As companies continue to invest in big data, their focus is shifting from predictive analytics for reporting and business dashboards to machine learning & AI for real-time intelligent decision-making embedded in software. Many organizations are testing, exploring and piloting applications that automatically promote trending products, adjust prices or respond to alerts raised by intelligent real-time systems. In her talk, Ms. Victoria Livschitz, founder and CTO of Grid Dynamics, will discuss common business drivers of real-time analytics applications and the emerging platforms for building such applications. To learn more, read an adjoining blog series on this topic here : https://blog.griddynamics.com/in-stream-processing-service-blueprint

1. Privileged and confidential
Open Blueprint for Real-Time Analytics
with In-Stream Processing (ISP)
Victoria Livschitz, Founder & CTO, Grid Dynamics
03/16/2017

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Business Need
About the speaker:
CTO @Grid Dynamics: present
Founder and CEO @Grid Dynamics: 2006 – 2013
Principal engineer @Sun: 1997 – 2006
Engineering IT services company focused on digital transformation
through cloud, big data & open source for Fortune 500 clients.
Pioneer in real-time processing from company’s inception in 2006.
Architected 3 out of top-10 busiest e-commerce sites. Never had
production outage in peak season.
Frequent contributor to open source projects: Hadoop, Solr,
Lucene, Storm, others.
Victoria Livschitz
About Grid Dynamics:

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Agenda
• What is “real-time” in analytics, and why it matters?
• In-Stream Processing: emerging platform for real-time processing
• Open ISP blueprint: reference architecture, reference implementation

4. What is “real-time”, anyways?
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What is “real-time” in analytics, machine learning,
data sciences & AI?
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How long is the cycle?
What is done online vs. offline?
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Weeks Days Hours Seconds
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What is “real-time” in analytics, machine learning,
data sciences & AI?

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1.Offline learning/analytics, online response
Valueof“real-time”

8. 2. Offline learning, real-time
analytics, online response
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10. Whyreal-timematters?
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Example: B2C retail use cases relative to “real-timeness”
Level 1: Segmented historic context: data on
what happened to all such customers before
Level 3: Situational context: where customer is,
what she wants – or might buy - right now
Level 4: Supply chain dynamics: demand surge,
product availability, competitive pricing
From time to time, send a coupon
based on a segment
Level 2: individualized historic context: 360-
degree view across personal data
On a birthday, offer a coupon based
on personal history
Right now, offer a product based on
what’s in her hands
During a storm, deliver trending
umbrella/pancho combo
Example: Personalized Offers

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Level 1: Segmented historic context: data on
what happened to all such customers before
Level 3: Situational context: where customer is,
what she wants – or might buy - right now
Level 4: Supply/demand dynamics: impact of
demand surge, shortage, competitive actions...
Level 2: individualized historic context: 360-
degree view across individual’s data Suited
for offline
ML
Requires
real-time
ML
Historic aggregated
data
Real-time
individual’s data
Historic
individual’s data
Real-time
everything
Example: B2C retail use cases relative to “real-timeness”

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Example 1: top drivers of real-time applications in retail
#3. Dynamic pricing
Determine “right price” for products
based on availability, trending,
personal context & competitive price
#1. Personalized search
Augment search hits and relevancy
ranking based on personal context
#2. Personalized offers
Motivate “buy now” behavior by
offering context-aware deals
#4. Dynamic inventory
Predict inventory needs & re-stock
products in stores based on
fluctuations in inventory & demand
#5. Intelligent sourcing
Determine what order to source from
what store to optimize delivery SLAs
& shipment costs
#6. Real-time alerts
Detect unusual patterns: fraud, surge in
demand, weather changes, shift in
brand sentiment. Respond right away

14. Example: real-time in fin tech & compliance

15. Emergingtechnologyforreal-time
analytics:In-StreamProcessing(ISP)
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In a complex landscape of Big Data systems…

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…In-Stream Processing (ISP) service is an approach
to build real-time extensions of Big Data applications
Today’s
focus

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Conceptual architecture

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ISP pipelines: complex behavior with simple steps
Easy to write, change or add a step

20. Open ISP blueprint: reference
architecture & reference
implementation
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Blueprint goals
Scalable to
100,000+
events /second
Real-time streaming;
real-time ML
Cloud-portable
Proven for mission-
critical use
Open source
(and built 100%
with open source)
Production-ready
Portable across
clouds
Extendable

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Selected stack for ISP blueprint
• REST API
• Message Queue
• HDFS
• Other

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Designed as a complete platform
• No single points of failure
• No bottlenecks
• Built-in scaling
• Dockerized
• Deployable to any cloud
• Reference implementation for
AWS (open source)
• Reference demo: real-time
twitter sentiment analytics for
new movie reviews

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Where to learn more
• 7-part blog series on ISP
• 7-part blog series on Data Science Kitchen
1. Read our blog: blog.griddynamics.com
2. Let’s chat today
• Stop by our booth, chat in a corridor
3. Connect
• Twitter: @griddynamics
• Subscribe to our blog
• Drop email: info@griddynamics.com