The Briefing Room with Robin Bloor and Cirro Live Webcast on Dec. 11, 2012 As the information landscape expands with all kinds of Big Data, businesses are searching for ways to unite their traditional analytics with this new source of insight. One ambitious approach involves federating access to multiple data sources, even across various operating systems. The idea is to take analytic processing to the data, then intelligently assemble the results for a business user. Could this be the long-awaited alternative to data virtualization? Check out this episode of The Briefing Room to hear veteran Analyst Robin Bloor explain how federated access to data sources can pave the way for a truly integrated data fabric. Bloor will be briefed by Mark Theissen of Cirro, who will tout his company's patent-pending Data Hub, which simplifies data access by federating queries across multiple sources of structured, semi-structured, and unstructured data. He'll discuss Cirro's cost based optimizer, smart caching, dynamic query plan re-optimization, normalization of cost estimates and a metadata repository for unstructured data sources. Visit: http://www.insideanalysis.com

1. The Briefing Room

2. Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
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3. Mission
! Reveal the essential characteristics of enterprise software,
good and bad
! Provide a forum for detailed analysis of today s innovative
technologies
! Give vendors a chance to explain their product to savvy
analysts
! Allow audience members to pose serious questions... and get
answers!
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4. December: Innovators
January: Big Data
February: Analytics
March: Data in Motion
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5. Innovators
! Charles Babbage conceived the Analytical Engine in 1834.
! Automation and ease of use have driven innovation in
computing ever since.
! The Cloud and Big Data are raising the bar.
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6. Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
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7. Cirro
! Cirro provides a single method to access any type of data,
on any platform, in any environment.
! Its product suite consists of Cirro Data Hub, Analyst for
Excel and Multi Store – all designed to remove complexity
from Big Data analytics.
! Cirro’s products are cloud based and can run in public,
private and on-premise environments.
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8. Mark Theissen
Mark is CEO at Cirro. He is a respected analytics and data
warehousing expert with more than 22 years in the industry.
Most recently Mark was the worldwide data warehousing
technical lead at Microsoft following the acquisition of
DATAllegro. At DATAllegro Mark was the COO and a member
of the board of directors. Prior to joining DATAllegro, Mark
was Vice President and Research Lead at META Group
(Gartner Group) for Enterprise Analytics Strategies, covering data warehousing,
business intelligence and data integration markets. Before META, Mark was VP
of Professional Services at Accruent where he was responsible for domestic and
overseas services and operations. Mark has a BS in Computer Information
Systems from Chapman University and a MBA from the University of California,
Irvine.
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9. Bringing Big Data to
the Desktop
Corporate Overview
©2012 Cirro Inc. All rights reserved.

10. The Big Data Dilemma
©2012 Cirro Inc. All rights reserved.

11. The Big Data Dilemma
©2012 Cirro Inc. All rights reserved.

12. The Big Data Dilemma
©2012 Cirro Inc. All rights reserved.

13. Accessing Big Data
©2012 Cirro Inc. All rights reserved.

14. Accessing Big Data
Incumbent
Approach
Hadoop
Approach
©2012 Cirro Inc. All rights reserved.

15. Accessing Big Data
Incumbent
Approach
Hadoop
Approach
©2012 Cirro Inc. All rights reserved.

16. Accessing Big Data
Incumbent
Approach
Hadoop
Approach
©2012 Cirro Inc. All rights reserved.

17. What the Market Needs
An enterprise data hub to
access any type of data, on
any platform, in any
environment
©2012 Cirro Inc. All rights reserved.

18. The Enterprise Data Hub
©2012 Cirro Inc. All rights reserved.

19. Simplifying the Access to Your Data
Conven/onal
Approach
Cirro
Approach
People
manage
the
access
to
data
Cirro
Data
Hub
manages
access
to
data
HIVE
Hadoop
Map
Install
&
Reduce
Config
Hive
–
Scoop
Access
tool
Sqoop
Install
&
Config
Source
Java
Control
SQL
Structured
-­‐
Cirro
(mul;ple
versions)
Unstructured
DataBase
Management
Data
Hub
Mashups
©2012 Cirro Inc. All rights reserved.

