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Introductions
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Twitter @lgleasain
Github lgleasain
www.lancegleason.com
www.polyglotprogrammincinc.com
[email protected]
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http://www.purrprogramming.com
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What Are Analytics?
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Data Science
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Gathering Data
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Database
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Database
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Database
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Database
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Logging
(Papertrail/
Loggly)
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Logging
(Papertrail/
Loggly)
Amazon S3
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{"measure":"instance","instance":
"stores","store_id":
64696,"company_id":
210,"store_name":"bebe",
"controller":"api/v1/
stores","action":"index"}
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Amazon Elastic
Map Reduce
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Amazon Elastic
Map Reduce
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DynamoDB
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CREATE EXTERNAL TABLE events_1 (
id bigint, received_at string, generated_at string, source_id
bigint, source_name string, source_ip string, facility string,
severity string, program string, message string
)
PARTITIONED BY (
dt string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY '\t'
STORED AS TEXTFILE
LOCATION 's3://mybucket/papertrail/logs/production';
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ALTER TABLE events_1 RECOVER PARTITIONS;
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CREATE EXTERNAL TABLE promotions_1 (id string,
received_at string, source_id string, source_ip string,
source_name string,measure string, instance string,
promotion_id string, company_id string, controller string,
action string)
stored by
'org.apache.hadoop.hive.dynamodb.DynamoDBStorageHan
dler'
TBLPROPERTIES ("dynamodb.table.name" =
"sh_promotions_latest",
"dynamodb.column.mapping" =
"id:id,received_at:received_at,source_id:source_id,source_i
p:source_ip,source_name:source_name,measure:measure,i
nstance:instance,promotion_id:promotion_id,company_id:c
ompany_id,controller:controller,action:action");
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alter table promotions_1 recover partitions;
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insert overwrite table promotions_1 select id, received_at,
source_id, source_ip, source_name,
get_json_object(message, '$.measure') as measure,
get_json_object(message, '$.instance') as instance,
get_json_object(message, '$.promotion_id') as
promotion_id, get_json_object(message, '$.company_id') as
company_id, get_json_object(message, '$.controller') as
controller, get_json_object(message, '$.action') as action
from events_1 where message like '%"promotion"%' ;
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Hadoop
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Cassandra
MongoDB
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Cleaning Data
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Segmentation
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Sparse Data
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Analysis
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Descriptive Statistics
Stats Sample
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Visualization
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To Get Statistically
Meaningful Results you
will need thousands of
data points
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False Positives
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Nearly ALL sick people have
eaten Rice (obviously then,
the effects
are cumulative).
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An estimated 99.9% of all
people who die from cancer
or heart attacks
have eaten Rice.
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Another 99.9% of people
involved in auto accidents ate
Rice within
60-days before the accident.
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Among people born in 1839
who later dined on Rice, there
has been a 100% mortality
rate
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Rice Will Kill You
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We had 4000 app downloads
this month. We are doing
great....
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Most people use the app once
and then uninstall it.
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My shopping app just saw a
spike in weekly usage after I
made UI changes.
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That UI change led to more
users!
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The change went live during
the last week of November.
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Be Wary of N of 1
Experiments
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The Results
Need to Pass
the Smell Test
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http://www.kaggle.com/c/titanic-gettingStarted
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What Are Analytics?
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Visualization
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Sparse Data
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Insights
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Trends
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SVG
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Rubyvis
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require 'rubygems'
require 'rubyvis'
vis = Rubyvis::Panel.new do
width 150
height 150
bar do
data [1, 1.2, 1.7, 1.5, 0.7, 0.3]
width 20
height {|d| d * 80}
bottom(0)
left {index * 25}
end
end
vis.render()
puts vis.to_svg # Output final SVG
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NVD3
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XCharts
C3.js
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D3JS
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SVG
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SVG
Canvas
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•Internet Explorer 9 and 10+
•Chrome 24, 25, and 26+
•Safari 5 and 6+
•Firefox 19, 20, and 21+
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Rubyvis for a
pure Ruby
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NVD3, C3 or
XCharts For
Easy Stuff
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D3JS for loads of
flexibility
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Limit Your
Dataset for
performance
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Don’t
overwhelm with
too much
information
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Be Wary of N of 1
Experiments
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Twitter @lgleasain
Github lgleasain
www.lancegleason.com
www.polyglotprogrammincinc.com
[email protected]
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