The document provides information about an upcoming webinar hosted by The Briefing Room. The webinar will focus on big data in January. Future webinars will cover analytics, open source, intelligence and other topics. The document includes details about the host, mission of The Briefing Room, and information on participating and asking questions during webinars. Analytics expert Robin Bloor and SQLstream CEO Damian Black are scheduled to present on big data during the January webinar.

1. The Briefing Room

2. Welcome
Host:
Eric Kavanagh
eric.kavanagh@bloorgroup.com
Twitter Tag: #briefr The Briefing Room

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. JANUARY: Big Data
February: Analytics
March: Open Source
April: Intelligence
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5. Big Data
Twitter Tag: #briefr
THERE IS NO MORE SMALL DATA
The Briefing Room
Copyrighted property. May not be copied or downloaded without permission from 123RF Limited.

6. Analyst: Robin Bloor
Robin Bloor is
Chief Analyst at
The Bloor Group
robin.bloor@bloorgroup.com
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7. SQLstream
! SQL stream is an enterprise software company focused on
making businesses responsive to real-time big data assets.
! Its core s-Server streaming data management platform
collects, analyzes and shares high volume, high velocity
structured and unstructured data from any source, in any
format.
! SQLstream recently introduced s-Server 3.o, which includes
distributed streaming data processing, machine data
collection, and integration with Google Big Query and
Hadoop Hbase.
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8. Damian Black
Damian Black is the founder and CEO of
SQLstream, a pioneer in Streaming Big Data.
Damian has worked for almost two decades
in Silicon Valley, with senior roles in a variety
of companies including Hewlett-Packard,
Neustar, Xacct Technologies and Followap.
He has always focused on real-time data
platforms for the largest Internet scale
applications. He has spoken at many
conferences, and was on GigaOM’s first Big
Data panel in 2008. Damian graduated from
Manchester University and was one of the
first research scientists to join HPLabs
Europe. He was selected for the
International Management Challenge in
conjunction with the Financial Times and
Ashridge business school while at Hewlett-
Packard. Damian is the author of eleven
granted patents with five more pending.
Twitter Tag: #briefr The Briefing Room

9. Windows of Oppor tunity:
Big Data on Tap™
January 2013
Damian Black
CEO, SQLstream
Copyright © 2013 SQLstream Inc.

10. S Q L s t r e a m V i s i o n
IN 2013 STREAMING DATA MANAGEMENT WILL EMERGE AS THE CORE
INTEGRATION AND OPERATIONAL INTELLIGENCE PLATFORM FOR
REAL-TIME BIG DATA SOLUTIONS WITHIN THE ENTERPRISE.
PROVEN
OPEN
INNOVATIVE
➔ Founded in 2003.
➔ 100% standard SQL.
➔ Leaders in Streaming Big
Data Management.
➔150+ engineering years.
➔ Dynamically extendable
using C++ Java and more.
➔ Best real-time technology
➔ Over 25 customers and
and most complete platform.
focused on Fortune 1000 ➔ Comprehensive set of
companies.
adapters.
➔ Holds 5 key streaming
patents (with 3 pending).
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 10

11. W h a t i s S t r e a m i n g B i g D a t a M a n a g e m e n t ?
DEFINITION
Streaming Big Data = Big Data +
Real-time Data Capture Collection +
Continuous Integration ETL +
Low Latency Transformation Analysis
EFFECT
Businesses become “real-time responsive” to Big Data.
Unlocks the power and value of real-time Big Data.
WHERE ARE THE REAL-TIME DATA SOURCES?
REAL- TIME DATA
Log and Machine Data
✔
Cloud and Device health
✔
Sensor Networks
✔
Social Interaction Feeds
✔
CDR and Service Data
✔
Automotive Telematics
✔
Wireless Networks
✔
Streaming media QoS
✔
GPS and Location Data
✔
Application transactions
✔
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 11

12. S t r e a m i n g B i g D a t a i n A c t i o n
Telematics
Cloud
Intelligent Transportation
Real-time traffic flow analytics from vehicle
Device health monitoring Real-time prediction of GPS data feeds
with intelligent integration
resource over-utilization
High volume, high velocity,
Social Media
structured and unstructured data Telecomm
Real-time semantic from software platforms, Real-time QoS and
streaming for QoE capacity monitoring from
monitoring
applications and systems.
CDR data
Log Files
Sensors
Services
Markets
Internet
Location
Networks
Devices
HPC
Banking
Big Data log monitoring on Real-time fraud and
a massive scale
security event prediction
Internet
Real-time content, activity and security
event monitoring
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 12

13. P l a t f o r m R e q u i r e m e n t s f o r R e a l - T i m e B i g D a t a
Continuous data analysis and integration using distributed streaming platform
Both On-Cloud Parallel Dataflow 100% Standards-
and On-Premise Distributed Stream compliant with
Deployment
Processing Architecture
true SQL:2008
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 13

14. S t r e a m i n g B i g D a t a – P a i n Po i n t s
DATA EXPLOSION
Too costly to analyse voluminous real-time data
BUSINESS AGILITY
Too slow to respond to new requirements
COMPLEXITY
Too difficult to build maintain real-time apps
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 14

