####Entity resolution is a common, yet difficult problem in data cleaning and integration. This lab will demonstrate how we can use Apache Spark to apply powerful and scalable text analysis techniques and perform entity resolution across two datasets of commercial products.
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[alias] | |
st = status -s | |
ls = log --pretty=format:"%C(yellow)%h%Cred%d\\ %Creset%s%Cblue\\ [%cn]" --decorate | |
ll = log --pretty=format:"%C(yellow)%h%Cred%d\\ %Creset%s%Cblue\\ [%cn]" --decorate --numstat | |
lds = log --pretty=format:"%C(yellow)%h\\ %ad%Cred%d\\ %Creset%s%Cblue\\ [%cn]" --decorate --date=short | |
le = log --oneline --decorate |
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git config --global alias.lo 'log --date=format:"%Y-%m-%d %H:%M" --pretty=format:"%ad %C(yellow)%h%Creset %an: %Cblue%s%Creset"' |
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# |
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find . -maxdepth 1 -type d -print0 | xargs -0 -I {} sh -c 'echo $(find "{}" | wc -l) \\t "{}"' | sort -rn | less |
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if [ -x /usr/bin/tput ] && tput setaf 1 >& /dev/null; then | |
c_git_clean=$(tput setaf 2) | |
c_git_dirty=$(tput setaf 1) | |
c_git_semi_dirty=$(tput setaf 3) | |
c_reset=$(tput sgr0) | |
else | |
c_git_clean= | |
c_git_dirty= | |
c_reset= | |
c_git_semi_dirty= |
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a = | |
name: 'n1' | |
items: [ | |
{ | |
name: 'n1.1' | |
items: [ | |
{name: 'n1.1.1', items: []} | |
{ | |
name: 'n1.1.2', items: [ | |
{ name: 'n1.1.2.1', items: []} |
# Terminal: iTerm 2