add new trends to Section 11 (#165)

Author: tomkralidis

workshop/jupyter/content/notebooks/11-emerging-technology-trends.ipynb

@@ -7,6 +7,36 @@
     "# Emerging technology and trends"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cloud and big multidimensional data\n",
+    "\n",
+    "### Overview\n",
+    "\n",
+    "From [OGC](https://www.ogc.org/ogc-topics/cloud-native-geospatial/):\n",
+    "\n",
+    "> Cloud-native geospatial offers many benefits to location data users ranging from decreasing the burden on data providers, to drastically lowering the costs of managing that data. Once the data is in the right cloud-native geospatial formats then it’s easy to tap into a rich ecosystem of platforms and tools without having to download large data files. This also increases the applicability of cloud-scale tools, and magnifies the impact of geospatial insights to a solution.\n",
+    "\n",
+    "Given the increased possibilities provided by cloud native approaches, a new suite of tools are developed to aid in lowering the barrier to discovery, access, visualization and compute of big geospatial data at scale.\n",
+    "\n",
+    "### Formats\n",
+    "\n",
+    "To leverage the advantages of cloud, a number of formats are becoming popular with the geospatial community, including, but not limited to:\n",
+    "\n",
+    "- [Apache Parquet](https://parquet.apache.org)\n",
+    "- [DuckDB](https://duckdb.org)\n",
+    "- [zarr](https://zarr.dev)\n",
+    "- [Cloud Optimized GeoTIFF](https://www.cogeo.org)\n",
+    "\n",
+    "All of the above formats have spatial capabilities, either directly or via specialized extensions.\n",
+    "\n",
+    "### Big multidimensional data hanndling\n",
+    "\n",
+    "Earth observation and Earth system data are now more available than ever.  [Pangeo](https://pangeo.io) is a popular community promoting open, reproducible, and scalable science.  Tools such as [xarray](https://docs.xarray.dev/en/stable), and [Dask](https://www.dask.org) are primary tools in the handling of big geospatial data such as climate/weather forecasting and Earth observation.  In addition, [pyogrio](https://github.com/geopandas/pyogrio) is a relatively new Python package to provide faster vector data handling.\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -217,9 +247,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.12"
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  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }