Here is a list of top players in Big Data world having influence over billion dollars (or more) Big Data projects directly or indirectly (not in order):
Microsoft
Google
Amazon
IBM
HP
Oracle
VMWare
Terradata
EMC
Facebook
GE
Intel
Cloudera
SAS
10Gen
SAP
Hortonworks
MapR
Palantir
Splunk
The list is based on each above companies involvement in Big data directly or indirectly along with a direct product or not. All of above companies are involved in Big Data projects worth considering Billion+ …
Compiling Hadoop branch 1.2.x code in OSX returned exception as below:
create-native-configure:
BUILD FAILED /Users/avkash/work/hadoop/branch-1.2/build.xml:634: Execute failed: java.io.IOException: Cannot run program “autoreconf” (in directory “/Users/avkash/work/hadoop/branch-1.2/src/native”): error=2, No such file or directory
at java.lang.ProcessBuilder.processException(ProcessBuilder.java:478)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:457)
at java.lang.Runtime.exec(Runtime.java:593)
at org.apache.tools.ant.taskdefs.Execute$Java13CommandLauncher.exec(Execute.java:862)
at org.apache.tools.ant.taskdefs.Execute.launch(Execute.java:481)
at org.apache.tools.ant.taskdefs.Execute.execute(Execute.java:495)
at org.apache.tools.ant.taskdefs.ExecTask.runExecute(ExecTask.java:631)
at org.apache.tools.ant.taskdefs.ExecTask.runExec(ExecTask.java:672)
at org.apache.tools.ant.taskdefs.ExecTask.execute(ExecTask.java:498)
at org.apache.tools.ant.UnknownElement.execute(UnknownElement.java:291)
at sun.reflect.GeneratedMethodAccessor4.invoke(Unknown Source)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
at java.lang.reflect.Method.invoke(Method.java:597)
at org.apache.tools.ant.dispatch.DispatchUtils.execute(DispatchUtils.java:106)
at org.apache.tools.ant.Task.perform(Task.java:348)
at org.apache.tools.ant.Target.execute(Target.java:390)
at org.apache.tools.ant.Target.performTasks(Target.java:411)
at org.apache.tools.ant.Project.executeSortedTargets(Project.java:1399)
at org.apache.tools.ant.Project.executeTarget(Project.java:1368)
at org.apache.tools.ant.helper.DefaultExecutor.executeTargets(DefaultExecutor.java:41)
at org.apache.tools.ant.Project.executeTargets(Project.java:1251)
at org.apache.tools.ant.Main.runBuild(Main.java:809)
at org.apache.tools.ant.Main.startAnt(Main.java:217)
at org.apache.tools.ant.launch.Launcher.run(Launcher.java:280)
at org.apache.tools.ant.launch.Launcher.main(Launcher.java:109)
Caused by: java.io.IOException: error=2, No such file or directory
at java.lang.UNIXProcess.forkAndExec(Native Method)
at java.lang.UNIXProcess.<init>(UNIXProcess.java:53)
at java.lang.ProcessImpl.start(ProcessImpl.java:91)
at java.lang.ProcessBuilder.start(ProcessBuilder.java:452)
… 23 more
Looking src/native there is no “autoconf” shipped with the source so to solve this problem the best option is to install
└─[1] <git:(master✗✈)> brew install automake
==> Installing automake dependency: autoconf
==> Downloading http://ftpmirror.gnu.org/autoconf/autoconf-2.69.tar.gz
######################################################################## 100.0%
==> ./configure –prefix=/usr/local/Cellar/autoconf/2.69
==> make install
🍺 /usr/local/Cellar/autoconf/2.69: 69 files, 2.0M, built in 21 seconds
==> Installing automake
==> Downloading http://ftpmirror.gnu.org/automake/automake-1.14.tar.gz
######################################################################## 100.0%
==> ./configure –prefix=/usr/local/Cellar/automake/1.14
==> make install
🍺 /usr/local/Cellar/automake/1.14: 127 files, 2.5M, built in 7 seconds
Thats all!!
After than just run the following commands to make sure the same exception does not come back:
I wanted to have a fancy looking and very useful terminal windows with customize command prompt so after digging I build something as below for me:
So what it have:
Line Separator including current time at the end of the terminal
History counter along with current command counter
Logged user @ Hostname
Current working folder $
Here is what I have done. First created a file call .avkashbash_profile at my $HOME folder as below:
fill=”-“ reset_style='[33[00m]’ status_style=$reset_style'[33[0;32m]’ # gray color; use 0;37m for lighter color prompt_style=$reset_style command_style=$reset_style'[33[0;32m]’ # bold black # Prompt variable: PS1=”$status_style”‘$fill[T]n'”$prompt_style”‘${debian_chroot:+($debian_chroot)}e[0;31me[47m[!:#]e[ 0m-e[1;33m[u@h]e[0mn[w]e[0;32m$ ‘”$command_style “ # Reset color for command output # (this one is invoked every time before a command is executed): function prompt_command { # create a $fill of all screen width minus the time string and a space: let fillsize=${COLUMNS}-11 fill=”” while [ “$fillsize” -gt “0” ] do fill=”-${fill}” # fill with underscores to work on let fillsize=${fillsize}-1 done } PROMPT_COMMAND=prompt_command
To make the setting permanent, just add the following code in .bash_profile first:
if [ -f “$HOME/.avkashbash_ps1” ]; then . “$HOME/.avkashbash_ps1” fi
And then run the following command to set it:
$sources .bash_profile
Or if you don’t want to make it permanent, just add the following code to .bashrc first:
if [ -f “$HOME/.avkashbash_ps1” ]; then . “$HOME/.avkashbash_ps1” fi
MachineHead:docs avkash$ ruby -e “$(curl -fsSL https://raw.github.com/mxcl/homebrew/go)”
==> This script will install:
/usr/local/bin/brew
/usr/local/Library/…
/usr/local/share/man/man1/brew.1
==> The following directories will be made group writable:
/usr/local/.
/usr/local/bin
/usr/local/etc
/usr/local/lib
/usr/local/share
/usr/local/share/man
/usr/local/share/man/man1
/usr/local/share/info
==> The following directories will have their group set to admin:
/usr/local/.
/usr/local/bin
/usr/local/etc
/usr/local/lib
/usr/local/share
/usr/local/share/man
/usr/local/share/man/man1
/usr/local/share/info
Press ENTER to continue or any other key to abort
==> /usr/bin/sudo /bin/chmod g+rwx /usr/local/. /usr/local/bin /usr/local/etc /usr/local/lib /usr/local/share /usr/local/share/man /usr/local/share/man/man1 /usr/local/share/info
Password:
==> /usr/bin/sudo /usr/bin/chgrp admin /usr/local/. /usr/local/bin /usr/local/etc /usr/local/lib /usr/local/share /usr/local/share/man /usr/local/share/man/man1 /usr/local/share/info
==> Downloading and Installing Homebrew…
remote: Counting objects: 121792, done.
remote: Compressing objects: 100% (59383/59383), done.
remote: Total 121792 (delta 85372), reused 95223 (delta 61439)
Receiving objects: 100% (121792/121792), 19.29 MiB | 283 KiB/s, done.
Resolving deltas: 100% (85372/85372), done.
From https://github.com/mxcl/homebrew
* [new branch] master -> origin/master
HEAD is now at c45c77d audit: don’t complain about bottle versions.
==> Installation successful!
You should run `brew doctor’ *before* you install anything.
Now type: brew help
Step 2:
MachineHead:docs avkash$ brew doctor
Your system is ready to brew.
Step 3:
MachineHead:docs avkash$ brew help
Example usage:
brew [info | home | options ] [FORMULA…]
brew install FORMULA…
brew uninstall FORMULA…
brew search [foo]
brew list [FORMULA…]
brew update
brew upgrade [FORMULA…]
Troubleshooting:
brew doctor
brew install -vd FORMULA
brew [–env | –config]
Continuuity decided to build Weave and be part of the journey to take Apache YARN to the next level of usability and functionality. Continuuity has been using Weave extensively to support their products and seen the benefit and power of Apache YARN and Weave combined. Continuuity decided to share Weave under the Apache 2.0 license in an effort to collaborate with members of the community, broaden the set of applications and patterns that Weave supports, and further the overall adoption of Apache YARN.