15. S t r e a m i n g B i g D a t a – P a i n Po i n t s
DATA EXPLOSION
Too costly to analyse voluminous real-time data
SQLstream slashes TCO for real-time analysis.
BUSINESS AGILITY
Too slow to respond to new requirements
SQLstream allows you to add new apps easily.
COMPLEXITY
Too difficult to build maintain real-time apps
SQLstream eliminates your development risk.
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 15

16. T h e R e a l - t i m e D a t a M a n a g e m e n t H e a d a c h e …
Finance
Supply Chain
CRM
Operations
Business Intelligence:
Hadoop HBase
Accounting
ERP
Billing
Management
Data Warehouses
TIME, MONEY, COMPLEXITY
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 16

17. T h e R e a l - t i m e D a t a M a n a g e m e n t H e a d a c h e …
Finance
Supply Chain
CRM
Operations
Business Intelligence:
Hadoop HBase
Accounting
ERP
Billing
Management
Data Warehouses
STREAMING
STEAMING EVENT STREAMING
CONTINUOUS
ANALYTICS AND
CORRELATION
ALERTS ALARMS
ETL
AGGREGATION
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 17

18. M o v i n g f ro m H i g h L a t e n c y t o R e a l - t i m e
R e s p o n s i v e n e s s
➔ Traditional ETL approach leads to high latency
COLLECT
CLEANSE
ENRICH
ANALYZE
SHARE
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 18

19. M o v i n g f ro m H i g h L a t e n c y t o R e a l - t i m e
R e s p o n s i v e n e s s
➔ Traditional ETL approach leads to high latency
COLLECT
➔ SQLstream Streaming Approach:
» Continuous Parallel Dataflow Execution
CLEANSE
» Generate real-time answers immediately
» Deliver and share the results immediately
ENRICH
ANALYZE
SHARE
LOW LATENCY
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 19

20. S Q L s t r e a m D a t a fl o w Te c h n o l o g y
P i p e l i n i n g a n d S u p e r s c a l a r P a r a l l e l P r o c e s s i n g
= Query Processor
Fine-grained parallelism: simple, massively scalable, super fast.
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 20

21. A S t r e a m i n g S Q L Q u e r y
C l o u d I n f r a s t r u c t u r e M o n i t o r i n g w i t h B o l l i n g e r B a n d s
SELECT STREAM ROWTIME, url, numErrorsLastMinute
FROM ( SELECT STREAM ROWTIME, url, numErrorsLastMinute,
AVG(numErrorsLastMinute) OVER lastMinute AS avgErrorsPerMinute,
STDDEV(numErrorsLastMinute) OVER lastMinute AS stdDevErrorsPerMinute
FROM ServiceRequestsPerMinute
WINDOW lastMinute AS (PARTITION BY url RANGE INTERVAL ‘1’ MINUTE PRECEDING) ) AS S
WHERE S.numErrorsLastMinute S.avgErrorsPerMinute + 2 * S.stdDevErrorsPerMinute;
Business need:
Predict run-away applications
before resource consumption
becomes an issue.
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 21

22. C u s t o m e r B e n c h m a r ke d E x a m p l e A p p l i c a t i o n
SYSTEM CHARACTERISTICS
PERFORMANCE STATISTICS
Collection:
Intelligent Remote Agents (Distributed)
System Throughput:
1.35M events / sec
Enrichment:
Streaming data augmentation
Server Configuration:
1 x 4-core CPU
Analytics:
Temporal spatial pattern detection
Event Size:
~1KB
Output:
Data warehouse + applications (JDBC)
Data Sources:
Many
Network Data
Remote
Agent
Network Data
Remote SQLstream
Data
Agent
Warehouse
Network Data
Remote
Agent
ENRICH
ANALYZE
SHARE
Network Data
Remote
Agent
External
Remote Systems
Network Data
Agent
External Data
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 22

23. S Q L s t r e a m P r o d u c t Po r t f o l i o
➔ s-Server
Core Streaming Data Management and Integration
Platform
➔ s-Analyzer
Real-time data stream visualization and dashboards
➔ s-Studio
Developer and administration console
➔ s-Cloud
Cloud-based EC2 offering
➔ s-Transport
GPS, location-based and geospatial analytics module
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 23

24. R e a l - t i m e We b S e r v e r L o g M o n i t o r i n g
M o z i l l a ( G o o g l e : “ Yo u t u b e M o z i l l a G l o w ” )
Real-time monitoring across all download web Web Server Log Files (Remote)
servers across the world simultaneously.
Streaming
collecDon,
real-­‐Dme
analysis
and
conDnuous
➔ Collect
integraDon
by
locaDon
Remote agents transform log files into real-time streams
➔ Analyze
Hadoop HBase
Real-time analysis aggregation by location
➔ Share
Continuous ETL into Hadoop Hbase
Internet ‘Glow’ app for real-time visualization
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 24

