Data science lab enabling flexibility

The Data Science Lab:
Enabling Flexible,
Complex Analytics
on a Single Platform
@Kognitio
#DataSci
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- Web Briefing -
Enabling Flexible, Complex Analytics
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@Kognitio #DataSci@Kognitio #DataSci
on a single platform
The Data Science Lab: Enabling Flexibility
Demonstrations
Summary, Question & Answer Session
Presenters:
‐ Dr. Sharon Kirkham, Data Scientist
‐ Michael Hiskey, Product Evangelist
Web Briefing
The Data Science Lab
@Kognitio
#DataSci
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on Twitter:
3

on a single platform
July 25, 2013
1. Data Accessibility
• Hadoop
• Data Mash‐Up
2. Analytical Productivity
• MPP in‐memory code execution
• R scripts with MPP
3. “Graduate” Projects to B.A.U.
• Data Science and the Business
Use Case Scenarios:
The Data Science Lab
POLL

Flexible Platform for Big Data Analytics
Flexible data
access
Flexible
processing
Flexible
deployment
options
Near-line
Storage
(optional)
All BI Tools All OLAP Clients Excel
Hadoop
Clusters
Enterprise Data
Warehouses
Legacy
Systems
Kognitio
Storage
Reporting
Cloud
Storage
Analytical
Platform
Layer
5

Mature Business Intelligence & Reporting
Numbers, tables, charts, indicators
…accessed with ease and simplicity
Historical information, latency
BI tools have plateaued
Decision Support
Advanced analytics and data science
More math…a lot more math
6

The Analytical Enterprise
Business
Analyst
Systems
Admin
Data
Scientist
Sexiest job of the 21st Century?
Key: “Graduation”
• Projects will need to easily Graduate
from the Data Science Lab and
become part of Business as Usual
7

Telling a story with data
Build, tune and run
complex data projects
Dealing with big data
from multiple sources
Must overcome IT
bottlenecks
Source: http://www.emc.com/microsites/bigdata/infographic.htm
Data scientists are
in demand:
8

Scenario 1: Data Accessibility
”… this exercise is to identify if
improvements in data preparation can
make a significant difference to the
productivity and earning capacity of our
analytics team”
- Global Digital marketing analytics firm
source: http://newvantage.com/wp-content/uploads/2012/12/NVP-Big-Data-Survey-Themes-Trends.pdf
POLL

SQL querying on
Hadoop
Scenario 1: Data Accessibility

Summary: Data Accessibility
Kognitio Hadoop Integration
• Map/Reduce agent dynamically executes on
all Hadoop nodes
• Query passes selections, relevant predicates
to the agents
• Data filtering & projection locally on each node
• Data filtered as it is read from file(s)
• Only data of interest is transferred and loaded
into memory via parallel load streams
Hadoop
Clusters
Enterprise Data
Warehouses
Legacy
Systems
Kognitio
Storage
Reporting
Cloud
Storage
11

Scenario 2: Analytical Productivity
“…want to see a significant
improvement in the analytical
throughput … from current
time frame of 2 weeks … to
no more than 1 day”
- A marketing science analytics company
“…we run much of our analytics
on a 5% sample of the data. We
want to be able to run on 100%
of the data in the same time as
the 5% sample.”
- A leading Ad Agency
Source: http://www.wired.com/insights/2013/07/the-new-horizon-for-bi-and-analytics/
POLL
12

Massively parallel in-
memory code execution
Scenario 2: Analytical Productivity

MPP in-memory code execution
NoSQL external scripting function:
• SQL provides standard data access framework
– Open, adaptable framework; pass data to/from any
executable or interpreter
– Fully flexible MPP execution of R, Python, Java, text
parsing libraries etc.
create interpreter perlinterp
command '/usr/bin/perl' sends 'csv' receives 'csv' ;
select top 1000 words, count(*)
from (external script using environment perlinterp
receives (txt varchar(32000))
sends (words varchar(100))
script S'endofperl(
while(<>)
{
chomp();
s/[,.!_]//g;
foreach $c (split(/ /))
{ if($c =~ /^[a-zA-Z]+$/) { print "$cn”} }
}
)endofperl'
from (select comments from customer_enquiry))dt
group by 1
order by 2 desc;
From the Demo:
This reads long comments text from
customer enquiry table, in line Perl
converts long text into output stream
of words (one word per row), query
selects top 1000 words by frequency
using standard SQL aggregation

Accessing Analytics
across the business
Scenario #3: Barriers to Deployment

An Ideal Deployment Scenario
Cloud model can provide a way to quickly
model, experiment, develop and build
• Deploy to existing reporting tools
• Pass ownership to IT
• Cloud instances can be “temporary”
• Repeatable framework
2011 2010 Sep.3
Aug. Jul. Sep. Aug.
3,443,873 8.1 382,009 401,951 391,878 351,696 369,199
617,194 10.4 67,055 71,725 69,801 61,676 66,085
65,237 1.0 7,671 7,892 7,422 7,357 7,611
70,324 0.0 7,737 8,240 7,888 7,685 8,082
226,261 5.8 24,764 26,196 25,973 23,288 23,722
455,276 5.6 50,418 52,164 53,062 47,710 48,597
446,918 3.5 48,368 51,797 51,160 46,166 49,848
88,590 8.7 10,510 10,681 10,258 9,591 9,514
279,985 13.2 31,390 31,889 28,478 28,266 28,282
368,372 5.5 41,188 42,244 43,097 37,992 40,228
Not Adjusted
9 Month Total 2011 2010
*
Business
Analyst
Business
User
IT Admin
Data
Scientist
PRESS
HERE
PRESS
HERE…and really cool Big Data stuff happens!
16

It’s all about flexibility
Flexible data
access
Flexible
processing
Flexible
deployment
options
Near-line
Storage
(optional)
All BI Tools All OLAP Clients Excel
Hadoop
Clusters
Enterprise Data
Warehouses
Legacy
Systems
Kognitio
Storage
Reporting
Cloud
Storage
17

Question & Answer session will be conducted electronically,
using the panel to the right of your screen
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