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www.ovum.com
© Copyright Ovum 2014. All rights reserved.
Fast Data:
The Rebirth of Streaming Analytics
Tony Baer
Ovum
Teradata Partners, October 21, 2015
© Copyright Ovum 2014. All rights reserved.
 What is Streaming Analytics?
 The Rebirth
 Technology Landscape
 Takeaways
Agenda
© Copyright Ovum 2014. All rights reserved.
What is Streaming Analytics?
Analyzing & Acting on data in motion
Incoming
data
In Motion
Filtered extract
Streaming Analytics
Conventional Analytics
Sense,
Transform/Filter,
Analyze Data Respond
Analyze Respond
Event
processor
Ingest,
Persist Data
Data store
Data with perishable
value
Data with historical value
Incoming
data
© Copyright Ovum 2014. All rights reserved.
Streaming Analytics is not
simply responding to
alarms or outliers
© Copyright Ovum 2014. All rights reserved.
Streaming Analytics examples
 Telcos – process CDRs for mediation,
revenue assurance, fraud detection,
churn prevention
 FS – process trades for fraud
detection & anomalous activity, refine
trading strategies
 Utilities – process smart meter data
for demand-side management
programs
 Healthcare – patient monitoring for
alerts (e.g., sepsis outbreaks) &
offline clinical research
© Copyright Ovum 2014. All rights reserved.
The roots of Streaming Analytics
© Copyright Ovum 2014. All rights reserved.
Streaming Analytics roots
Complex Event Processing (CEP)
Define Event &
relationships to
other events
Define
Event/state
Transition
Define Pattern
matching rules
Define
Response rules
Event Stream Processing (ESP)
Sliding time
windows for
correlation &
aggregation of
events
© Copyright Ovum 2014. All rights reserved.
The showstopper?
Complexity
Costly hardware
Limited bandwidth
Proprietary software
Narrow market appealLimited skills base
No standards
CEP
© Copyright Ovum 2014. All rights reserved.
The rebirthof
StreamingAnalytics
© Copyright Ovum 2014. All rights reserved.
What’s changed?
 Use Cases – Driven by explosion of
Mobile & IoT data
 Commodity Infrastructure – Scale-
out clusters, multi-core CPUs,
gigabit networks, affordable DRAM
& Flash storage
 Open Source – lowering barriers to
entry for developers, data scientists,
enterprises, and vendors
 Machine Learning provides more
flexible, adaptive alternative to rules
© Copyright Ovum 2014. All rights reserved.
Mobile data growth
Source: Ovum
© Copyright Ovum 2014. All rights reserved.
IoT growth
Source: Cisco
2014 2019
67%
40%
By 2019, most IP
traffic will come from
non-PC devices
By 2019 Global IP traffic will
grow 3x to 2 zettabytes/yr.
By 2016, most IP traffic to come from wireless devices
© Copyright Ovum 2014. All rights reserved.
Emerging use cases
 Retail – real-time customer
engagement via smartphone
interaction
 Manufacturing – prescriptive
maintenance
 Telco – Real-time message routing
optimization & bottleneck prevention
 Local govt. – Real-time Smart City
applications
 Cybersecurity – Real-time detection
& thwarting of intrusions/attacks
© Copyright Ovum 2014. All rights reserved.
Streaming Analytics Technology Landscape –
Then
Tibco Streambase
Software AG Apama
SAP Complex Event Processing
Oracle Event Processing
Informatica Rulepoint
IBM InfoSphere Streams
© Copyright Ovum 2014. All rights reserved.
Streaming Analytics Technology Landscape –
Now
Veterans
Community
Open Source
New Players
Tibco
Software AG
SAP
Oracle
Informatica
IBM
Spark Streaming
Flink
Kafka
Storm
Samza
DataTorrent
Msft. Azure Stream Analytics
Amazon Kinesis
Teradata Listener
Tigon
Heron
SAS
© Copyright Ovum 2014. All rights reserved.
Streaming Analytics Technology Landscape –
Contrasts
Veterans
Community
Open Source
New Players
• CEP/ESP rebranded &
leveraging modern
commodity
infrastructure
• Mature enterprise
software
• Mix of proprietary &
vendor-lead open
source
• Cloud prominence
• Expanding the
practitioner base
• Leveraging ML instead
or in addition to rules
• Manual coding
Tibco
Software AG
SAP
Oracle
Informatica
IBM
Spark Streaming
Flink
Kafka
Storm
Samza
DataTorrent
Msft. Azure Stream Analytics
Amazon Kinesis
Teradata Listener
Tigon
Heron
SAS
© Copyright Ovum 2014. All rights reserved.
Takeaways
 Streaming Analytics… is back!
 It’s not only for Wall St. anymore
 Mobile & IoT driving compelling real-time use cases outside traditional
FS/capital markets niche
 Machine Learning provides more adaptive, flexible alternative (or addition) to
rules
 Commodity infrastructure & open source makes Streaming Analytics
affordable, scalable & performant
 Open source erodes barriers to entry – but the software is still raw
 Don’t rule out mature commercial products – but they must exploit modern
commodity, scale-out distributed architectures!
www.ovum.com
© Copyright Ovum 2014. All rights reserved.