Weave is NOT a replacement for Apache YARN. It is instead a value-added framework that operates on top of Apache YARN.
What is Weave: Weave is a simple set of libraries that allows you to easily manage distributed applications through an abstraction layer built on Apache YARN. Weave allows you to use YARN’s distributed capabilities with a programming model that is similar to running threads.
Features of Weave:
– Simple API for specifying, running and managing application lifecycle
– An easy way to communicate with an application or parts of an application
– A generic Application Master to better support simple applications
– Simplified archive management and local file transport
– Improved control over application logs, metrics and errors
– Discovery service
– And many more…
As you can see above there is a warning message even when the myblog does have all the lines in it. To disable this warning message we can use “warn=FALSE” as below:
After I read all the lines in my blog, lets perform some specific search operation in the content:
Searching all lines with Hadoop or hadoop in it:
To search all the lines which have Hadoop or hadoop in it we can run grep command to find all the line numbers as below:
> hd <- grep(“[hH]adoop”, myblog)
Lets print hd to see all the line numbers: > hd
[1] 706 803 804 807 811 812 814 819 822 823 826 827 830 834
[15] 837 863 869 871 872 875 899 911 912 921 923 925 927 931
[29] 934 1000 1010 1011 1080 1278 1281
To print all the lines with Hadoop or hadoop in it we can just use:
> myblog[hd] [1] “<p>A: ACID – Atomicity, Consistency, Isolation and Durability <br />B: Big Data – Volume, Velocity, Variety <br />C: Columnar (or Column-Oriented) Database <br />D: Data Warehousing – Relevant and very useful <br />E: ETL – Extract, transform and load <br />F: Flume – A framework for populating Hadoop with data <br />G: Geospatial Analysis – A picture worth 1,000 words or more <br />H: Hadoop, HDFS, HBASE – Do you really want to know? <br />I: In-Memory Database – A new definition of superfast access <br />J: Java – Hadoop gave biggest push in last years to stay in enterprise market <br />K: Kafka – High-throughput, distributed messaging system originally developed at LinkedIn <br />L: Latency – Low Latency and High Latency <br />M: Map/Reduce – MapReduce <br />N: NoSQL Databases – No SQL Database or Not Only SQL <br />O: Oozie – Open-source workflow engine managing Hadoop job processing <br />P: Pig – Platform for analyzing huge data sets <br />Q: Quantitative Data Analysis <br />R: Relational Database – Still relevant and will be for some time <br />S: Sharding (Database Partitioning) and Sqoop (SQL Database to Hadoop) <br />T: Text Analysis – Larger the information, more needed analysis <br />U: Unstructured Data – Growing faster than speed of thoughts <br />V: Visualization – Important to keep the information relevant <br />W: Whirr – Big Data Cloud Services i.e. Hadoop distributions by cloud vendors <br />X: XML – Still eXtensible and no Introduction needed <br />Y: Yottabyte – Equal to 1,000 exabytes, 1 million petabytes and 1 billion terabytes <br />Z: Zookeeper – Help managing Hadoop nodes across a distributed network </p>” [2] “ttt
[34] “PDRTJS_settings_5386869_post_412={“id”:5386869,”unique_id”:”wp-post-412″,”title”:”Merging%20two%20data%20set%20in%20R%20based%20on%20one%20common%26nbsp%3Bcolumn”,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/30\/merging-two-data-set-in-r-based-on-one-common-column\/”,”item_id”:”_post_412″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_412 == ‘undefined’ ){PDRTJS_5386869_post_412 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_412 );}}PDRTJS_settings_5386869_post_409={“id”:5386869,”unique_id”:”wp-post-409″,”title”:”Working%20with%20dataset%20in%20R%20and%20using%20subset%20to%20work%20on%26nbsp%3Bdataset”,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/30\/working-with-dataset-in-r-and-using-subset-to-work-on-dataset\/”,”item_id”:”_post_409″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_409 == ‘undefined’ ){PDRTJS_5386869_post_409 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_409 );}}PDRTJS_settings_5386869_post_398={“id”:5386869,”unique_id”:”wp-post-398″,”title”:”Listing%20base%20datasets%20in%20R%20and%20loading%20as%20Data%26nbsp%3BFrame”,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/19\/listing-base-datasets-in-r-and-loading-as-data-frame\/”,”item_id”:”_post_398″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_398 == ‘undefined’ ){PDRTJS_5386869_post_398 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_398 );}}PDRTJS_settings_5386869_post_397={“id”:5386869,”unique_id”:”wp-post-397″,”title”:”ABC%20of%20Data%26nbsp%3BScience”,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/01\/abc-of-data-science\/”,”item_id”:”_post_397″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_397 == ‘undefined’ ){PDRTJS_5386869_post_397 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_397 );}}PDRTJS_settings_5386869_post_390={“id”:5386869,”unique_id”:”wp-post-390″,”title”:”R%20Programming%20Language%20%28Installation%20and%20configuration%20on%26nbsp%3BWindows%29″,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2012\/12\/18\/r-programming-language-installation-and-configuration-on-windows\/”,”item_id”:”_post_390″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_390 == ‘undefined’ ){PDRTJS_5386869_post_390 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_390 );}}PDRTJS_settings_5386869_post_382={“id”:5386869,”unique_id”:”wp-post-382″,”title”:”How%20Hadoop%20is%20shaping%20up%20at%20Disney%26nbsp%3BWorld%3F”,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2012\/11\/13\/how-hadoop-is-shaping-up-at-disney-world\/”,”item_id”:”_post_382″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_382 == ‘undefined’ ){PDRTJS_5386869_post_382 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_382 );}}PDRTJS_settings_5386869_post_376={“id”:5386869,”unique_id”:”wp-post-376″,”title”:”Hadoop%20Adventures%20with%20Microsoft%26nbsp%3BHDInsight”,”permalink”:”http:\/\/cloudcelebrity.wordpress.com\/2012\/11\/03\/hadoop-adventures-with-microsoft-hdinsight\/”,”item_id”:”_post_376″}; if ( typeof PDRTJS_RATING !== ‘undefined’ ){if ( typeof PDRTJS_5386869_post_376 == ‘undefined’ ){PDRTJS_5386869_post_376 = new PDRTJS_RATING( PDRTJS_settings_5386869_post_376 );}}” [35] “ttWPCOM_sharing_counts = {“http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/30\/merging-two-data-set-in-r-based-on-one-common-column\/”:412,”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/30\/working-with-dataset-in-r-and-using-subset-to-work-on-dataset\/”:409,”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/19\/listing-base-datasets-in-r-and-loading-as-data-frame\/”:398,”http:\/\/cloudcelebrity.wordpress.com\/2013\/01\/01\/abc-of-data-science\/”:397,”http:\/\/cloudcelebrity.wordpress.com\/2012\/12\/18\/r-programming-language-installation-and-configuration-on-windows\/”:390,”http:\/\/cloudcelebrity.wordpress.com\/2012\/11\/13\/how-hadoop-is-shaping-up-at-disney-world\/”:382,”http:\/\/cloudcelebrity.wordpress.com\/2012\/11\/03\/hadoop-adventures-with-microsoft-hdinsight\/”:376}t</script>”
Above I have just removed the lines in middle to show the result snippet.
In the above If I try to collect lines between 553 .. 648, there is list of all dataset in R, so to collect I can do the following:
> myLines <- myblog[553:648] > summary(myLines)
Length Class Mode
96 character character
Note: Above mylines character list has total 110 lines so you can try printing and see what you get.
Create a list of available dataset from above myLines vector:
The pattern in myLines is as below:
[1] “AirPassengers Monthly Airline Passenger Numbers 1949-1960”
[2] “BJsales Sales Data with Leading Indicator”
[3] “BOD Biochemical Oxygen Demand”
[4] “CO2 Carbon Dioxide Uptake in Grass Plants”
……….
