The Briefing Room with Mark Madsen and Striim Live Webcast October 20, 2015 Watch the archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=e99ac17a361f5e77f3844edf8471750ca Organizations worldwide are learning hard lessons these days about the constraints of dated information systems. The time-tested process of Extract-Transform-Load (ETL) is fast losing its ability to cope with the volume, velocity and variety of Big Data coming down the pike. Forward-thinking companies are therefore prepping the battlefield by designing on-ramps to the future of streaming analytics. Register for this episode of The Briefing Room to hear Analyst Mark Madsen explain how a new era of data solutions is rising to the challenge of streaming data. He'll be briefed by Steve Wilkes, founder and CTO of the Striim platform. Steve will share how enterprises are turning to streaming data integration, in-memory transformations and continuous processing to achieve the goals of ETL in milliseconds – at a fraction of the cost and complexity of legacy systems. Several case studies will be shared. Visit InsideAnalysis.com for more information.

1. Grab s-­me
c-­ffee and
enj-­y the
pre!sh-­w
banter
bef-­re the
t-­p -­f the
h-­ur

2. The Briefing Room
Is ETL Now a 4-Letter Word? Preparing for Streaming Analytics

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

4. Twitter Tag: #briefr The Briefing Room
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!
Mission

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Topics
October: DATA MANAGEMENT
November: ANALYTICS
December: INNOVATORS

6. Twitter Tag: #briefr The Briefing Room
Race Cars Need Professional Race Tracks
! Structural
Integrity Matters
! Archaic
Architecture
Causes Problems
! Once in Place,
Enjoy the Show!

7. Twitter Tag: #briefr The Briefing Room
Analyst: Mark Madsen
Mark Madsen is president of Third Nature, a
technology research and consulting firm
focused on business intelligence, data
integration and data management. Mark is
an award-winning author, architect and
CTO whose work has been featured in
numerous industry publications. Over the
past ten years Mark received awards for his
work from the American Productivity &
Quality Center, TDWI, and the Smithsonian
Institute. He is an international speaker, a
contributor to Forbes Online and on the
O’Reilly Strata program committee. For
more information or to contact Mark, follow
@markmadsen on Twitter or visit http://
ThirdNature.net

8. Twitter Tag: #briefr The Briefing Room
Striim
Striim, formerly WebAction, is a streaming and intelligence
platform specializing in data integration across multiple
internal and external sources
The platform leverages continuous in-memory processing to
deliver insights within seconds
Striim enables data correlation, anomaly detection, alert
and workflow triggers and detailed visualizations

9. Twitter Tag: #briefr The Briefing Room
Guest: Steve Wilkes
Steve Wilkes, Founder and CTO of Striim, is a life-
long technologist, architect, and hands-on
development executive. Prior to founding
WebAction, Steve was the senior director of the
Advanced Technology Group at GoldenGate
Software. Here he focused on data integration and
continued this role following the acquisition by
Oracle, where he also took the lead for Oracle’s
cloud data integration strategy. His earlier career
included Senior Enterprise Architect at The
Middleware Company, principal technologist at
AltoWeb and a number of product development
and consulting roles including Cap Gemini’s
Advanced Technology Group. Steve has handled
every role in the software lifecycle and most roles
in a technology company at some point during his
career. He still codes in multiple languages, often
at the same time.