Thank you
Tony Baer
Ovum
(646) 546-5330
tony.baer@ovum.com Twitter: @TonyBaer

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Fast Data:The Rebirth of Streaming Analytics

  • 1. www.ovum.com © Copyright Ovum 2014. All rights reserved. Fast Data: The Rebirth of Streaming Analytics Tony Baer Ovum Teradata Partners, October 21, 2015
  • 2. © Copyright Ovum 2014. All rights reserved.  What is Streaming Analytics?  The Rebirth  Technology Landscape  Takeaways Agenda
  • 3. © Copyright Ovum 2014. All rights reserved. What is Streaming Analytics? Analyzing & Acting on data in motion Incoming data In Motion Filtered extract Streaming Analytics Conventional Analytics Sense, Transform/Filter, Analyze Data Respond Analyze Respond Event processor Ingest, Persist Data Data store Data with perishable value Data with historical value Incoming data
  • 4. © Copyright Ovum 2014. All rights reserved. Streaming Analytics is not simply responding to alarms or outliers
  • 5. © Copyright Ovum 2014. All rights reserved. Streaming Analytics examples  Telcos – process CDRs for mediation, revenue assurance, fraud detection, churn prevention  FS – process trades for fraud detection & anomalous activity, refine trading strategies  Utilities – process smart meter data for demand-side management programs  Healthcare – patient monitoring for alerts (e.g., sepsis outbreaks) & offline clinical research
  • 6. © Copyright Ovum 2014. All rights reserved. The roots of Streaming Analytics
  • 7. © Copyright Ovum 2014. All rights reserved. Streaming Analytics roots Complex Event Processing (CEP) Define Event & relationships to other events Define Event/state Transition Define Pattern matching rules Define Response rules Event Stream Processing (ESP) Sliding time windows for correlation & aggregation of events
  • 8. © Copyright Ovum 2014. All rights reserved. The showstopper? Complexity Costly hardware Limited bandwidth Proprietary software Narrow market appealLimited skills base No standards CEP
  • 9. © Copyright Ovum 2014. All rights reserved. The rebirthof StreamingAnalytics
  • 10. © Copyright Ovum 2014. All rights reserved. What’s changed?  Use Cases – Driven by explosion of Mobile & IoT data  Commodity Infrastructure – Scale- out clusters, multi-core CPUs, gigabit networks, affordable DRAM & Flash storage  Open Source – lowering barriers to entry for developers, data scientists, enterprises, and vendors  Machine Learning provides more flexible, adaptive alternative to rules
  • 11. © Copyright Ovum 2014. All rights reserved. Mobile data growth Source: Ovum
  • 12. © Copyright Ovum 2014. All rights reserved. IoT growth Source: Cisco 2014 2019 67% 40% By 2019, most IP traffic will come from non-PC devices By 2019 Global IP traffic will grow 3x to 2 zettabytes/yr. By 2016, most IP traffic to come from wireless devices
  • 13. © Copyright Ovum 2014. All rights reserved. Emerging use cases  Retail – real-time customer engagement via smartphone interaction  Manufacturing – prescriptive maintenance  Telco – Real-time message routing optimization & bottleneck prevention  Local govt. – Real-time Smart City applications  Cybersecurity – Real-time detection & thwarting of intrusions/attacks
  • 14. © Copyright Ovum 2014. All rights reserved. Streaming Analytics Technology Landscape – Then Tibco Streambase Software AG Apama SAP Complex Event Processing Oracle Event Processing Informatica Rulepoint IBM InfoSphere Streams
  • 15. © Copyright Ovum 2014. All rights reserved. Streaming Analytics Technology Landscape – Now Veterans Community Open Source New Players Tibco Software AG SAP Oracle Informatica IBM Spark Streaming Flink Kafka Storm Samza DataTorrent Msft. Azure Stream Analytics Amazon Kinesis Teradata Listener Tigon Heron SAS
  • 16. © Copyright Ovum 2014. All rights reserved. Streaming Analytics Technology Landscape – Contrasts Veterans Community Open Source New Players • CEP/ESP rebranded & leveraging modern commodity infrastructure • Mature enterprise software • Mix of proprietary & vendor-lead open source • Cloud prominence • Expanding the practitioner base • Leveraging ML instead or in addition to rules • Manual coding Tibco Software AG SAP Oracle Informatica IBM Spark Streaming Flink Kafka Storm Samza DataTorrent Msft. Azure Stream Analytics Amazon Kinesis Teradata Listener Tigon Heron SAS
  • 17. © Copyright Ovum 2014. All rights reserved. Takeaways  Streaming Analytics… is back!  It’s not only for Wall St. anymore  Mobile & IoT driving compelling real-time use cases outside traditional FS/capital markets niche  Machine Learning provides more adaptive, flexible alternative (or addition) to rules  Commodity infrastructure & open source makes Streaming Analytics affordable, scalable & performant  Open source erodes barriers to entry – but the software is still raw  Don’t rule out mature commercial products – but they must exploit modern commodity, scale-out distributed architectures!
  • 18. www.ovum.com © Copyright Ovum 2014. All rights reserved. Thank you Tony Baer Ovum (646) 546-5330 tony.baer@ovum.com Twitter: @TonyBaer