Who's afraid of Open Source in 2013?

Recently I have read this article (http://tinyurl.com/d3lnxns) and the related comments on ComputerWeekly.com and then I asked myself (for the billionth time):"Why the hell in 2013 are there still people frightened by Open Source?". Some governments and big organizations are discovering belatedly the advantages of Open Source software adoption, after wasting a lot of money on license purchasing and above all on maintenance, bug fixing, support and customization (wasting a lot of time too (and time is more precious than money)) . It's sad to see that in some cases the choice of Open Source is just because someone thinks that it's cheaper than proprietary: Open Source means also the possibility to reduce the learning curve of a software, to quickly discover and fix possible bugs, to find a way to improve or extend the code (no lock-ins as for the proprietary), to have a large community to share tips and suggestions, to improve your personal knowledge and to find often a better quality. I am no dogmatic about Open Source: if a proprietary solution is well coded and stable, suits fine my business requirements and grants me a real, efficient and prompt support I have no problems to adopt it. But each time there is a good Open Source alternative I prefer this one for the reasons above. Furthermore I think that companies should decide to reinvest in people a big part of the money saved by adopting Open Source solutions: people are the real value of a company.

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Streamsets Data Collector log shipping and analysis using ElasticSearch, Kibana and... the Streamsets Data Collector

One common use case scenario for the Streamsets Data Collector (SDC) is the log shipping to some system, like ElasticSearch, for real-time analysis. To build a pipeline for this particular purpose in SDC is really simple and fast and doesn't require coding at all. For this quick tutorial I will use the SDC logs as example. The log data will be shipped to Elasticsearch and then visualized through a Kibana dashboard. Basic knowledge of SDC, Elasticsearch and Kibana is required for a better understanding of this post. These are the releases I am referring to for each system involved in this tutorial: JDK 8 Streamsets Data Collector 1.4.0 ElasticSearch 2.3.3 Kibana 4.5.1 Elasticsearch and Kibana installation You should have your Elasticsearch cluster installed and configured and a Kibana instance pointing to that cluster in order to go on with this tutorial. Please refer to the official documentation for these two products in order to complete their installation (if you do

Exporting InfluxDB data to a CVS file

Sometimes you would need to export a sample of the data from an InfluxDB table to a CSV file (for example to allow a data scientist to do some offline analysis using a tool like Jupyter, Zeppelin or Spark Notebook). It is possible to perform this operation through the influx command line client. This is the general syntax: sudo /usr/bin/influx -database '<database_name>' -host '<hostname>' -username '<username>' -password '<password>' -execute 'select_statement' -format '<format>' > <file_path>/<file_name>.csv where the format could be csv , json or column . Example: sudo /usr/bin/influx -database 'telegraf' -host 'localhost' -username 'admin' -password '123456789' -execute 'select * from mem' -format 'csv' > /home/googlielmo/influxdb-export/mem-export.csv

Using Rapids cuDF in a Colab notebook

During last Spark+AI Summit Europe 2019 I had a chance to attend a talk from Miguel Martinez who was presenting Rapids , the new Open Source framework from NVIDIA for GPU accelerated end-to-end Data Science and Analytics. Fig. 1 - Overview of the Rapids eco-system Rapids is a suite of Open Source libraries: cuDF cuML cuGraph cuXFilter I enjoied the presentation and liked the idea of this initiative, so I wanted to start playing with the Rapids libraries in Python on Colab , starting from cuDF, but the first attempt came with an issue that I eventually solved. So in this post I am going to share how I fixed it, with the hope it would be useful to someone else running into the same blocker. I am assuming here you are already familiar with Google Colab. I am using Python 3.x as Python 2 isn't supported by Rapids. Once you have created a new notebook in Colab, you need to check if the runtime for it is set to use Python 3 and uses a GPU as hardware accelerator. You

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