10. brought to you by WebAction
Is#ETL#Now#a#4,Le.er#Word?#
Preparing#for#Streaming#Analy?cs#
SteveWilkes -CTO/FounderWebAction October2015

11. Striim Executive Summary
The Striim Team is uniquely qualified to solve the
streaming analytics problem.
Founded#
#
Leading#investors#
#
Technology#
#
Customers#
Founded#in#2012#by#core#team#out#of#GoldenGate#SoKware#and#WebLogic#
#
Backed#by#leading#investors:#Summit#Partners#and#Intel#Capital#
#
Striim#PlaOorm#,#Current#release#,#v.#3.1.4##
#
Deployments#in#financial#services,#telco,#retail,#gaming,#IoT#

12. Striim provides
Streaming Integration and Intelligence
enabling companies to
Make data useful the instant it’s born.

13. FINANCE# CRM# MARKETING# HR# SALES# SUPPLY# INVENTORY#

14. FINANCE# HR#CRM# SUPPLY# INVENTORY#MARKETING# SALES#
DATA#
WAREHOUSE#
ETL

15. Log files
Sensors
Sensors
Sensors
Sensors Sensors
Log files
Log files Log filesLog files
Log files
Message Queues
Message Queues

16. Log files
Sensors
Sensors
Sensors
Sensors Sensors
Log files
Log files Log filesLog files
Log files
Message Queues
Message Queues

17. • Exponential increase in volume/velocity/variety of data
• Increasing business need to address issues while you can affect
the outcome
• Customer / Employee expectations for instant response
• Know what the customer knows as soon or before
• Big Data and ETL are designed for batch processing
• Enterprises aren’t getting value out of Big Data investments
– data dumped into data lakes with no organization/filtering
– by the time it’s processed/analyzed, it’s too late
– can't integrate database change into Big Data

18. Data Growth Challenges
• Data Growth is Exponential
• More data created in the next 2 years
than ever created before
• Storage cannot keep up
– In 2014 we could store 1/3 of the data
– By 2020 it will be 1/6
• Only half of the data that should
be secured is secured
* According to IDC's 2014 Digital Universe Study
0
10
20
30
40
50
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Amount of Data on the Planet (ZB)

19. Gartner 2014 (Casonato, Heudecker, Beyer, Adrian)
“Through 2018, 90% of deployed data lakes will be useless as they
are overwhelmed with information assets captured for uncertain
use cases. ”
Bill Schmarzo 2015 - CTO of EMC Global Services
“The problem is that most customers are still tackling this whole big
data conversation. You start with your technology, bring in some
Hadoop, throw some data in there and you kind of hope magic stuff
happens. It’s really a process fraught with all kind of misdirection.”

20. You Don't Query OLTP
• You need to:
– simplify
– filter
– denormalize
• Data dumped into
Hadoop is not
designed for querying
*#OBIEE#11g#Training#Online#Class#

21. • Access to data
– CDC for databases
– Parallel collection of log files
– Edge processing for sensor data
• In-flight processing
– Transformation / Filtering
– Enrichment / Denormalization
– Aggregation / Removal of redundant data
• Scale-out
– Handle huge and increasing volume
– Handle incremental processing requirements
• Security and Governance
– Know what data you have
– Being able to protect it granularly
="Streaming"Integra-on"

22. Database
Transactions
Streaming CDC
Log files
Parallel Log Collection
Sensors
Continuous
Event Collection
Message Queues
Continuous
Event Collection
STREAMINGINTEGRATION
Correlation
EASY TO IMPLEMENT | COST EFFECTIVE | REALTIME CONTINUOUS PROCESSING
Simple Pass Through
of Streaming Data
Collect from Source
Deliver to Target
In Milliseconds
Big Data
Cloud
Databases &
Data Warehouses
Message Queues

23. Database
Transactions
Streaming CDC
Log files
Parallel Log Collection
Sensors
Continuous
Event Collection
Message Queues
Continuous
Event Collection
STREAMINGINTEGRATION
Correlation
EASY TO IMPLEMENT | COST EFFECTIVE | REALTIME CONTINUOUS PROCESSING
Big Data
Cloud
Databases &
Data Warehouses
Message Queues
Filtering
EnrichmentAggregation
Transformation
WindowingContinuous
Queries

