp) FROM entity_dataframe) - {{ featureview.ttl }} * interval '1' second
-    {% endif %}
-),
-
-"{{ featureview.name }}__base" AS (
-    SELECT
-        subquery.*,
-        entity_dataframe.entity_timestamp,
-        entity_dataframe."{{featureview.name}}__entity_row_unique_id"
-    FROM "{{ featureview.name }}__subquery" AS subquery
-    INNER JOIN "{{ featureview.name }}__entity_dataframe" AS entity_dataframe
-    ON TRUE
-        AND subquery.event_timestamp <= entity_dataframe.entity_timestamp
-
-        {% if featureview.ttl == 0 %}{% else %}
-        AND subquery.event_timestamp >= entity_dataframe.entity_timestamp - {{ featureview.ttl }} * interval '1' second
-        {% endif %}
-
-        {% for entity in featureview.entities %}
-        AND subquery."{{ entity }}" = entity_dataframe."{{ entity }}"
-        {% endfor %}
-),
-
-/*
- 2. If the `created_timestamp_column` has been set, we need to
- deduplicate the data first. This is done by calculating the
- `MAX(created_at_timestamp)` for each event_timestamp.
- We then join the data on the next CTE
-*/
-{% if featureview.created_timestamp_column %}
-"{{ featureview.name }}__dedup" AS (
-    SELECT
-        "{{featureview.name}}__entity_row_unique_id",
-        event_timestamp,
-        MAX(created_timestamp) as created_timestamp
-    FROM "{{ featureview.name }}__base"
-    GROUP BY "{{featureview.name}}__entity_row_unique_id", event_timestamp
-),
-{% endif %}
-
-/*
- 3. The data has been filtered during the first CTE "*__base"
- Thus we only need to compute the latest timestamp of each feature.
-*/
-"{{ featureview.name }}__latest" AS (
-    SELECT
-        event_timestamp,
-        {% if featureview.created_timestamp_column %}created_timestamp,{% endif %}
-        "{{featureview.name}}__entity_row_unique_id"
-    FROM
-    (
-        SELECT *,
-            ROW_NUMBER() OVER(
-                PARTITION BY "{{featureview.name}}__entity_row_unique_id"
-                ORDER BY event_timestamp DESC{% if featureview.created_timestamp_column %},created_timestamp DESC{% endif %}
-            ) AS row_number
-        FROM "{{ featureview.name }}__base"
-        {% if featureview.created_timestamp_column %}
-            INNER JOIN "{{ featureview.name }}__dedup"
-            USING ("{{featureview.name}}__entity_row_unique_id", event_timestamp, created_timestamp)
-        {% endif %}
-    ) AS sub
-    WHERE row_number = 1
-),
-
-/*
- 4. Once we know the latest value of each feature for a given timestamp,
- we can join again the data back to the original "base" dataset
-*/
-"{{ featureview.name }}__cleaned" AS (
-    SELECT base.*
-    FROM "{{ featureview.name }}__base" as base
-    INNER JOIN "{{ featureview.name }}__latest"
-    USING(
-        "{{featureview.name}}__entity_row_unique_id",
-        event_timestamp
-        {% if featureview.created_timestamp_column %}
-            ,created_timestamp
-        {% endif %}
-    )
-){% if loop.last %}{% else %}, {% endif %}
-
-
-{% endfor %}
-/*
- Joins the outputs of multiple time travel joins to a single table.
- The entity_dataframe dataset being our source of truth here.
- */
-
-SELECT "{{ final_output_feature_names | join('", "')}}"
-FROM entity_dataframe
-{% for featureview in featureviews %}
-LEFT JOIN (
-    SELECT
-        "{{featureview.name}}__entity_row_unique_id"
-        {% for feature in featureview.features %}
-            ,"{% if full_feature_names %}{{ featureview.name }}__{{featureview.field_mapping.get(feature, feature)}}{% else %}{{ featureview.field_mapping.get(feature, feature) }}{% endif %}"
-        {% endfor %}
-    FROM "{{ featureview.name }}__cleaned"
-) AS "{{featureview.name}}" USING ("{{featureview.name}}__entity_row_unique_id")
-{% endfor %}
-"""
diff --git a/sdk/python/feast/infra/transformation_servers/Dockerfile b/sdk/python/feast/infra/transformation_servers/Dockerfile
index 653e34cdf5..79997ce01b 100644
--- a/sdk/python/feast/infra/transformation_servers/Dockerfile
+++ b/sdk/python/feast/infra/transformation_servers/Dockerfile
@@ -9,7 +9,7 @@ COPY protos protos
 COPY README.md README.md
 
 # Install dependencies
-RUN pip3 install -e 'sdk/python[ci]'
+RUN pip3 install -e 'sdk/python'
 
 # Start feature transformation server
 CMD [ "python", "app.py" ]
diff --git a/sdk/python/requirements/py3.10-ci-requirements.txt b/sdk/python/requirements/py3.10-ci-requirements.txt
index 346aa6da47..99258bd96d 100644
--- a/sdk/python/requirements/py3.10-ci-requirements.txt
+++ b/sdk/python/requirements/py3.10-ci-requirements.txt
@@ -255,6 +255,8 @@ grpcio-tools==1.44.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
+happybase==1.2.0
+    # via feast (setup.py)
 hiredis==2.0.0
     # via feast (setup.py)
 httplib2==0.20.4
@@ -356,9 +358,10 @@ nbformat==5.3.0
     # via great-expectations
 nodeenv==1.6.0
     # via pre-commit
-numpy==1.22.3
+numpy==1.21.6
     # via
     #   altair
+    #   feast (setup.py)
     #   great-expectations
     #   pandas
     #   pandavro
@@ -407,6 +410,8 @@ platformdirs==2.5.1
     # via virtualenv
 pluggy==1.0.0
     # via pytest
+ply==3.11
+    # via thriftpy2
 portalocker==2.4.0
     # via msal-extensions
 pre-commit==2.18.1
@@ -594,6 +599,7 @@ six==1.16.0
     #   google-cloud-core
     #   google-resumable-media
     #   grpcio
+    #   happybase
     #   mock
     #   msrestazure
     #   pandavro
@@ -638,6 +644,8 @@ termcolor==1.1.0
     # via great-expectations
 testcontainers==3.5.3
     # via feast (setup.py)
+thriftpy2==0.4.14
+    # via happybase
 toml==0.10.2
     # via
     #   black
diff --git a/sdk/python/requirements/py3.10-requirements.txt b/sdk/python/requirements/py3.10-requirements.txt
index 455d80b790..55c5807b50 100644
--- a/sdk/python/requirements/py3.10-requirements.txt
+++ b/sdk/python/requirements/py3.10-requirements.txt
@@ -73,8 +73,9 @@ markupsafe==2.1.1
     # via jinja2
 mmh3==3.0.0
     # via feast (setup.py)
-numpy==1.22.3
+numpy==1.21.6
     # via
+    #   feast (setup.py)
     #   pandas
     #   pandavro
     #   pyarrow
diff --git a/sdk/python/requirements/py3.7-ci-requirements.txt b/sdk/python/requirements/py3.7-ci-requirements.txt
index 224840a6f7..2d0da79a65 100644
--- a/sdk/python/requirements/py3.7-ci-requirements.txt
+++ b/sdk/python/requirements/py3.7-ci-requirements.txt
@@ -265,6 +265,8 @@ grpcio-tools==1.44.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
+happybase==1.2.0
+    # via feast (setup.py)
 hiredis==2.0.0
     # via feast (setup.py)
 httplib2==0.20.4
@@ -430,6 +432,7 @@ notebook==6.4.10
 numpy==1.21.5
     # via
     #   altair
+    #   feast (setup.py)
     #   great-expectations
     #   pandas
     #   pandavro
@@ -481,6 +484,8 @@ platformdirs==2.5.1
