Stream processing architectures are rapidly emerging in various business domains to offer real-time ML and processing applications such as fraud detection, dynamic inventory, personalized offers and many more. To learn more, read an adjoining blog series on this topic here : https://blog.griddynamics.com/in-stream-processing-service-blueprint

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Open Blueprint for Real-Time Analytics in Retail
Victoria Livschitz, Founder & CTO, Grid Dynamics

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About the speaker:
Chairman & CTO: present
Founder and CEO: 2006 – 2013
Principal engineer @Sun: 1997 - 2006
About Grid Dynamics:
Engineering IT services company focused on
digital transformation through cloud & open
source for Fortune 500 clients.
Pioneer in real-time processing from 2006.
Frequent contributor to open source projects:
Hadoop, Solr, Lucene, Storm, others.
Victoria Livschitz

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What is “real-time”, anyways?

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What is “real-time” in analytics, ML, DS & AI?
Receive
event
Event
Analyze
event
Act on
event
ResponseAugment
model
How long is the cycle?
What is done online vs. offline?
Learning Analysis

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Weeks Days Hours Seconds
What is “real-time” in analytics, ML, DS & AI?
Receive
event
Event
Analyze
event
Act on
event
ResponseAugment
model
How long is the cycle?
What is done online vs. offline?
Learning Analysis

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Event
Act on
event
Response
Receive
event
A few seconds
A day or more
Receive
event
Augment
model
Analyze
event
Modify
reaction
1.Offline learning/analytics, online response
Value
of “real-time”

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2. Offline learning, real-time
analytics, online response
Event
Act on
event
Response
Receive
event
A few seconds
Receive
event
Augment
model
Analyze
event
Modify
reaction
1.Offline learning/analytics, online response
Event
Receive
event Response
Analyze
event
Act on
event
A few
seconds
Receive
event
Augment
modelDay +
Value
of “real-time”
A day or more

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Receive
event
Analyze
event
Act on
event
Augment
model
3. Real-time learning/analytics, online response A few seconds
2. Offline learning, real-time
analytics, online response
Event
Act on
event
Response
Receive
event
A few seconds
A day
Receive
event
Augment
model
Analyze
event
Modify
reaction
1.Offline learning/analytics, online response
Valueof“real-time”
Event
Receive
event Response
Analyze
event
Act on
event
A few
seconds
Receive
event
Augment
modelDay +
Event Response

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Where real-time matters in retail?

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Classification of 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/demand dynamics: demand
surge, product availability, competitive pricing
From time to time, send a coupon
based on a sex, age, income
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 coupon based
on what product is in her hands
During a storm, offer to deliver
umbrella/pancho combo in 30 min
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
Classification of use cases relative
to “real-timeness”

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Catalog of common use cases (L 2-4): data sources
Category
Real-time online
recommendations
Real-time aid to store
sales associate
Dynamic
product pricing
Augmented search
results & facets
Individualized, real-time
email offers
Real-time inventory
management
Real-time corporate
dashboards
Individualized, dynamic
product bundles
Where
Online
Anywhere
Online &
in-store
Online search
& browse
In-store
Online &
in-store
Store &
corporate
Store &
corporate
Target
Consumer
Consumer
Consumer
Consumer
Sales
associate
Consumer
Logistics
Marketing,
logistics, etc.
Level 2:
Individualization
Level 3:
Personal situation
Level 4:
Demand dynamics
Aggregated
historic data
+
Customer’s
preferences, prior
completed /
abandoned
purchases,
searches, product
reviews, social
feeds
All of the above
+
Streaming geo-
fence data,
streaming beacon
data, click
streams,
streaming
checkout or
payment events
All of the above
+
What’s trending,
viral stories in
social networks,
changes in local
product
availability,
weather or event
driven demand,
competitive
pricing & offers
N/A
All of the above
+
Streaming supply
chain events

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Catalog of common use cases (L 2-4): user stories
Category
Real-time online
recommendations
Real-time aid to store
sales associate
Dynamic
product pricing
Augmented search
results & facets
Individualized, real-time
email offers
Real-time inventory
management
Real-time corporate
dashboards
Individualized, dynamic
product bundles
Where
Online
Anywhere
Online &
in-store
Online search
& browse
In-store
Online &
in-store
Store &
corporate
Store &
corporate
Target
Consumer
Consumer
Consumer
Consumer
Sales
associate
Consumer
Logistics
Marketing,
logistics, etc.
Level 2:
Individualization
Level 3:
Personal situation
Level 4:
Demand dynamics
Example:
Search results are
ranked higher for
products that
match (a) prior
purchases, (b)
prior product
views, (c) known
brand preference,
(d) trending
Example
In-store associate
offers products
based on
(a) known
brand/color/style
preferences,
(b) products tried
in the dressing
room (c) trending
matches
N/A
(a) Predict inter-
day inventory
shortage; (b) alert
excess discounts
Example
At checkout, offer
bundle
recommendations
& discount based
on (a) trending
purchases, (b)
individual
preferences, (c)
predicted
product’s
availability