20. Architecture Overview
Cirro
Data
Hub
• Cost
based
federa;on
op;mizer
• Smart
caching
• Dynamic
op;miza;on
• Normalized
cost
es;mates
• Metadata
for
unstructured
sources
Cirro
Func;on
Library
• Library
of
Func;ons
• Logic
to
build
complex
specific
formulas
Cirro
Analyst
• Excel
plug-­‐in
that
allows
analysts
to
explore
&
process
Big
Data
and
tradi;onal
data
Cirro
Mul;
Store
(op;onal)
• Pre-­‐built
structured/unstructured
data
store
• Used
for
holding
data
or
addi;onal
workspace
©2012 Cirro Inc. All rights reserved.

21. Typical Deployment
Excel Analyst Users Data Consumers
• Design Views Access CDH Views via ODBC
• Minimal IT Support & JDBC across all data types
• Publish Views
• Data Exploration
• Analysis Tableau
Extend, Add Cirro Data Hub
Proprietary • Cirro Function Library Business
Functions to CFL • Proprietary MapReduce Objects
• Custom Views
IT Staff
• Programmers Other BI Tools
• Developers
• DBA’s
MapReduce
HQL
No
SQL
RDBMS
Splunk
Hive
Oracle
Cassandra
MapReduce Teradata
MongoDB
MySQL
SQL
Hadoop Distributed File System Ver;ca
©2012 Cirro Inc. All rights reserved.

22. Sample Use Case
Summarize the number of tweets per hour with
certain keywords from a raw twitter feed.
Requirements:
• Use raw twitter data files in Hadoop
• Keywords stored in SQL table for easy
manipulation
• Results into Tableau Excel for visualization
©2012 Cirro Inc. All rights reserved.

23. Too Many Skills, Coding, Processing
Write
mapper/reducer
in
java
using
development
tool
:
• parse
twi[er
text
-­‐
convert
to
lower
case
-­‐
parse
words
-­‐
exclude
common
words
-­‐
group
words
by
hour
Import
java
classes
into
Hadoop
Execute
command
line
hadoop
using
CLI
• bin/hadoop
jar
Twi[erParse
/home/cloudera/WordCount.jar
/usr/tweet/input
/usr/local/output
–libjars
Move
result
into
HIVE
using
JDBC
SQL
tool
• create
table
output1
(text
STRING,created_at
STRING,count
BIGINT)
ROW
FORMAT
DELIMITED
FIELDS
TERMINATED
BY
't'
STORED
AS
TEXTFILE
• LOAD
DATA
INPATH
'/usr/data/1-­‐88f1-­‐864e22e77801/part*'OVERWRITE
INTO
TABLE
output1
Move
SQL
table
with
keywords
to
HIVE
through
Scoop
using
CLI
• export
-­‐-­‐connect
jdbc:mySQL://10.17.185.44/mytable
-­‐-­‐password
mypasswd
-­‐-­‐username
root
-­‐-­‐table
words
-­‐-­‐export-­‐dir
'/home/cloudera/inpumile
• create
table
mytable
(word
STRING)
ROW
FORMAT
DELIMITED
FIELDS
TERMINATED
BY
','
STORED
AS
TEXTFILE
• LOAD
DATA
INPATH
'/home/cloudera/inpumile/part*'OVERWRITE
INTO
TABLE
mytable
Run
HIVE
query
using
JDBC
SQL
tool
• select
a.text
,a.created_at
,a.count
from
output1
a
join
mytable
b
on
(a.text
=
b.word
)
Import
results
into
Excel
using
Excel
©2012 Cirro Inc. All rights reserved.

