Updated the Large Data user guide introduction

guides/user_guide/Large_Data.ipynb

@@ -6,9 +6,9 @@
    "source": [
     "# Working with large data using datashader\n",
     "\n",
-    "The plotting extensions supported by HoloViews are good for different tasks depending on the strengths of the corresponding plotting library. For instance, matplotlib is the standard choice when generating output for publication and Bokeh offers excellent interactivity useful for data exploration. What none of these plotting libraries can handle directly are truly large datasets where the memory footprint of the visualized output is many magnitudes smaller than the data being visualized.\n",
+    "The various plotting backends supported by HoloViews (such as Matplotlib and Bokeh) each have  limitations on the amount of data that is practical to work with, for a variety of reasons.  For instance, Bokeh mirrors your data directly into an HTML page viewable in your browser, which can cause problems when data sizes approach the limited memory available for each web page in your browser.\n",
     "\n",
-    "[Datashader](https://github.com/bokeh/datashader) is a rasterizer that can process very large datasets quickly enough for interactive use. What this means is that it takes in some large dataset as input and outputs a raster which in HoloViews corresponds to either an ``RGB`` or ``Image`` element. This section of the user guide details the HoloViews operations designed to expose datashader's functionality when working with large data."
+    "Luckily, a visualization of even the largest dataset will be constrained by the resolution of your display device, and so one approach to handling such data is to pre-render or rasterize the data into a fixed-size array or image before sending it to the backend.  The [Datashader package](https://github.com/bokeh/datashader) provides a high-performance big-data rasterization pipeline that works seamlessly with HoloViews to support datasets that are orders of magnitude larger than those supported natively by the plotting backends."
    ]
   },
   {