diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3ecdab37..34eb23b2 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -71,9 +71,10 @@ Example
 
    user-guide/introduction
    user-guide/basics
-   user-guide/configuration
+   user-guide/data-sources
    user-guide/common-operations/index
    user-guide/io/index
+   user-guide/configuration
    user-guide/sql
 
 
diff --git a/docs/source/user-guide/common-operations/index.rst b/docs/source/user-guide/common-operations/index.rst
index b15b04c6..d7c708c2 100644
--- a/docs/source/user-guide/common-operations/index.rst
+++ b/docs/source/user-guide/common-operations/index.rst
@@ -18,6 +18,8 @@
 Common Operations
 =================
 
+The contents of this section are designed to guide a new user through how to use DataFusion.
+
 .. toctree::
    :maxdepth: 2
 
diff --git a/docs/source/user-guide/data-sources.rst b/docs/source/user-guide/data-sources.rst
new file mode 100644
index 00000000..b7f10b41
--- /dev/null
+++ b/docs/source/user-guide/data-sources.rst
@@ -0,0 +1,185 @@
+.. Licensed to the Apache Software Foundation (ASF) under one
+.. or more contributor license agreements.  See the NOTICE file
+.. distributed with this work for additional information
+.. regarding copyright ownership.  The ASF licenses this file
+.. to you under the Apache License, Version 2.0 (the
+.. "License"); you may not use this file except in compliance
+.. with the License.  You may obtain a copy of the License at
+
+..   http://www.apache.org/licenses/LICENSE-2.0
+
+.. Unless required by applicable law or agreed to in writing,
+.. software distributed under the License is distributed on an
+.. "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+.. KIND, either express or implied.  See the License for the
+.. specific language governing permissions and limitations
+.. under the License.
+
+.. _user_guide_data_sources:
+
+Data Sources
+============
+
+DataFusion provides a wide variety of ways to get data into a DataFrame to perform operations.
+
+Local file
+----------
+
+DataFusion has the abilty to read from a variety of popular file formats, such as :ref:`Parquet <io_parquet>`,
+:ref:`CSV <io_csv>`, :ref:`JSON <io_json>`, and :ref:`AVRO <io_avro>`.
+
+.. ipython:: python
+
+    from datafusion import SessionContext
+    ctx = SessionContext()
+    df = ctx.read_csv("pokemon.csv")
+    df.show()
+
+Create in-memory
+----------------
+
+Sometimes it can be convenient to create a small DataFrame from a Python list or dictionary object.
+To do this in DataFusion, you can use one of the three functions
+:py:func:`~datafusion.context.SessionContext.from_pydict`,
+:py:func:`~datafusion.context.SessionContext.from_pylist`, or
+:py:func:`~datafusion.context.SessionContext.create_dataframe`.
+
+As their names suggest, ``from_pydict`` and ``from_pylist`` will create DataFrames from Python
+dictionary and list objects, respectively. ``create_dataframe`` assumes you will pass in a list
+of list of `PyArrow Record Batches <https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatch.html>`_.
+
+The following three examples all will create identical DataFrames:
+
+.. ipython:: python
+
+    import pyarrow as pa
+
+    ctx.from_pylist([
+        { "a": 1, "b": 10.0, "c": "alpha" },
+        { "a": 2, "b": 20.0, "c": "beta" },
+        { "a": 3, "b": 30.0, "c": "gamma" },
+    ]).show()
+
+    ctx.from_pydict({
+        "a": [1, 2, 3],
+        "b": [10.0, 20.0, 30.0],
+        "c": ["alpha", "beta", "gamma"],
+    }).show()
+
+    batch = pa.RecordBatch.from_arrays(
+        [
+            pa.array([1, 2, 3]),
+            pa.array([10.0, 20.0, 30.0]),
+            pa.array(["alpha", "beta", "gamma"]),
+        ],
+        names=["a", "b", "c"],
+    )
+
+    ctx.create_dataframe([[batch]]).show()
+
+
+Object Store
+------------
+
+DataFusion has support for multiple storage options in addition to local files.
+The example below requires an appropriate S3 account with access credentials.
+
+Supported Object Stores are
+
+- :py:class:`~datafusion.object_store.AmazonS3`
+- :py:class:`~datafusion.object_store.GoogleCloud`
+- :py:class:`~datafusion.object_store.Http`
+- :py:class:`~datafusion.object_store.LocalFileSystem`
+- :py:class:`~datafusion.object_store.MicrosoftAzure`
+
+.. code-block:: python
+
+    from datafusion.object_store import AmazonS3
+
+    region = "us-east-1"
+    bucket_name = "yellow-trips"
+
+    s3 = AmazonS3(
+        bucket_name=bucket_name,
+        region=region,
+        access_key_id=os.getenv("AWS_ACCESS_KEY_ID"),
+        secret_access_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
+    )
+
+    path = f"s3://{bucket_name}/"
+    ctx.register_object_store("s3://", s3, None)
+
+    ctx.register_parquet("trips", path)
+
+    ctx.table("trips").show()
+
+Other DataFrame Libraries
+-------------------------
+
+DataFusion can import DataFrames directly from other libraries, such as
+`Polars <https://pola.rs/>`_ and `Pandas <https://pandas.pydata.org/>`_.
+Since DataFusion version 42.0.0, any DataFrame library that supports the Arrow FFI PyCapsule
+interface can be imported to DataFusion using the
+:py:func:`~datafusion.context.SessionContext.from_arrow` function. Older verions of Polars may
+not support the arrow interface. In those cases, you can still import via the
+:py:func:`~datafusion.context.SessionContext.from_polars` function.
+
+.. ipython:: python
+
+    import pandas as pd
+
+    data = { "a": [1, 2, 3], "b": [10.0, 20.0, 30.0], "c": ["alpha", "beta", "gamma"] }
+    pandas_df = pd.DataFrame(data)
+
+    datafusion_df = ctx.from_arrow(pandas_df)
+    datafusion_df.show()
+
+    import polars as pl
+    polars_df = pl.DataFrame(data)
+
+    datafusion_df = ctx.from_arrow(polars_df)
+    datafusion_df.show()
+
+Delta Lake
+----------
+
+DataFusion 43.0.0 and later support the ability to register table providers from sources such
+as Delta Lake. This will require a recent version of
+`deltalake <https://delta-io.github.io/delta-rs/>`_ to provide the required interfaces.
+
+.. code-block:: python
+
+    from deltalake import DeltaTable
+
+    delta_table = DeltaTable("path_to_table")
+    ctx.register_table_provider("my_delta_table", delta_table)
+    df = ctx.table("my_delta_table")
+    df.show()
+
+On older versions of ``deltalake`` (prior to 0.22) you can use the 
+`Arrow DataSet <https://arrow.apache.org/docs/python/generated/pyarrow.dataset.Dataset.html>`_
+interface to import to DataFusion, but this does not support features such as filter push down
+which can lead to a significant performance difference.
+
+.. code-block:: python
+
+    from deltalake import DeltaTable
+
+    delta_table = DeltaTable("path_to_table")
+    ctx.register_dataset("my_delta_table", delta_table.to_pyarrow_dataset())
+    df = ctx.table("my_delta_table")
+    df.show()
+
+Iceberg
+-------
+
+Coming soon!
+
+Custom Table Provider
+---------------------
+
+You can implement a custom Data Provider in Rust and expose it to DataFusion through the
+the interface as describe in the :ref:`Custom Table Provider <io_custom_table_provider>`
+section. This is an advanced topic, but a
+`user example <https://github.com/apache/datafusion-python/tree/main/examples/ffi-table-provider>`_
+is provided in the DataFusion repository.
diff --git a/docs/source/user-guide/io/avro.rst b/docs/source/user-guide/io/avro.rst
index 5f1ff728..66398ac7 100644
--- a/docs/source/user-guide/io/avro.rst
+++ b/docs/source/user-guide/io/avro.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_avro:
+
 Avro
 ====
 
