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Luca Naso
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Today, technologies have advanced a great deal and made many things easier. This does not mean that implementing and doing advanced analytics is a no brainer!

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Il 23 e 24 marzo Google terrà la conferenza "Google Cloud Platform Next" (https://cloudplatformonline.com/NEXT2016.html), un evento per la divulgazione della tecnologia cloud di casa Google. Google ha deciso di dare la possibilità alla community globale dei GDG di seguire l'evento in diretta streaming, quello che nel nostro gergo viene detto fare una "extended" dell'evento principale.

Per l'occasione abbiamo deciso di tenere anche noi la nostra extended, che vogliamo condire con speech interni dedicati alla tecnologia cloud in generale e alle sue implementazioni più importanti nel panorama attuale.

Oltre agli speech i partecipanti potranno mettersi alla prova con vari codelab proposti da Google ed un'hackathon della tipologia "cracca al tesoro" (maggiori dettagli nei prossimi giorni).

-- Stay hungry, stay googlish :-)

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Ho sempre pensato che di imparare non si finisce mai. Ultimamente sono sempre piu' convinto che imparare sia anche divertente! C'e' pero' un problema non indifferente: "learning takes time", per imparare serve del tempo. Da qui la necessita' di ottimizzare il processo di apprendimento (il concetto di "efficienza" mi e' molto caro, e torna in maniera trasversale).

Un mese fa ho iniziato questo corso su Coursera, Learning How to Learn, cioe' "Imparare ad imparare". Oggi lo concludo e devo dire di averlo trovato molto interessante. Molte delle tecniche descritte nel corso le applico normalmente, ma senza sapere perche' siano cosi' efficaci. Altre non le conoscevo e sono stato felice di imparare ancora una volta qualcosa di nuovo.

E adesso via verso la prossima cosa da imparare, per mettere in pratica tutti i concetti appena appresi!

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Are you a Data Scientist coding in Python? Then you might find this tutorial helpful.
 
Virtualenvwrapper is a tool for managing virtual environments under Python.
 
This guide and video tutorial well explain the basic usage of the package and also give suggestions on how to go beyond that.

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In my opinion, Spark should NOT be compared with Hadoop but with MapReduce. However, people usually compare Hadoop and Spark (probably because they are buzzwords).
 
5 things to keep in mind:
 
1. They do different things -
Hadoop is a distributed data infrastructure (HDFS),
Spark is a data-processing tool.
 
2. Hadoop is more complete -
Hadoop also includes a data-processing tool (MapReduce),
Spark does not have its own filesystem and needs to be integrated with some.
 
3. Spark is (much) faster -
MapReduce operates in step;
Spark operates in one shot (because it is in-memory).
 
4. Speed is not always what you need -
For batch processing you do not need Spark's high velocity;
Common applications for Spark are those requiring real-time analysis.
 
5. Failure recovery -
both Hadoop and Spark are resilient to failures.

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In my coding activity with Python I use virtualenvwrapper quite often. Therefore, I decided to write this guide and make short a video, for those interested on how to use virtual environment in python.

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My 3 takeaways from 2015 Gartner's research on Big Data:
1. Big Data solutions and technologies are becoming mainstream;
2. Big Data is mainly used for enhancing customer experience;
3. Business Leaders are as active as CIO/CTO in launching Big Data projects.
 
Organisations are overwhelmingly targeting enhanced customer experience as the primary goal of big data projects (64 percent). Process efficiency and more-targeted marketing are now tied at 47 percent. As data breaches continue to make headlines, enhanced security capabilities saw the largest increase, from 15 percent to 23 percent.
 
Last year, 37 percent of big data projects were initiated by the CIO, while 25 percent were initiated by business unit heads. In 2015, the roles are nearly tied, at 32 percent and 31 percent, respectively.
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