[DOCS] Create separate Iceberg docs

docs/source/user_guide/integrations/data_catalogs.rst → docs/source/user_guide/daft_in_depth/data-catalogs.rst

@@ -29,15 +29,3 @@ 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.

docs/source/user_guide/integrations.rst

@@ -3,4 +3,4 @@ Integrations
 
 .. toctree::
 
-    integrations/data_catalogs
+    integrations/iceberg

docs/source/user_guide/integrations/iceberg.rst

@@ -0,0 +1,40 @@
+Apache Iceberg
+==============
+
+`Apache Iceberg <https://iceberg.apache.org/>`_ 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 easily from PyIceberg's Table objects.
+
+.. code:: python
+
+    # Access a PyIceberg table as per normal
+    from pyiceberg.catalog import load_catalog
+
+    catalog = load_catalog("my_iceberg_catalog")
+    table = catalog.load_table("my_namespace.my_table")
+
+    # Create a Daft Dataframe
+    import daft
+
+    df = daft.read_iceberg(table)
+
+Daft currently natively supports:
+
+1. **Distributed Reads:** Daft will fully distribute the I/O of reads over your compute resources (whether Ray or on multithreading on the local PyRunner)
+2. **Skipping filtered data:** Daft uses ``df.where(...)`` filter calls to only read data that matches your predicates
+3. **All Catalogs From PyIceberg:** Daft is natively integrated with PyIceberg, and supports all the catalogs that PyIceberg does!
+
+Selecting a Table
+*****************
+
+Daft currently leverages PyIceberg for catalog/table discovery. Please consult `PyIceberg documentation <https://py.iceberg.apache.org/api/#load-a-table>`_ for more details on how to load a table!
+
+Roadmap
+*******
+
+Here are features of Iceberg that are works-in-progress.
+
+1. Iceberg V2 merge-on-read features
+2. Writing back to an Iceberg table (appends, overwrites, upserts)