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Catalog of common use cases (L 2-4): algorithms
Category
Real-time online
recommendations
Real-time aid to store
sales associate
Dynamic
product pricing
Augmented search
results & facets
Individualized, real-time
email offers
Real-time inventory
management
Real-time corporate
dashboards
Individualized, dynamic
product bundles
Where
Online
Anywhere
Online &
in-store
Online search
& browse
In-store
Online &
in-store
Store &
corporate
Store &
corporate
Target
Consumer
Consumer
Consumer
Consumer
Sales
associate
Consumer
Logistics
Marketing,
logistics, etc.
Level 2:
360-degree, historic
Level 3:
Personal situation
Level 4:
Demand dynamics
Offline data
feed
+
Offline learning
+
Offline analytics
+
Online
response
N/A
Real-time
events
+
Offline learning
+
Real-time
analytics
+
Online
response
Real-time
events
+
Real-time
learning
+
Real-time
analytics
+
Online
response

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Top 6 Drivers of Real-Time Analytics in Retail

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Top 6 drivers of real-time applications
#6. Real-time alerts
Detect unusual patterns: fraud, surge
in demand, weather changes, shift in
brand sentiment. Respond right away

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Top 6 drivers of real-time applications
#6. Real-time alerts
Detect unusual patterns: fraud, surge
in demand, weather changes, shift in
brand sentiment. Respond right away
#5. Intelligent sourcing
Determine what order to source
from what store to optimize
delivery SLAs & shipment costs

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Top 6 drivers of real-time applications
#6. Real-time alerts
Detect unusual patterns: fraud, surge
in demand, weather changes, shift in
brand sentiment. Respond right away
#5. Intelligent sourcing
Determine what order to source
from what store to optimize
delivery SLAs & shipment costs
#4. Dynamic inventory
Predict inventory needs & re-stock
products in right stores based on
fluctuations in demand

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Top 6 drivers of real-time applications
#6. Real-time alerts
Detect unusual patterns: fraud, surge
in demand, weather changes, shift in
brand sentiment. Respond right away
#3. Dynamic pricing
Determine right price or incentives
based on availability, trending,
personal context & competitive price
#5. Intelligent sourcing
Determine what order to source
from what store to optimize
delivery SLAs & shipment costs
#4. Dynamic inventory
Predict inventory needs & re-stock
products in right stores based on
fluctuations in demand

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

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

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Emerging platform for real-time analytics:
In-Stream Processing (ISP)

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In a complex landscape of Big Data systems…

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…in-stream processing service is an approach
to build real-time extensions of Big Data applications
Today’s
focus

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• Fraud detection
• Sentiment analytics
• Preventive maintenance
• Facilities optimization
• Network monitoring
• Intelligence and surveillance
• Risk management
• E-commerce
• Clickstream analytics
• Dynamic pricing
• Supply chain optimization
• Predictive medicine
• Transaction cost analysis
• Market data management
• Algorithmic trading
• Data warehouse augmentation
Rapidly growing applications in multiple industries

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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

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ISP marketplace: build vs. buy

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Grid Dynamics open blueprint for ISP

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Blueprint goals
Pre-integrated Real-time streaming;
real-time ML
Cloud-ready
Proven 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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Common ISP systems interfaces

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

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“Take ISP for a spin” demo: Real-time twitter
sentiment analytics for new movie reviews

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Real-time demo, a.k.a. “Data Science Kitchen”
• Provide reference example on how to use ISP platform…
• .. and learn the basics of data science along the way
• Gets actual Twitter data via streaming API
• Analyses & visualizes what people think about latest movies
• Exposes data science “kitchen”: models, training sets, dictionaries
• Provides nice web UI to play with data
• Uses our ISP RI (reference implementation)
• Demo is running on AWS as a public service
• Everything is open sourced
• Documentation on http://blog.griddynamics.com
goo.gl/ZoC7nB

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Demo app: pick movies you want to monitor

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Compare different views on data

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New
Star Wars
movie
announced
Oscar night
Carrie Fisher
dies
Compare trending between different movies
Examples of
positive &
negative Carrie
Fisher tweets

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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. Connect
• Twitter: @griddynamics
• Subscribe to our blog
• Drop email: info@griddynamics.com

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