     # via virtualenv
 pluggy==1.0.0
     # via pytest
+ply==3.11
+    # via thriftpy2
 portalocker==2.4.0
     # via msal-extensions
 pre-commit==2.17.0
@@ -682,7 +687,7 @@ six==1.16.0
     #   google-cloud-core
     #   google-resumable-media
     #   grpcio
-    #   isodate
+    #   happybase
     #   mock
     #   msrestazure
     #   pandavro
@@ -727,8 +732,8 @@ terminado==0.13.2
     # via notebook
 testcontainers==3.5.3
     # via feast (setup.py)
-testpath==0.6.0
-    # via nbconvert
+thriftpy2==0.4.14
+    # via happybase
 toml==0.10.2
     # via
     #   black
diff --git a/sdk/python/requirements/py3.7-requirements.txt b/sdk/python/requirements/py3.7-requirements.txt
index f3c90a6e3b..1ca911f134 100644
--- a/sdk/python/requirements/py3.7-requirements.txt
+++ b/sdk/python/requirements/py3.7-requirements.txt
@@ -79,6 +79,7 @@ mmh3==3.0.0
     # via feast (setup.py)
 numpy==1.21.5
     # via
+    #   feast (setup.py)
     #   pandas
     #   pandavro
     #   pyarrow
diff --git a/sdk/python/requirements/py3.8-ci-requirements.txt b/sdk/python/requirements/py3.8-ci-requirements.txt
index 34032ef7d2..f1d46e6ec4 100644
--- a/sdk/python/requirements/py3.8-ci-requirements.txt
+++ b/sdk/python/requirements/py3.8-ci-requirements.txt
@@ -259,6 +259,8 @@ grpcio-tools==1.44.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
+happybase==1.2.0
+    # via feast (setup.py)
 hiredis==2.0.0
     # via feast (setup.py)
 httplib2==0.20.4
@@ -362,9 +364,10 @@ nbformat==5.3.0
     # via great-expectations
 nodeenv==1.6.0
     # via pre-commit
-numpy==1.22.3
+numpy==1.21.6
     # via
     #   altair
+    #   feast (setup.py)
     #   great-expectations
     #   pandas
     #   pandavro
@@ -413,6 +416,8 @@ platformdirs==2.5.1
     # via virtualenv
 pluggy==1.0.0
     # via pytest
+ply==3.11
+    # via thriftpy2
 portalocker==2.4.0
     # via msal-extensions
 pre-commit==2.18.1
@@ -602,6 +607,7 @@ six==1.16.0
     #   google-cloud-core
     #   google-resumable-media
     #   grpcio
+    #   happybase
     #   mock
     #   msrestazure
     #   pandavro
@@ -646,6 +652,8 @@ termcolor==1.1.0
     # via great-expectations
 testcontainers==3.5.3
     # via feast (setup.py)
+thriftpy2==0.4.14
+    # via happybase
 toml==0.10.2
     # via
     #   black
diff --git a/sdk/python/requirements/py3.8-requirements.txt b/sdk/python/requirements/py3.8-requirements.txt
index 9000c7b1f7..f0fc61e1b2 100644
--- a/sdk/python/requirements/py3.8-requirements.txt
+++ b/sdk/python/requirements/py3.8-requirements.txt
@@ -75,8 +75,9 @@ markupsafe==2.1.1
     # via jinja2
 mmh3==3.0.0
     # via feast (setup.py)
-numpy==1.22.3
+numpy==1.21.6
     # via
+    #   feast (setup.py)
     #   pandas
     #   pandavro
     #   pyarrow
diff --git a/sdk/python/requirements/py3.9-ci-requirements.txt b/sdk/python/requirements/py3.9-ci-requirements.txt
index 1ab910a16d..c79daa3ffa 100644
--- a/sdk/python/requirements/py3.9-ci-requirements.txt
+++ b/sdk/python/requirements/py3.9-ci-requirements.txt
@@ -255,6 +255,8 @@ grpcio-tools==1.44.0
     # via feast (setup.py)
 h11==0.13.0
     # via uvicorn
+happybase==1.2.0
+    # via feast (setup.py)
 hiredis==2.0.0
     # via feast (setup.py)
 httplib2==0.20.4
@@ -356,9 +358,10 @@ nbformat==5.3.0
     # via great-expectations
 nodeenv==1.6.0
     # via pre-commit
-numpy==1.22.3
+numpy==1.21.6
     # via
     #   altair
+    #   feast (setup.py)
     #   great-expectations
     #   pandas
     #   pandavro
@@ -407,6 +410,8 @@ platformdirs==2.5.1
     # via virtualenv
 pluggy==1.0.0
     # via pytest
