Library GDAL (developed by OSGeo) is more or less standard for processing geospatial data. However, although it implements almost all the tools needed for this purpose, it is notoriously known for many operational problems. Mainly, it is practically impossible to install it quickly (as it requires many system-level dependencies), API is obsolete (designed in the late nineties), and it is not difficult to find unexpected behaviours, etc. So it is not surprising that many developers actively started to seek different tools. This post presents some possibilities for the most common issues (mainly focused on Python users).

One of the common challenges when dealing with big multidimensional data sets is how effectively store them. There are many problems to deal with, but maybe the most important is choosing the proper dimension order. Although typically, the selection of the optimal dimensional order does not impact the size of the output file (if you use some compression situation that may differ), it has a profound effect on the response time when accessing stored data.

The famous library xarray is more or less standard in the data analyst branch. During the last few years, it has become prevalent. It has been deployed in many projects, often without any careful decision if it is needed or if there is any other way to treat the issue. One of the essential xarray functionality is the reading and writing of the NetCDF files. This is also a focus of this article where at the end, you should be capable of making the informed decision whether or not to use xarray.