24. Database
Transactions
Streaming CDC
Log files
Parallel Log Collection
Sensors
Continuous
Event Collection
Big Data
Cloud
Databases &
Data Warehouses
Message Queues
Message Queues
Continuous
Event Collection
Correlation
EASY TO IMPLEMENT | COST EFFECTIVE | REALTIME CONTINUOUS PROCESSING
Alerts
ResultsStore
Real-time
Dashboards
CorrelationDetection
STREAMING
INTELLIGENCE
External
Context
Analyze
Predict
Search
Filtering
EnrichmentAggregation
Transformation
WindowingContinuous
Queries
STREAMINGINTEGRATION

25. Internode Communication
Distributed Cache
Indexed Results StoreProcessing
Machine Learning
Visualization
Cluster Management
Data
Collection
Data
Delivery
DevelopmentCDC
Logs
Queues
Sensors
Cloud
Big
Data
Queues
DB
con?nuous#streaming#data#and#queries#

26. Internode Communication
Distributed Cache
Indexed Results StoreProcessing
Machine Learning
Visualization
Cluster Management
Data
Collection
Data
Delivery
DevelopmentCDC
con?nuous#streaming#data#and#queries#
YARN#

27. Internode Communication
Distributed Cache
Indexed Results StoreProcessing
Machine Learning
Visualization
Cluster Management
Data
Collection
Data
Delivery
DevelopmentCDC
con?nuous#streaming#data#and#queries#
YARN#
+
Java / Python / C++ / Scala /
Erlang / Go / JS
+
A Team of Engineers
+
Time

28. Internode Communication
Distributed Cache
Indexed Results StoreProcessing
Machine Learning
Visualization
Cluster Management
Data
Collection
Data
Delivery
Development
Vendor
Specific
CDC
con?nuous#streaming#data#and#queries#
Product Specific Hand-Crafted / 3rd Party
ETL
ETL
ETL CEP
Analytics In Memory Grid
In Memory Grid +
Enterprise Search
Message Bus
OS Clustering or Other Product

29. Internode Communication
Distributed Cache
Indexed Results StoreProcessing
Machine Learning
Visualization
Cluster Management
Data
Collection
Data
Delivery
Development
Vendor
Specific
CDC
ETL
con?nuous#streaming#data#and#queries#
Product Specific Hand-Crafted / 3rd Party
ETL
ETL CEP
Analytics In Memory Grid
In Memory Grid +
Enterprise Search
Message Bus
OS Clustering or Other Product
+
Product Specific Languages
+
A Team of Consultants
+
Time

30. Internode Communication
Distributed Cache
Indexed Results StoreProcessing
Machine Learning
Visualization
Cluster Management
Data
Collection
Data
Delivery
DevelopmentCDC
Logs
Queues
Sensors
Cloud
Big
Data
Queues
DB
con?nuous#streaming#data#and#queries#
Single End-To-End Platform
Develop in UI with SQL
Enterprise Ready
Secure and Robust
Scale-Out
Architecture

31. • Capture data the instant it’s born from structured
and unstructured sources
• Non-intrusive streaming CDC for transactional
data sources
• Correlate data across multiple streams in real time
• Enrich streaming data with reference and
historical data for continuous integration
• Build streaming integration pipelines quickly,
using a SQL-like language
• Granular security framework for streaming data
Streaming"Integra-on"
• Analyze data and trigger alerts and workflows
the instant data is born
• Continuously updating visualizations of
streaming data
• Enrich streaming data with reference and
historical data for immediate context
• Process events once-and-only-once
• Enterprise-strength and enterprise-scale
leveraging low-cost compute.
Streaming"Intelligence"
An#end2to2end"streaming#integra?on#and#intelligence#solu?on.#
#