diff --git a/docs/source/user-guide/io/csv.rst b/docs/source/user-guide/io/csv.rst
index d2a62bfe..144b6615 100644
--- a/docs/source/user-guide/io/csv.rst
+++ b/docs/source/user-guide/io/csv.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_csv:
+
 CSV
 ===
 
diff --git a/docs/source/user-guide/io/json.rst b/docs/source/user-guide/io/json.rst
index f9da3755..39030db7 100644
--- a/docs/source/user-guide/io/json.rst
+++ b/docs/source/user-guide/io/json.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_json:
+
 JSON
 ====
 `JSON <https://www.json.org/json-en.html>`_ (JavaScript Object Notation) is a lightweight data-interchange format.
diff --git a/docs/source/user-guide/io/parquet.rst b/docs/source/user-guide/io/parquet.rst
index 75bc981c..c5b9ca3d 100644
--- a/docs/source/user-guide/io/parquet.rst
+++ b/docs/source/user-guide/io/parquet.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_parquet:
+
 Parquet
 =======
 
@@ -22,7 +24,6 @@ It is quite simple to read a parquet file using the :py:func:`~datafusion.contex
 
 .. code-block:: python
 
-
     from datafusion import SessionContext
 
     ctx = SessionContext()
diff --git a/docs/source/user-guide/io/table_provider.rst b/docs/source/user-guide/io/table_provider.rst
index 2ff9ae46..bd1d6b80 100644
--- a/docs/source/user-guide/io/table_provider.rst
+++ b/docs/source/user-guide/io/table_provider.rst
@@ -15,6 +15,8 @@
 .. specific language governing permissions and limitations
 .. under the License.
 
+.. _io_custom_table_provider:
+
 Custom Table Provider
 =====================