diff --git a/examples/duckdb_plugin/README.md b/examples/duckdb_plugin/README.md
index fcf209b51..769269527 100644
--- a/examples/duckdb_plugin/README.md
+++ b/examples/duckdb_plugin/README.md
@@ -6,9 +6,9 @@
 .. tags:: Integration, Data, Analytics, Beginner
 ```
 
-[DuckDB](https://duckdb.org/) is an in-process SQL OLAP database management system that is explicitly designed to achieve high performance in analytics.
+[DuckDB](https://duckdb.org/) is an in-process SQL OLAP database management system that is explicitly designed to achieve high performance in analytics. MotherDuck is a collaborative data warehouse that extends the power of DuckDB to the cloud.
 
-The Flytekit DuckDB plugin facilitates the efficient execution of intricate analytical queries within your workflow.
+The Flytekit DuckDB plugin facilitates the efficient execution of intricate analytical queries within your workflow either in-process with DuckDB, on the cloud with MotherDuck, or a hybrid of the two.
 
 To install the Flytekit DuckDB plugin, run the following command:
 
@@ -18,8 +18,9 @@ pip install flytekitplugins-duckdb
 
 The Flytekit DuckDB plugin includes the {py:class}`~flytekitplugins:flytekitplugins.duckdb.DuckDBQuery` task, which allows you to specify the following parameters:
 
-- `query`: The DuckDB query to execute.
+- `query`: The DuckDB query to execute. This is optional as it can be passed at initialization or run time.
 - `inputs`: The query parameters to be used during query execution. This can be a StructuredDataset, a string or a list.
+- `provider`: This is a {py:class}`~flytekitplugins:flytekitplugins.duckdb.DuckDBProvider` or a callable that facilitates the connection to a remote database if desired.
 
 ```{auto-examples-toc}
 duckdb_example
diff --git a/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py b/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py
index 80f6b3993..c3ee5b484 100644
--- a/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py
+++ b/examples/duckdb_plugin/duckdb_plugin/duckdb_example.py
@@ -7,13 +7,13 @@
 
 # %%
 import json
-from typing import List
+from typing import List, Tuple
 
 import pandas as pd
 import pyarrow as pa
-from flytekit import kwtypes, task, workflow
+from flytekit import Secret, dynamic, kwtypes, task, workflow
 from flytekit.types.structured.structured_dataset import StructuredDataset
-from flytekitplugins.duckdb import DuckDBQuery
+from flytekitplugins.duckdb import DuckDBProvider, DuckDBQuery
 from typing_extensions import Annotated
 
 # %% [markdown]
@@ -160,3 +160,96 @@ def params_wf(
 
 if __name__ == "__main__":
     print(f"Running params_wf()... {params_wf()}")
+
+# %% [markdown]
+# ## Queries to MotherDuck
+#
+# The DuckDB plugin can be used to make DuckDB queries to a remote MotherDuck data warehouse by specifying the
+# MotherDuck `DuckDBProvider` and passing a secret called `motherduck_token`. Hybrid queries can target remote
+# data in MotherDuck and data local to a Flyte task at the same time.
+#
+# %%
+# This query targets a sample_data.nyc.rideshare table in MotherDuck
+motherduck_query = DuckDBQuery(
+    name="motherduck_query",
+    query="SELECT MEAN(trip_time) FROM sample_data.nyc.rideshare",
+    provider=DuckDBProvider.MOTHERDUCK,
+    secret_requests=[Secret(key="motherduck_token")],
+)
+
+# This query targets a e_commerce.year_09_10 table in MotherDuck and a DataFrame mydf local to the task
+hybrid_motherduck_query = DuckDBQuery(
+    name="my_query",
+    query="""
+    WITH HistoricalData AS
+        (SELECT COUNT(DISTINCT "Customer ID") AS CustomerCount_Historical FROM e_commerce.year_09_10),
+    RecentData AS
+        (SELECT COUNT(DISTINCT "Customer ID") AS CustomerCount_Recent FROM mydf)
+
+    SELECT HistoricalData.CustomerCount_Historical, RecentData.CustomerCount_Recent FROM HistoricalData, RecentData
+    """,
+    inputs=kwtypes(mydf=pd.DataFrame),
+    provider=DuckDBProvider.MOTHERDUCK,
+    secret_requests=[Secret(key="motherduck_token")],
+)
+
+
+@workflow
+def motherduck_wf(mydf: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    motherduck_response = motherduck_query()
+    hybrid_response = hybrid_motherduck_query(mydf=mydf)
+    return motherduck_response, hybrid_response
+
+
+if __name__ == "__main__":
+    print(f"Running motherduck_wf()... {motherduck_wf()}")
+
+# %% [markdown]
+# ## Runtime Queries
+#
+# If task logic is necessary to craft a query, the query can be passed at runtime rather than when the `DuckDBQuery` is
+# initialized.
+#
+# %%
+runtime_query_task = DuckDBQuery(
+    name="runtime_query_task",
+    inputs=kwtypes(query=str, mydf=pd.DataFrame),
+)
+
+
+@dynamic
+def check_dataframe(mydf: pd) -> pd.DataFrame:
+    col_a_present = "column_a" in mydf.columns
+    col_b_present = "column_b" in mydf.columns
+
+    if col_a_present and col_b_present:
+        query = """
+        SELECT column_a, column_b
+        FROM mydf
+        WHERE column_a > 10 AND column_b < 100;
+        """
+    elif col_a_present:
+        query = """
+        SELECT column_a
+        FROM mydf
+        WHERE column_a > 10;
+        """
+    elif col_b_present:
+        query = """
+        SELECT column_b
+        FROM mydf
+        WHERE column_b < 100;
+        """
+    else:
+        raise ValueError("Neither 'column_a' nor 'column_b' is present in the DataFrame.")
+
+    return simple_duckdb_query(query=query, mydf=mydf)
+
+
+@workflow
+def runtime_query_wf(mydf: pd.DataFrame) -> pd.DataFrame:
+    return check_dataframe(mydf=mydf)
+
+
+if __name__ == "__main__":
+    print(f"Running runtime_query_wf()... {runtime_query_wf()}")