32. Solutions and Use Cases
Security Event
Processing
Cloud Application
Control
Risk & Fraud
Alerting
Quality of Service
Management
Consumer
Analytics
Device (IOT),
Datacenter
Analytics
VIP Customer Quality of Service Monitoring
Anti-Money Laundering
Predictive Device Maintenance
Geo-targeted Mobile Marketing
Anomaly Detection
Connected Cars
Point of Sale Monitoring
Retail Trends and Anomalies
Multi-log Correlation
Fraud Detection and Prevention
Risk Management
Credential Monitoring
API Usage Monitoring
SLA MonitoringCross-platform Attack Monitoring
Partner Activity Monitor

33. Scenario: You need to get activity in the form of change from
relational databases into Hadoop, processing it on the way
• User and application activity in enterprise databases is an essential
information asset but it can be difficult and expensive to access
• To get a complete picture of what’s happening in your enterprise,
you need to include the realtime change in corporate databases
• Upfront stream processing such as filtering and enrichment, optimizes
data storage footprint and stored data become more actionable
• Change data can be used for
• maintaining an audit trail
• understanding activity patterns
• cross referencing with other sources like websites

34. Scenario #1: A credit or debit card is used from two different ATM
devices in distant locations within a short span of time.
• Fraud case over distance
• 10 km minimum distance between two ATM locations
• 5 minute window
• Tens of Thousands of ATMs
Scenario #2: Account balances of multiple cards have been
requested from the same ATM device within a short span of
time without completing a transaction.
• At least three or more cards used
• 2 minute window
• Tens of Thousands of ATMs

35. Scenario: Monitor activity of customers in realtime to ensure
best possible customer experience
• Compare behavior to historical records and flag anomalies
• Realtime risk profiling, alerting and workflow automation
• Identify issues affecting customers before they experience them
• Utilize customer context to determine actions
• deal with VIP customers differently
• use customer models for realtime scoring
• Detect and react to patterns that may indicate customer churn

36. striim.com
@striimteam
steve@striim.com

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Perceptions & Questions
Analyst:
Mark Madsen

38. 11
Is#ETL#Now#a#4,Le.er#
Word?#Preparing#for#
Streaming#Analy?cs#
!
Analyst!commentary!
!
!
!
October,!2015!
Mark!Madsen!
Third!Nature!
@markmadsen!
!
#!$@*!!ETL
%*ELT#&*!

39. Copyright!Third!Nature,!Inc.!
In#a#mostly,connected#world,#events#occur#in#
different#?me#frames,#follow#different#cycles#of#use#
12
Source:!Noumenal!
Disconnected!
Milliseconds !Minutes !Hours+!

40. Copyright!Third!Nature,!Inc.!
In#a#mostly,connected#world,#events#occur#in#
different#?me#frames,#follow#different#cycles#of#use#
13
Source:!Noumenal!
Disconnected!
Every!event!is!
persisted!for!some!
period!of!Kme!before!
it!is!forgoLen!or!
forwarded!
Milliseconds !Minutes !Hours+!

41. Copyright!Third!Nature,!Inc.!
In#a#mostly,connected#world,#events#occur#in#
different#?me#frames,#follow#different#cycles#of#use#
14
Source:!Noumenal!
Disconnected!
Local!context!
and!control,!
local!decisions,!
local!latency!
Milliseconds !Minutes !Hours+!

42. Copyright!Third!Nature,!Inc.!
In#a#mostly,connected#world,#events#occur#in#
different#?me#frames,#follow#different#cycles#of#use#
15
Disconnected!
Source:!Noumenal!
Milliseconds !Minutes !Hours+!
Bigger!context,!
likely!correlated,!
more!complex!
rules,!external!
monitoring!

43. Copyright!Third!Nature,!Inc.!
In#a#mostly,connected#world,#events#occur#in#
different#?me#frames,#follow#different#cycles#of#use#
16
Disconnected!
Source:!Noumenal!
Milliseconds !Minutes !Hours+!
Broad!context,!human!
intervenKon,!diagnosis!
and!analyKcal!tasks!that!
have!to!be!coordinated.!

