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awk -F. '!a[$NF]++{print $NF}'

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awk -F\" '{print $(NF-1)}'

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mysqlimport -u root -p<password or prompt> -f --local <database> `pwd`/*.csv &> ../db_insert.log

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find . -name '*.sql' | awk '{ print "source",$0 }' | mysql --batch -u xxx -p <database>

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sudo service mysql stop
sudo mkdir /ebs1/etc /ebs1/lib /ebs1/log
sudo mv /etc/mysql /ebs1/etc
sudo mv /var/lib/mysql /ebs1/lib
sudo mv /var/log/mysql /ebs1/log
sudo mkdir /etc/mysql /var/lib/mysql /var/log/mysql
echo "/ebs1/etc/mysql /etc/mysql none bind" | sudo tee -a /etc/fstab
sudo mount /etc/mysql
echo "/ebs1/lib/mysql /var/lib/mysql none bind" | sudo tee -a /etc/fstab
sudo mount /var/lib/mysql

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set-alias installutil $env:windir\microsoft.net\framework\v2.0.50727\installutil
installutil -i "C:\Program Files\Microsoft SQL Server\100\Tools\Binn\Redist\Microsoft.SqlServer.Management.PSProvider.dll"
installutil -i "C:\Program Files\Microsoft SQL Server\100\Tools\Binn\Redist\Microsoft.SqlServer.Management.PSSnapins.dll" 


Add-PSSnapin SqlServerCmdletSnapin100   
Add-PSSnapin SqlServerProviderSnapin100

$SERVER_NAME="<SERVER NAME>"; $DB_NAME="<DB NAME>";$Tables = invoke-sqlcmd -query "SELECT name FROM sys.tables order by name" -database $DB_NAME -serverinstance $SERVER_NAME;foreach ($Table in $Tables){$TableName = $Table["name"];write-host -ForegroundColor Green "Creating File $TableName.csv";invoke-sqlcmd -query "SELECT * FROM $TableName" -database $DB_NAME -serverinstance $SERVER_NAME |export-csv -path D:\dump\$TableName.csv;}

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# ~/.profile: executed by the command interpreter for login shells.
# This file is not read by bash(1), if ~/.bash_profile or ~/.bash_login
# exists.
# see /usr/share/doc/bash/examples/startup-files for examples.
# the files are located in the bash-doc package.

# the default umask is set in /etc/profile; for setting the umask
# for ssh logins, install and configure the libpam-umask package.
#umask 022

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"""Benchmark multiple method of removing duplicates in a list 
    while keeping item's order in the list.

    numpy.unique will likely be faster and more efficient
    the more the list grows.
    If a list is submitted to numpy.unique, it will be transformed and flattened
    into a numpy.Array which it's data structure is more efficient than standard
    lists since all elements have the same size in memory therefor requires less
    access to the memory. Lists are stored as array of adresses pointing to 
    to the objects and requires more memory access (but more flexible).

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cd /usr/local/share
wget http://phantomjs.googlecode.com/files/phantomjs-1.9.7-linux-x86_64.tar.bz2
tar xjf phantomjs-1.9.7-linux-x86_64.tar.bz2
sudo ln -s /usr/local/share/phantomjs-1.9.7-linux-x86_64/bin/phantomjs /usr/local/share/phantomjs
sudo ln -s /usr/local/share/phantomjs-1.9.7-linux-x86_64/bin/phantomjs /usr/local/bin/phantomjs
sudo ln -s /usr/local/share/phantomjs-1.9.7-linux-x86_64/bin/phantomjs /usr/bin/phantomjs

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mkdir lib
cd lib

wget http://sourceforge.net/projects/pyqt/files/PyQt4/PyQt-4.10.4/PyQt-x11-gpl-4.10.4.tar.gz
tar xvf PyQt-x11-gpl-4.10.4.tar.gz
cd PyQt-x11-gpl-4.10.4/
python configure.py
make && make install