24. Too Many Skills, Coding, Processing
Write
mapper/reducer
in
java
using
development
tool
:
• parse
twi[er
text
-­‐
convert
to
lower
case
-­‐
parse
words
-­‐
exclude
common
words
-­‐
group
words
by
hour
Import
java
classes
into
Hadoop
Execute
command
line
hadoop
using
CLI
• bin/hadoop
jar
Twi[erParse
/home/cloudera/WordCount.jar
/usr/tweet/input
/usr/local/output
–libjars
B1=Twi[erParse("/user/twi[er/sample","text,created_at")
Move
result
into
HIVE
using
JDBC
SQL
tool
B2=ToLower(B1,"text")
• create
table
output1
(text
STRING,created_at
STRING,count
BIGINT)
ROW
FORMAT
DELIMITED
FIELDS
TERMINATED
BY
B3=WordSeparate(B2,"text")
't'
STORED
AS
TEXTFILE
• LOAD
DATA
INPATH
'/usr/data/1-­‐88f1-­‐864e22e77801/part*'OVERWRITE
INTO
TABLE
output1
B4=Exclude(B3,"text")
Move
SQL
table
with
keywords
to
HIVE
through
Scoop
using
CLI
B5=GroupBy(B4,"text,created_at")
• export
-­‐-­‐connect
jdbc:mySQL://10.17.185.44/mytable
-­‐-­‐password
mypasswd
-­‐-­‐username
root
-­‐-­‐table
words
-­‐-­‐export-­‐dir
B6=Cirro_Match(B5,"text","MYSQL.KeyWords","word",C9)
'/home/cloudera/inpumile
• create
table
mytable
(word
STRING)
ROW
FORMAT
DELIMITED
FIELDS
TERMINATED
BY
','
STORED
AS
TEXTFILE
Results
displayed
at
cell
C9
• LOAD
DATA
INPATH
'/home/cloudera/inpumile/part*'OVERWRITE
INTO
TABLE
mytable
Run
HIVE
query
using
JDBC
SQL
tool
• select
a.text
,a.created_at
,a.count
from
output1
a
join
mytable
b
on
(a.text
=
b.word
)
Import
results
into
Excel
using
Excel
©2012 Cirro Inc. All rights reserved.

25. Bringing Big Data to
the Desktop
Corporate Overview
©2012 Cirro Inc. All rights reserved.

26. Perceptions & Questions
Analyst:
Robin Bloor
Twitter Tag: #briefr The Briefing Room

27. Big Data, Hot Data?
The Bloor Group

28. Hadoop & The Big Data Dynamic
Hadoop has become the de facto reservoir for data
The Bloor Group

29. Hadoop & The Big Data Dynamic
– We witnessed something like this a long time
ago, with ISAM files - before the advent of
RDBMS
– The difference this time is that Hadoop has an
ecosystem and it is growing
– Big Data (usually caught first by Hadoop) is
mostly new data and mostly event data
– Hadoop is not (yet) a performance engine. It is
an all-purpose capability
– It is delivering business benefits in a big way: it
is hot….
The Bloor Group

30. BI Categories
HINDSIGHT Regular reporting/operational BI, Excel
OVERSIGHT Dashboards, OLAP, BPM, Excel
Data mining, statistical analysis
INSIGHT (trends and relationships)
FORESIGHT Predictive analytics
The Bloor Group

31. The New BI Universe (?)
The Bloor Group

32. Data Sources
Graph
DBMS,
XML
Standard DBMS, NoSQL
SQL Flat files
Hadoop
and Metadata
Hadoop Hub?
++
The Bloor Group

33. Problems Of The Data Layer
Hadoop is capable of ETL and often
Hadoop is multi-role and hence
used for ETL, but that usually
can spawn multiple instances
involves coding of a kind
BI tools, which had good-enough
The data layer is more
interfaces to RDBMS, don’t link to
complicated than it was and its
Hadoop directly, and probably
complexity is increasing
shouldn’t
Point to point connectivity usually
A connectivity architecture is
was, is and may always be a bad
needed
idea
IT REQUIRES SIMPLE CONNECTORS
The Bloor Group

34. ! How would one use the Cirro Multi Store?
! Which companies/products do you regard as
competitors (either directly or close competitors)?
! How does a Cirro implementation proceed, i.e.,
where do you start, what are the medium term
goals, what do you replace?
! Conceptually a hub for the data layer is attractive.
But how well does it scale out?
The Bloor Group

35. ! Can the hub be physically distributed, i.e., one
logical instance with multiple physical instances?
! How does your proprietary MapReduce differ from
Hadoop MapReduce?
! Is there any aspect of BI that you don’t or can’t
cater for (CEP, Data governance, MDM, etc.)?
The Bloor Group

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37. Upcoming Topics
January: Big Data
February: Analytics
March: Data in Motion
2013 Editorial Calendar
www.insideanalysis.com
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38. Thank You
for Your
Attention
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