44. Copyright!Third!Nature,!Inc.!
Source:!Noumenal!
Milliseconds !Minutes !Hours+!
In#a#mostly,connected#world,#events#occur#in#
different#?me#frames,#follow#different#cycles#of#use#
17
Disconnected!
Data!lives!in!mulKple!places,!
at!mulKple!levels!of!detail,!for!
differing!duraKons.!Unlikely!to!
all!be!in!one!place.!
Nor#should#it#be.#

45. Copyright!Third!Nature,!Inc.!
We#have#a#model#for#the#persisted#por?on#only#
The!DW!can’t!handle!real!Kme!ingest!
▪ One!of!the!original!DW!design!assumpKons:!solve!for!
conflicKng!workloads!by!using!a!different!database!
▪ Workload!management!has!limits!
▪ Scalability!problem!for!event!streams!
▪ Spiky!flow!paLerns!and!dynamic!scaling!
StaKc!schema:!
▪ ReacKon!Kme!W!shapes,!holes,!dropped!packets!
▪ What!happens!first,!upstream!change!or!data!model!change?!
Polling!architectures!do!not!work!well!for!streaming!
▪ Introduces!latency!
▪ Polling!creates!performance!and!scaling!problems!

46. Copyright!Third!Nature,!Inc.!
Capture#
Sensors#
Machine#
Data#
Logs#
Events#
Transc?ons#
Table#
changes#
!
Propagat
e#
Filter#
Transform#
Correlate#
Aggregate#
Analyze#
Classify#
Detect#
anomalies#
Detect#
pa.erns#
Correlate#
Elect#
Rules#
Algorithms#
Select#
Coordinate#
Effect#
No?fy#
Publish#
Approve#
Execute#
!
Persist#
Database,#NoSQL,#Files,#Hadoop#

47. Copyright!Third!Nature,!Inc.!
Flowing Persisted
Sliding window
of “now”
Persisted but not yet
loaded into a platform
Queryable history
Managed history
Streaming#isn’t#either,or,#it’s#part#of#core#architecture#
A DB or ETL can get you to within
minutes (at large scale) but it
won’t be easy or cheap; mainly
lives in the realm of history
Event streams, in-mem
stores, CEP streaming
SQL can be used for these
Real time monitoring doesn’t use only real time data: windows, restarts,
detecting deviation, so the above boundaries are crossed.
ESB Cache/Queue Database / platform

48. Copyright!Third!Nature,!Inc.!
Stream
If#you#want#to#do#real?me#and#s?ll#manage#your#data#
effec?vely#then#you#need#to#think#about#data#architecture!
Collect Refine Manage Deliver
Flowing Managed historyPersisted
Microservices Metadata
Metadata & reuse?
Flow,!persisted,!managed!define!different!access,!
processing,!storage!and!retrieval!requirements

49. Copyright!Third!Nature,!Inc.!
About#Third#Nature#
Third!Nature!is!a!research!and!consulKng!firm!focused!on!new!and!emerging!technology!
and!pracKces!in!analyKcs,!business!intelligence,!and!performance!management.!If!your!
quesKon!is!related!to!data,!analyKcs,!informaKon!strategy!and!technology!infrastructure!
then!you‘re!at!the!right!place.!
Our!goal!is!to!help!companies!take!advantage!of!informaKonWdriven!management!
pracKces!and!applicaKons.!We!offer!educaKon,!consulKng!and!research!services!to!
support!business!and!IT!organizaKons!as!well!as!technology!vendors.!
We!fill!the!gap!between!what!the!industry!analyst!firms!cover!and!what!IT!needs.!We!
specialize!in!product!and!technology!analysis,!so!we!look!at!emerging!technologies!and!
markets,!evaluaKng!technology!and!hw!it!is!applied!rather!than!vendor!market!posiKons.!

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Upcoming Topics
www.insideanalysis.com
October: DATA MANAGEMENT
November: ANALYTICS
December: INNOVATORS

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for your
ATTENTION!
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