The explosion of iIoT & mobile data have created compelling new use cases for analyzing data in motion. Commodity scale-out infrastructure, bandwidth&open source are pushing streaming analytics to the front burner.

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Fast Data:
The Rebirth of Streaming Analytics
Tony Baer
Ovum
Teradata Partners, October 21, 2015

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 What is Streaming Analytics?
 The Rebirth
 Technology Landscape
 Takeaways
Agenda

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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

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Streaming Analytics is not
simply responding to
alarms or outliers

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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

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The roots of Streaming Analytics

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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

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The showstopper?
Complexity
Costly hardware
Limited bandwidth
Proprietary software
Narrow market appealLimited skills base
No standards
CEP

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The rebirthof
StreamingAnalytics

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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

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Mobile data growth
Source: Ovum

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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

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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

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Streaming Analytics Technology Landscape –
Then
Tibco Streambase
Software AG Apama
SAP Complex Event Processing
Oracle Event Processing
Informatica Rulepoint
IBM InfoSphere Streams

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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

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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

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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!

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Thank you
Tony Baer
Ovum
(646) 546-5330
tony.baer@ovum.com Twitter: @TonyBaer