[DOCS] Add documentation for read_iceberg

daft/io/_iceberg.py

@@ -70,6 +70,30 @@ def read_iceberg(
     pyiceberg_table: "PyIcebergTable",
     io_config: Optional["IOConfig"] = None,
 ) -> DataFrame:
+    """Create a DataFrame from an Iceberg table
+
+    Example:
+        >>> import pyiceberg
+        >>>
+        >>> pyiceberg_table = pyiceberg.Table(...)
+        >>> df = daft.read_iceberg(pyiceberg_table)
+        >>>
+        >>> # Filters on this dataframe can now be pushed into
+        >>> # the read operation from Iceberg
+        >>> df = df.where(df["foo"] > 5)
+        >>> df.show()
+
+    .. NOTE::
+        This function requires the use of `PyIceberg <https://py.iceberg.apache.org/>`_, which is the Apache Iceberg's
+        official project for Python.
+
+    Args:
+        pyiceberg_table: Iceberg table created using the PyIceberg library
+        io_config: A custom IOConfig to use when accessing Iceberg object storage data. Defaults to None.
+
+    Returns:
+        DataFrame: a DataFrame with the schema converted from the specified Iceberg table
+    """
     from daft.iceberg.iceberg_scan import IcebergScanOperator
 
     io_config = (

docs/source/api_docs/creation.rst

@@ -20,71 +20,83 @@ Python Objects
     from_pylist
     from_pydict
 
-Arrow
-~~~~~
+Files
+-----
+
+.. _df-io-files:
+
+Parquet
+~~~~~~~
+
+.. _daft-read-parquet:
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
+    read_parquet
+
+CSV
+~~~
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
-    from_arrow
+    read_csv
 
-Pandas
-~~~~~~
+JSON
+~~~~
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
-    from_pandas
+    read_json
 
-File Paths
-~~~~~~~~~~
+Data Catalogs
+-------------
+
+Apache Iceberg
+^^^^^^^^^^^^^^
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
-    from_glob_path
+    read_iceberg
 
-Files
------
-
-.. _df-io-files:
+Arrow
+~~~~~
 
-Parquet
-~~~~~~~
+.. autosummary::
+    :nosignatures:
+    :toctree: doc_gen/io_functions
 
-.. _daft-read-parquet:
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
-    read_parquet
+    from_arrow
 
-CSV
-~~~
+Pandas
+~~~~~~
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
-    read_csv
+    from_pandas
 
-JSON
-~~~~
+File Paths
+~~~~~~~~~~
 
 .. autosummary::
     :nosignatures:
     :toctree: doc_gen/io_functions
 
-    read_json
+    from_glob_path
 
 Integrations
 ------------

docs/source/user_guide/index.rst

@@ -9,6 +9,7 @@ Daft User Guide
     basic_concepts
     daft_in_depth
     poweruser
+    integrations
     tutorials
 
 Welcome to **Daft**!
@@ -61,6 +62,11 @@ Core Daft concepts all Daft users will find useful to understand deeply.
 
 Become a true Daft Poweruser! This section explores advanced topics to help you configure Daft for specific application environments, improve reliability and optimize for performance.
 
+:doc:`Integrations <integrations>`
+**********************************
+
+Learn how to use Daft's integrations with other technologies such as Ray Datasets or Apache Iceberg.
+
 :doc:`Tutorials <tutorials>`
 ****************************
 

docs/source/user_guide/integrations.rst

@@ -0,0 +1,6 @@
+Integrations
+============
+
+.. toctree::
+
+    integrations/data_catalogs

docs/source/user_guide/integrations/data_catalogs.rst

@@ -0,0 +1,43 @@
+Data Catalogs
+=============
+
+**Data Catalogs** are services that provide access to **Tables** of data. **Tables** are powerful abstractions for large datasets in storage, providing many benefits over naively storing data as just a bunch of CSV/Parquet files.
+
+There are many different **Table Formats** that are employed by Data Catalogs. These table formats will differ implementation and capabilities, but will often provide advantages such as:
+
+1. **Schema:** what data do these files contain?
+2. **Partitioning Specification:** how is the data organized?
+3. **Statistics/Metadata:** how many rows does each file contain, and what are the min/max values of each files' columns?
+4. **ACID compliance:** updates to the table are atomic
+
+.. NOTE::
+    The names of Table Formats and their Data Catalogs are often used interchangeably.
+
+    For example, "Apache Iceberg" often refers to both the Data Catalog and its Table Format.
+
+    You can retrieve an **Apache Iceberg Table** from an **Apache Iceberg REST Data Catalog**.
+
+    However, some Data Catalogs allow for many different underlying Table Formats. For example, you can request both an **Apache Iceberg Table** or a **Hive Table** from an **AWS Glue Data Catalog**.
+
+Why use Data Catalogs?
+----------------------
+
+Daft can effectively leverage the statistics and metadata provided by these Data Catalogs' Tables to dramatically speed up queries.
+
+This is accomplished by techniques such as:
+
+1. **Partition pruning:** ignore files where their partition values don't match filter predicates
+2. **Schema retrieval:** convert the schema provided by the data catalog into a Daft schema instead of sampling a schema from the data
+3. **Metadata execution**: utilize metadata such as row counts to read the bare minimum amount of data necessary from storage
+
+Data Catalog Integrations
+-------------------------
+
+Apache Iceberg
+^^^^^^^^^^^^^^
+
+Apache Iceberg is an open-sourced table format originally developed at Netflix for large-scale analytical datasets.
+
+To read from the Apache Iceberg table format, use the :func:`daft.read_iceberg` function.
+
+We integrate closely with `PyIceberg <https://py.iceberg.apache.org/>`_ (the official Python implementation for Apache Iceberg) and allow the reading of Daft dataframes from PyIceberg's Table objects.