How to provide raster data to a GeoServer?
One node will host a GeoServer instance to provide maps to a web application (built with OpenLayers). My problem is how to provide rasters (GeoTIFF format) to GeoServer. I have two main solutions for the storage: Yse a tool (HBase?Accumulo?) to save raster data in some way. Which one is the best?
How to use rounding functions in float power?
To be able to use rounding functions we literally need to add 3 fields, 1 with the base calculation of type decimal, 1 is an integer (whole number) which is set to the decimal value (so a standard rounding applies) and then the actual field that calculates which value it is supposed to be.
Can a GeoServer read from a distributed file store?
As Geoserver uses GDAL and GDAL is able to read also from distributed file stores through its Virtual File Systems Driver you just have to define your datasource by providing an corresponding /vsihdfs/-Path to it, but I am afraid you need a special build of the GDAL java binding libraries with HDFS Support set to ‘yes’
How to round a float power platform by addition?
I worked around this ridiculous issue by doing a manual round up by addition. 1. Divide your number 2. Add 0.5 (if you want to round to nearest) or 0.9999 (if you want to round up, as I did in my case. Will have to configure based on your data/accuracy desired) 3. Convert to int, which will chop off everything beyond the decimal.
What’s the best way to store raster data?
There are a couple of LocationTech Projects which adress this, I think the most mature one is GeoMesa, but also GeoWave is able to store raster data in an distributed key/value (accumolo is mentioned most often here) store and connect it to geoserver. preferring the Big Spatial Store Option: I think you should have a look at Geomesa Raster tutorial
What can you do with raster data in spark?
This opens up your raster data for high througput processing engines like RasterFames and GeoTrellis which leverage Spark in general, and therefore allows you to run analytic pipelines on most of your data simultanously.