CC 2.0 by Per Olesen | http://flic.kr/p/7pVCgZ

CC 2.0 by Franck BLAIS | http://flic.kr/p/cwVnSy

CC 2.0 by John Steven Fernandez | http://flic.kr/p/a8uTzz

CC 2.0 by Ian Carroll | http://flic.kr/p/6NWoGm

CC 2.0 by Perry French | http://flic.kr/p/8wDMJS

CC 2.0 by John Mitchell | http://flic.kr/p/5UaPg8

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How do we answer these questions?

Before we started designing a blueprint solution we first of all asked ourselves:

1  Who would be asked to answer questions like this?

2  Who is this person? 3  What tools does this person expect to

use? 4  And what is a typical skill set of this

person? 5  How do they work?

Preparation

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So, how do we answer these questions as a Data Scientist?

From a high level of abstraction the answer is simple. We need a data management system with three pieces: ingest, store and process.

Traditional Data Management System Approach

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2013

Data Source

Data Ingestion

Data Processing

Data Storage

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So, how do we answer these questions as a Data Scientist?

We take this basis architecture and replace the generic terms while mapping it onto the Hadoop ecosystem. With this Hadoop architecture a Data Scientist should be able to answer the questions without any programming environment. He/she can also use familiar BI, analysis and reporting tools as well.

Blueprint for a Data Management System with Hadoop

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2013

Data Source Flume

HIVE, Impala HDFS

BI/Analysis/Reporting

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Ingrediants

1  2 WiFi access points to simulate two different stores with OpenWRT, a linux based firmware for routers, installed

2  Flume to move all log messages to HDFS, without any manual intervention (no transformation, no filtering)

3  A 4 node CDH4 cluster 4  Pentaho Data Integration‘s graphical designer for data

transformation, parsing, filtering and loading to the warehouse

5  Hive as data warehouse system on top of Hadoop to project structure onto data

6  Impala for querying data from Hive in real time 7  Tool to visualize results

Setup

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2013

CC 2.0 by Qi Wei Fong | http://flic.kr/p/7w8vfq

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Visits for stores number one & two

The plot indicates that about 85% of the visits were detected in store number one and about 15% in store number two. One might draw the conclusion that store number one is in a much better location with more occasional customers.

But let’s gain more insights by analysing the number of unique visitors.

Analysis Result

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2013

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Unique visitors

This plot gives us more details about the customers. It turns out that the 135 visits in store number one were caused by just 9 unique visitors while store number two encountered 5 unique visitors.

Analysis Result

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2013

14 This plot indicates that we have more returning than new users in both stores. In store number two we didn’t see a new user over the past 4 days at all. It’s probably a good idea to start a marketing campaign which aims at new customers, e.g. to give out vouchers for the first purchase.

New vs. returning users

Analysis Result

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2013

15 The plot for the last 4 days vividly visualizes that the visit duration in store number one was evenly distributed while the distribution in store number two shows some peaks. We can also see that visitors tend to stay in shop number one much longer.

Visit duration over the past 4 days

Analysis Result

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2013

16 There is a lot of useful information that can be derived from this plot. 1.  There is a repeating pattern of step-ins and step-outs

within a short period of time. 2.  There was a step-out of store number one and a step-in

into store number two within just 28 seconds.

Avg. Duration Between Visits of one particular user

Analysis Result

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2013

März  8,  2013  

CC 2.0 by Aurelien Guichard | http://flic.kr/p/cjg9yw

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Links

1  Presentation, Video and Post Series •  http://bit.ly/YgtIMK

2  http://sentric.ch 3  http://www.bigdata-usergroup.ch 4  http://about.me/jpkoenig

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2013