………
[92] “treering Yearly Treering Data, -6000-1979”
[93] “trees Girth, Height and Volume for Black Cherry Trees”
[94] “uspop Populations Recorded by the US Census”
[95] “volcano Topographic Information on Auckland’s Maunga”
[96] ” Whau Volcano”
so the first word is dataset name and after the space is the dataset information. To get the dataset name only lets use subfunction as below:
mpg cyl disp hp drat
Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0 Min. :2.760
1st Qu.:15.43 1st Qu.:4.000 1st Qu.:120.8 1st Qu.: 96.5 1st Qu.:3.080
Median :19.20 Median :6.000 Median :196.3 Median :123.0 Median :3.695
Mean :20.09 Mean :6.188 Mean :230.7 Mean :146.7 Mean :3.597
3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0 3rd Qu.:3.920
Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0 Max. :4.930
wt qsec vs am
Min. :1.513 Min. :14.50 Min. :0.0000 Min. :0.0000
1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000 1st Qu.:0.0000
Median :3.325 Median :17.71 Median :0.0000 Median :0.0000
Mean :3.217 Mean :17.85 Mean :0.4375 Mean :0.4062
3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000 3rd Qu.:1.0000
Max. :5.424 Max. :22.90 Max. :1.0000 Max. :1.0000
gear carb
Min. :3.000 Min. :1.000
1st Qu.:3.000 1st Qu.:2.000
Median :4.000 Median :2.000
Mean :3.688 Mean :2.812
3rd Qu.:4.000 3rd Qu.:4.000
Max. :5.000 Max. :8.000
As you can see above the summary give you the mean, media, max and other values for each column.
Using Subset with Dataset in R
How to list data frame with a few columns? Sure we will be using subset() for this:
Subset(dataframe, condition, format)
For example the following subset will show all the rows in mtcrs but only mpg column:
> subset(mtcars, select = c(mpg))
mpg
Mazda RX4 21.0
Mazda RX4 Wag 21.0
Datsun 710 22.8
Hornet 4 Drive 21.4
Hornet Sportabout 18.7
Valiant 18.1
Duster 360 14.3
Merc 240D 24.4
Merc 230 22.8
Merc 280 19.2
Merc 280C 17.8
Merc 450SE 16.4
Merc 450SL 17.3
Merc 450SLC 15.2
Cadillac Fleetwood 10.4
Lincoln Continental 10.4
Chrysler Imperial 14.7
Fiat 128 32.4
Honda Civic 30.4
Toyota Corolla 33.9
Toyota Corona 21.5
Dodge Challenger 15.5
AMC Javelin 15.2
Camaro Z28 13.3
Pontiac Firebird 19.2
Fiat X1-9 27.3
Porsche 914-2 26.0
Lotus Europa 30.4
Ford Pantera L 15.8
Ferrari Dino 19.7
Maserati Bora 15.0
Volvo 142E 21.4
Now what if we want to see the list of cars which have mpg above 20 along with mpg and hp column:
For example we need to get a list of cars with HP above 100 and mpg above 20.
> subset(subset(mtcars, mpg > 20, select = c(mpg, hp)),hp >= 100)
mpg hp
Mazda RX4 21.0 110
Mazda RX4 Wag 21.0 110
Hornet 4 Drive 21.4 110
Lotus Europa 30.4 113
Volvo 142E 21.4 109
Note: following will not work as condition will not match and you will see a full list of cars with mpg and hp column
> subset(mtcars, mpg > 20 && hp >= 100, select = c(mpg, hp))
You can also use subset to show specific columns
> subset(mtcars[1:3])
mpg cyl disp
Mazda RX4 21.0 6 160.0
Mazda RX4 Wag 21.0 6 160.0
Datsun 710 22.8 4 108.0
Hornet 4 Drive 21.4 6 258.0
Hornet Sportabout 18.7 8 360.0
Everything Artificial Intelligence 