25. R e a l - t i m e Tr a f fi c A n a l y t i c s
Tr a n s f o r m G P S d a t a i n t o r e a l - t i m e t r a f fi c fl o w i n f o r m a t i o n
Real-time traffic flow and congestion prediction GPS Vehicle Data Feeds
from vehicle GPS data.
Streaming
transformaDon
of
GPS
data
into
Traffic
Flow
and
➔ Collect
CongesDon
PredicDon
Events
Collect, cleanse and filter vehicle GPS data feeds
➔ Analyze
Geo-DB
‘Snap-to-map’
Transform GPS records into traffic flow information
Prediction events for congestion alerts
➔ Share
Real-time Google Maps and Google Earth Displays
Web and Smartphone access
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 25

26. R e a l - t i m e O p e r a t i o n a l I n t e l l i g e n c e
M a r k e t C o m p a r i s o n
TRADITIONAL BIG SQLSTREAM
ENTERPRISE
DATA OPERATIONAL STREAMING BIG
CAPABILITY
INTELLIGENCE TOOLS
DATA PLATFORM
SQLSTREAM BENEFITS
True Real-time
Moderate to high latency.
Real-time low latency.
Instant results
Incomplete answers.
Complete answers.
Sophisticated
Simple patterns.
Full SQL power.
Elegantly handle every
No real power.
Very high-level, concise.
business need.
Analytics
Joins Correlation
Operates on a single feed Join correlate across Compare and combine
only.
multiple different feeds
info in real-time.
Data Enrichment Weak, simplistic.
Continuous, powerful.
Create complete
Incomplete.
Comprehensive.
answers continuously.
Integration
Big Data Scalability
Limited scalability.
Massively scalable.
Delivers low cost, high
Cost prohibitive.
Inexpensive.
performance needed
No parallel processing.
Massively parallel.
for real-time big data.
Development Ease
Proprietary and low level. Standard SQL.
Instant productivity.
Expensive.
Optimized.
No hidden obstacles.
Time consuming.
Parallel.
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 26

27. A N e w D a t a M a n a g e m e n t Q u a d r a n t
High Level Declarative
Language Operation (SQL)
Stale snapshots
Always current
Not real-time
Streaming integration
Costly recalculations
Rapid development
DATA
STREAMING
WAREHOUSES
BIG DATA
Historical Analysis
Continuous Analysis
Periodic Batches
Real-time Processing
BATCHED
MESSAGING
BIG DATA
MIDDLEWARE
Batch processing
Low-level software
Low-level but scalable
Scattered business logic
Extensive coding
Brittle with high TCO
Low Level Procedural Language
Operation (C++, C#, Java, Pig, JCL, etc.)
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 27

28. B I G DATA O N TA P ™ – D e l i v e r e d .
Slashes the TCO for real-time analysis.
DATA EXPLOSION
• Scales easily and continuously processes data in real-time.
Makes adding new apps easy.
BUSINESS AGILITY
• Create powerful real-time apps, and share results easily.
Eliminates the development risk and pain.
COMPLEXITY
• Real-time parallel processing made simple, scalable and fast.
Copyright
©
2013
1 Big Data on Tap™ |
Damian
Black
|
+1
877
571
5775
|
enquiries@sqlstream.com | 28

29. Windows of Oppor tunity:
Big Data on Tap™
Thanks!
Damian Black
CEO, SQLstream
Copyright © 2013 SQLstream Inc.

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

31. Harnessing Data Flow
The Bloor Group

32. Hadoop Is The Reservoir
• Because of its flexibility and scalability as a
data store, Hadoop has become the natural
reservoir for data, but…
– Hadoop is a multi-purpose engine, but not a
performance engine – do not be fooled by its
parallelism
– Sometimes you don’t have time to drop the data
into Hadoop first; it is not necessarily the first
port of call for data
– Sometimes it may be better to leave data where
it is, and just replicate
The Bloor Group

33. Event Processing
The Bloor Group

34. Event Stream Processing
The Bloor Group

35. Operational Intelligence
• Real-time BI could also be called
operational Intelligence (OI)
• It poses three problems:
– How to establish the stream data flow
(at an acceptable speed)
– How to process the data
– How to manage the data
The Bloor Group

36. A Side Comment…
• We are familiar with the issue of “Data
Life Cycle”
• This issue didn’t just evaporate with the
advent of Big Data and Streams
Processing – it became more important
The Bloor Group

37. ! Does the platform include its own database?
! You use an enhanced SQL for streams processing.
Can it handle unstructured data (such as a tweet
stream)?
! You characterize your analytics as being
“advanced.” Can you expand on what you mean by
that? What analytic capabilities does it include?
! It wasn’t entirely clear to me as to how you
integrate with legacy data warehouse data flows.
Consider data cleansing for example. How does the
SQLstream Platform accommodate that?
The Bloor Group

38. ! Which sectors/businesses do you expect to be able
to make best use of this technology?
! Which companies/products do you regard as
competitors (either directly or close competitors)?
! Which companies/products do you partner with?
! How is the product/platform priced? How is the
cloud version priced?
The Bloor Group

39. Twitter Tag: #briefr The Briefing Room

40. Upcoming Topics
This month: Big Data
February: Analytics
March: Open Source
April: Intelligence
www.insideanalysis.com
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41. Thank You
for Your
Attention
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