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 STDIN def wc_mapper(key: str, values: str): # remove leading and trailing whitespaces line = values.strip() # split the line into words words = line.split() for word in words: # write the results to standard # output STDOUT yield word, 1
def wc_reducer(key: str, values: list): current_count = 0 word = key for value in values: current_count += value yield word, current_count
Finally, call the function with the MapReduceSlim framework
# Import the slim framework from map_reduce_slim import MapReduceSlim, 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)
Further information @ Github: https://github.com/2er0/MapReduceSlim
Today Microsoft released the first Dev-Version of its Edge Browser for Linux.
Let’s have a first look on it within Kubuntu 20.04
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.
 Export your data from observation.org as SQLITE-dump:
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 🙂
Data science and Jupyter notebook can sometimes get exhausting. What about debugging, version control, code reviewing and so on. Coming from a Software Engineering background it‘s like losing 50% of the stuff you were used to.
To mitigate those problems I recently partially switched from Python to R with many improvements. For local Python coding, JetBrains PyCharm is my tool of choice and Jupyter notebooks for remote coding. With R it is RStudio Desktop and for remote, there is RStudio Server, which is almost like the desktop version within a browser. This allows one to develop and analyze data from any device with a browser.
The Center for Systems Science and Engineering (CSSE) at Johns Hopkins University provides the famous Corona Dashboard and Map (ESRI ArcGIS Online App) and an ArcGIS Feature Service with the recent data (and a GIT Repo with the raw data). The ArcGIS Feature Server support of QGIS makes it easy to have „some fun“ with QGIS and the provided datasets.