DOC: Enhanced Quickstart documentation

docs/developers/index.rst

@@ -6,4 +6,4 @@ Developer's Guide
     :maxdepth: 1
 
     contributing
-    roadmap
+    roadmap

docs/quickstart.rst

@@ -12,10 +12,29 @@ Quickstart
 Welcome to the Zarr-Python Quickstart guide! This page will help you get up and running with
 the Zarr library in Python to efficiently manage and analyze multi-dimensional arrays.
 
+Introduction
+------------
+
 Zarr is a powerful library for storage of n-dimensional arrays, supporting chunking,
 compression, and various backends, making it a versatile choice for scientific and
 large-scale data.
 
+An `ndarray <https://numpy.org/doc/2.2/reference/arrays.ndarray.html>`_ is a (usually fixed-size) multidimensional container of items of the
+same type and size.
+
+
+Zarr's core features:
+
+- :ref:`Create<creating-an-array>`  N-dimensional arrays with any NumPy `dtype`.
+- :ref:`Chunk arrays<user-guide-chunks>` along any dimension.
+- :ref:`Compress<user-guide-compress>` and/or filter chunks using any NumCodecs codec.
+- :ref:`Store arrays<user-guide-storage>` in memory, on disk, inside a zip file, on S3, etc...
+- :ref:`Read<user-guide-array>` an array :ref:`concurrently <user-guide-sync>` from multiple threads or processes.
+- :ref:`Write<user-guide-array>` to an array concurrently from multiple threads or processes.
+- Organize arrays into hierarchies via :ref:`groups<hierarchical-groups>`.
+
+
+
 Installation
 ------------
 
@@ -31,6 +50,8 @@ or `conda`:
 
     conda install --channel conda-forge zarr
 
+.. _creating-an-array:
+
 Creating an Array
 -----------------
 
@@ -92,6 +113,8 @@ Zarr supports data compression and filters. For example, to use Blosc compressio
 
 This compresses the data using the Zstandard codec with shuffle enabled for better compression.
 
+.. _hierarchical-groups:
+
 Hierarchical Groups
 -------------------