diff --git a/CHANGES.txt b/CHANGES.txt
index 59b20fc9..718a4818 100644
--- a/CHANGES.txt
+++ b/CHANGES.txt
@@ -1,10 +1,26 @@
 Changes
 --------------------------------------------------------------------------------
 
+3.0.0
+................................................................................
+
+* Pythonic pipeline creation https://github.com/PDAL/python/pull/91
+
+* Support streaming pipeline execution https://github.com/PDAL/python/pull/94
+
+* Replace Cython with PyBind11 https://github.com/PDAL/python/pull/102
+
+* Remove pdal.pio module https://github.com/PDAL/python/pull/101
+
+* Move readers.numpy and filters.python to separate repository https://github.com/PDAL/python/pull/104
+
+* Miscellaneous refactorings and cleanups
+
 2.3.5
 ................................................................................
 
 * Fix memory leak https://github.com/PDAL/python/pull/74
+
 * Handle metadata with invalid unicode by erroring https://github.com/PDAL/python/pull/74
 
 2.3.0
@@ -29,5 +45,3 @@ Changes
   schedule at https://github.com/PDAL/python
 
 * Extension now builds and works under PDAL OSGeo4W64 on Windows.
-
-
diff --git a/README.rst b/README.rst
index 37ca51b9..481b50e3 100644
--- a/README.rst
+++ b/README.rst
@@ -142,14 +142,14 @@ The following more complex scenario demonstrates the full cycling between
 PDAL and Python:
 
 * Read a small testfile from GitHub into a Numpy array
-* Filters those arrays with Numpy for Intensity
+* Filters the array with Numpy for Intensity
 * Pass the filtered array to PDAL to be filtered again
-* Write the filtered array to an LAS file.
+* Write the final filtered array to a LAS file and a TileDB_ array
+  via the `TileDB-PDAL integration`_ using the `TileDB writer plugin`_
 
 .. code-block:: python
 
     import pdal
-    import numpy as np
 
     data = "https://github.com/PDAL/PDAL/blob/master/test/data/las/1.2-with-color.las?raw=true"
 
@@ -175,9 +175,12 @@ PDAL and Python:
     print(pipeline.execute())  # 387 points
     clamped = pipeline.arrays[0]
 
-    # Write our intensity data to an LAS file
+    # Write our intensity data to a LAS file and a TileDB array. For TileDB it is
+    # recommended to use Hilbert ordering by default with geospatial point cloud data,
+    # which requires specifying a domain extent. This can be determined automatically
+    # from a stats filter that computes statistics about each dimension (min, max, etc.).
     pipeline = pdal.Writer.las(
-        filename="clamped2.las",
+        filename="clamped.las",
         offset_x="auto",
         offset_y="auto",
         offset_z="auto",
@@ -185,8 +188,14 @@ PDAL and Python:
         scale_y=0.01,
         scale_z=0.01,
     ).pipeline(clamped)
+    pipeline |= pdal.Filter.stats() | pdal.Writer.tiledb(array_name="clamped")
     print(pipeline.execute())  # 387 points
 
+    # Dump the TileDB array schema
+    import tiledb
+    with tiledb.open("clamped") as a:
+        print(a.schema)
+
 Executing Streamable Pipelines
 ................................................................................
 Streamable pipelines (pipelines that consist exclusively of streamable PDAL
@@ -293,6 +302,9 @@ USE-CASE : Take a LiDAR map, create a mesh from the ground points, split into ti
 .. _`Numpy`: http://www.numpy.org/
 .. _`schema`: http://www.pdal.io/dimensions.html
 .. _`metadata`: http://www.pdal.io/development/metadata.html
+.. _`TileDB`: https://tiledb.com/
+.. _`TileDB-PDAL integration`: https://docs.tiledb.com/geospatial/pdal
+.. _`TileDB writer plugin`: https://pdal.io/stages/writers.tiledb.html
 
 .. image:: https://github.com/PDAL/python/workflows/Build/badge.svg
    :target: https://github.com/PDAL/python/actions?query=workflow%3ABuild
diff --git a/pdal/__init__.py b/pdal/__init__.py
index 09d58be9..7bc49270 100644
--- a/pdal/__init__.py
+++ b/pdal/__init__.py
@@ -1,4 +1,4 @@
-__version__ = "2.4.2"
+__version__ = "3.0.0"
 __all__ = ["Pipeline", "Stage", "Reader", "Filter", "Writer", "dimensions", "info"]
 
 from . import libpdalpython