Altough the HP Elitebook 745 G2 (AMD Hardware) has some age, it’s a nice working-tool with good built-quality and mine works fine after 6 years intense use. BUT: HP reports the most recent BIOS versioned 1.48 – the BIOS internal update tool reports no update available based on V 1.44
Just for fun I gave Fedora and Gnome with version 34 a try again 🙂 One of the first things to do as a geoscientist has been the installation of QGIS… because of the not always up-to-date repo versions (and COPR), I selected the Flatpak-version… but got 3.16 LTS alltough I expected it to be 3.18.2 :-/ What I did not know, Flathub encapsulates 2 versions in one „Repo“.
MapReduce represents a pattern that had a huge impact on the data analysis and big data community. Apache Hadoop allows to scatter and scale data processing with the number of nodes and cores.
One of the many corner points in this full framework is that code is shipped and executed on-site where the data resides. Next, only a pre-processed transformed version (map) of the data is then shuffled and sorted to the aggregators on different executors via the network.
MapReduce is hard to use on its own, so it usually is deployed with
Apache Hadoop or Apache Spark. To play around with it without either one of those large frameworks, I created one in Python – MapReduceSlim. It emulates all core features of the MapReduce. It has one difference, it loads each line of the files separately into the map function. In the case of Apache Hadoop, it would be block-wise. This provides a nice solution to understand the behavior and the pattern of MapReduce and how to implement a mapper and reducer.
Classic WordCount Example
# Hint: in MapReduce with Hadoop Streaming the
# input comes from standard input STDINdefwc_mapper(key: str, values: str):
# remove leading and trailing whitespacesline=values.strip()
# split the line into wordswords=line.split()
# write the results to standard
# output STDOUTyieldword, 1
Finally, call the function with the MapReduceSlim framework
# Import the slim framework
frommap_reduce_slimimportMapReduceSlim, wc_mapper, wc_reduce
### One input file version
# Read the content from one file and use the
# content as input for the run.
MapReduceSlim('davinci.txt', 'davinci_wc_result_one_file.txt', wc_mapper, wc_reducer)
### Directory input version
# Read all files in the given directory and
# use the content as input for the run.
MapReduceSlim('davinci_split', 'davinci_wc_result_multiple_file.txt', wc_mapper, wc_reducer)
Good news for all users and fans of Geopackage – with ArcGIS 2.6 ESRI made it possible to edit features stored in a geopackage database directly 🙂 So after QGIS making geopackage it’s default „geodatabase“, ESRI also supports it including editing features.
I did a first workflow and ArcGIS Pro 2.6 did it’s job editing features stored in a geopackage 🙂
The nature observation platform observation.org provides a SQLite-dump of your observations. As a geospatial nerd it is obvious to have a deeper look on the database and how the location of the observations is stored… and to think one step further: Make a Spatialite database of it and use it directly in QGIS or ArcGIS.
QGIS 3.14 supports temporal data out of the box (many, many thanks to Anita Graser and the time manager plug-in in the previous versions of QGIS). The support of expressions within the temporal data settings could be really helpful 🙂