+ply==3.11
+    # via thriftpy2
 portalocker==2.4.0
     # via msal-extensions
 pre-commit==2.18.1
@@ -596,6 +601,7 @@ six==1.16.0
     #   google-cloud-core
     #   google-resumable-media
     #   grpcio
+    #   happybase
     #   mock
     #   msrestazure
     #   pandavro
@@ -640,6 +646,8 @@ termcolor==1.1.0
     # via great-expectations
 testcontainers==3.5.3
     # via feast (setup.py)
+thriftpy2==0.4.14
+    # via happybase
 toml==0.10.2
     # via
     #   black
diff --git a/sdk/python/requirements/py3.9-requirements.txt b/sdk/python/requirements/py3.9-requirements.txt
index 6413886c5b..1098331989 100644
--- a/sdk/python/requirements/py3.9-requirements.txt
+++ b/sdk/python/requirements/py3.9-requirements.txt
@@ -73,8 +73,9 @@ markupsafe==2.1.1
     # via jinja2
 mmh3==3.0.0
     # via feast (setup.py)
-numpy==1.22.3
+numpy==1.21.6
     # via
+    #   feast (setup.py)
     #   pandas
     #   pandavro
     #   pyarrow
diff --git a/sdk/python/setup.py b/sdk/python/setup.py
index 44cee568f8..6fbba14435 100644
--- a/sdk/python/setup.py
+++ b/sdk/python/setup.py
@@ -57,6 +57,7 @@
     "Jinja2>=2.0.0",
     "jsonschema",
     "mmh3",
+    "numpy<1.22",  # 1.22 drops support for python 3.7.
     "pandas>=1.0.0",
     "pandavro==1.5.*",
     "protobuf>=3.10,<3.20",
diff --git a/sdk/python/tests/unit/test_feature_views.py b/sdk/python/tests/unit/test_feature_views.py
deleted file mode 100644
index 8708a983c4..0000000000
--- a/sdk/python/tests/unit/test_feature_views.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from datetime import timedelta
-
-import pytest
-
-from feast.batch_feature_view import BatchFeatureView
-from feast.data_format import AvroFormat
-from feast.data_source import KafkaSource
-from feast.infra.offline_stores.file_source import FileSource
-from feast.stream_feature_view import StreamFeatureView
-
-
-def test_create_batch_feature_view():
-    batch_source = FileSource(path="some path")
-    BatchFeatureView(
-        name="test batch feature view",
-        entities=[],
-        ttl=timedelta(days=30),
-        source=batch_source,
-    )
-
-    with pytest.raises(ValueError):
-        BatchFeatureView(
-            name="test batch feature view", entities=[], ttl=timedelta(days=30)
-        )
-
-    stream_source = KafkaSource(
-        name="kafka",
-        timestamp_field="",
-        bootstrap_servers="",
-        message_format=AvroFormat(""),
-        topic="topic",
-        batch_source=FileSource(path="some path"),
-    )
-    with pytest.raises(ValueError):
-        BatchFeatureView(
-            name="test batch feature view",
-            entities=[],
-            ttl=timedelta(days=30),
-            source=stream_source,
-        )
-
-
-def test_create_stream_feature_view():
-    stream_source = KafkaSource(
-        name="kafka",
-        timestamp_field="",
-        bootstrap_servers="",
-        message_format=AvroFormat(""),
-        topic="topic",
-        batch_source=FileSource(path="some path"),
-    )
-    StreamFeatureView(
-        name="test batch feature view",
-        entities=[],
-        ttl=timedelta(days=30),
-        source=stream_source,
-    )
-
-    with pytest.raises(ValueError):
-        StreamFeatureView(
-            name="test batch feature view", entities=[], ttl=timedelta(days=30)
-        )
-
-    with pytest.raises(ValueError):
-        StreamFeatureView(
-            name="test batch feature view",
-            entities=[],
-            ttl=timedelta(days=30),
-            source=FileSource(path="some path"),
-        )