Dheeraj Remella, Director of Solutions Architecture, VoltDB discusses how five customers transformed their business with VoltDB. Industries covered include ad tech data counting and tracking, content analytics for a global CDN, financial market data latency reduction and regulatory compliance, online game sessionization, player profiling and monetization and mobile network subscriber personalization, and ad delivery.

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TRANSFORMING YOUR BUSINESS
WITH FAST DATA – FIVE USE CASE
EXAMPLES
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OUR SPEAKER
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Dheeraj Remella
Dir. of Solutions
Architecture, VoltDB

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DATA-FICATION OF LIFE
"Smartness can be embedded everywhere,"
said Professor Sangiovanni-Vincentelli, EE/CS
at University of California at Berkeley.
"The entire environment is going to be full of
sensors of all kinds. Chemical sensors, cameras
and microphones of all types and shapes.
Sensors will check the quality of the air and
temperatures. Microphones around your
environment will listen to you giving
commands.”
Computerworld, September 2015
The 10 Trillion Device World

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FAST = ADVANTAGE
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IN THE FUTURE….
All businesses will compete on
their ability to make decisions
“in the moment” using Fast Data.

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Big Data
“Perishable insights can have exponentially more value than
after-the-fact traditional historical analytics.”
Mike Gualtieri, Principal Analyst, Forrester Research
Fast Data

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WHAT’S NEW HERE?
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Analytics Action

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TRANSFORM YOUR BUSINESS TO FAST
Use Cases
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Interactive personalized, customer engagement
Precision resource counting and billing
Real-time policy enforcement
IoT sensor data processing

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TRANSFORM YOUR BUSINESS TO FAST
• Fast is different than Big
• VoltDB is the industry’s only purpose-
built architecture for fast data
• VoltDB has helped hundreds of
developers build fast data apps
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VOLTDB: DESIGNED BY DATABASE ‘SUPER FRIENDS’
• Fast Data Technology
• In-Memory operational DB + Data Pipeline
• Scale-out architecture, SQL + Java
• Designed by database ‘super friends’
• Mike Stonebraker, MIT, 2014 Turing Award Winner
• Dan Abadi, Yale
• Sam Madden, MIT
• Andy Pavlo, CMU
• Stan Zdonik, Brown University
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DATA FLOWS
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Pipeline Data Lake
Ingest
Real Time
Analytics
Export
Using Real Time Data
Request/
Response

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COUNTING / BALANCE KEEPING
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• High rate of ingestion
• Rapidly changing data
• Materialized Views
• Real-time Group By counts and sums

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COUNTING / BALANCE KEEPING
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• “Last Dollar” problem
• Accuracy is important:
• For billing
• To decide the right action

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AIRPUSH
• Pay for placement, bill for clicks
• Correlate clicks to ad budgets
• Ad campaign balance tracking
• Cost of overshooting budget:
• Lost revenue
• Opportunity cost
• Bidding cost (would have been a lower bid)
• Real-time analytics to support budget optimization
• Reduced infrastructure costs
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MaxCDN
• Content delivery network (CDN)
• Real-time analytics of content delivery
• Over 32 TB of daily web server log data
• 300K log records per second
• Accurate billing
• Custom bulk-loading to batch records by partition
• Procedures load a batch of hundreds of records
• Ad-Hoc SQL queries
• Reduced infrastructure costs
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SESSIONIZATION / CORRELATION
• Sessions consist of segments or events
• Can arrive out of order
• While ingesting each event:
• Query to correlate with previous events in session
• Process when session is complete
• Known “last” event arrives
• Check for a complete set
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Events Sessions

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Business model: convert from free to fee
• They needed to select a database that could
• Give consistent single figure millisecond response times for
complex transactions
• Maintain in near real time potentially hundreds of
transactionally consistent metrics for a user
• This is vital in the online game industry
• Games change on a weekly basis to sustain growth
• Massive numbers of players, creating billions of data points
• Real-time in game interaction to catch the player at exactly
the right point
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DELTADNA

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EVENT DETECTION
• Filtered events
• Aggregate events crossing
thresholds
• Calendar events within range
• Geo-spatial entry or exit events
• Patterns of events
• A then B
• A then B then 3 or more C’s
• Threshold exceeded then 2 or more D’s
• Complexity leads to a Rules Engine
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Raw Events Business Events

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PERSONALIZATION
• Personalized experiences
• Fast retrieval and intelligent use of user data
• Segment
• Preferences
• History
• Recommendations
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REAL-TIME EVENT DECISIONING
PLATFORM
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Real-Time Alerting & Monitoring, Reporting, Analytics, Configuration, Auditing
IN
DWH
HLR/
VLR
Web
CSR
Social
Filters
Accumulators
Detectors
Pattern
Recognition
Rules Engine
Offer
Matching
Business
Rules
Models
Decisioning
NBA
Fulfilment
Comms
Channels
Integration with
Real-Time
Real-Time Event
Detection
Real-Time
Decisioning Fulfilment
Event Decision Execute
Network
Elements
Upstream
Deliver the best interaction
aligned to each
individual customer in
real-time to drive customer
engagement & maximise business
results

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• Ingest Mobile Phone usage events in real-time
• Detect marketing opportunity events
• First time use of a feature
• Plan usage thresholds: 50%, 75%, 90%, 95%, 100%
• Time left, or time since activation, first use, etc.
• Personalize offers by segment and history
• Pre-paid phones = much more customer engagement
• Measure success rate of offers
• A/B testing
Emagine results show the power of real-time to be
1.5to 2.5times greater

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REAL-TIME CALCULATIONS
• Similar to Counting:
• High rate of ingestion
• Rapidly changing data
• Materialized Views
• Group By counts and sums
• Real-time
• Accurate
• More complex calculations
• Not just aggregation
• Stored procedures retrieve
history to calculate and
update values
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MAJOR FINANCIAL CUSTOMER
• National Best Bid and Offer (NBBO)
• The lowest available ask price and highest available bid price across all markets
• Upsert a table with the latest prices for each stock from each market, then query to
find the best prices
• Volume-weighted average price (VWAP)
• SUM(price*quantity)/SUM(quantity)
• Time-weighted average price (TWAP)
• SUM(price*seconds at that price)/(elapsed seconds since market opened)
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• Real-time Slippage
• Comparing trades & executions to the market
• Not getting the best available price
• Incurring market impact costs
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MAJOR FINANCIAL CUSTOMER

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FAST DATA: A WIDE VARIETY OF USE CASES
Mobile
• Subscriber engagement,
personalization
• User session Management
• OSS/BSS – policy, billing, routing
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Financial Services
• Risk management (portfolio, trading)
• Fraud detection
• Compliance (BB&O)
• Customer engagement
Media and Entertainment
• Personalization
• Digital advertising
• Content delivery
• Gaming
IoT/Sensors
• Smart Energy
• Connected Home
• Patient monitoring

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WHY POWER YOUR FAST DATA APPS WITH VOLTDB?
Faster
Simpler Better
ü Faster
• tps, response latency
ü Simpler
• Fraction of components and coding vs alternatives
• Easier to test, maintain and support
ü Better
• Correct results
• Lower overall system risk
• Higher availability and reliability
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RESOURCES
Fast Data and the New Enterprise Data
Architecture:
http://learn.voltdb.com/EBookFastData.html
Download VoltDB:
www.voltdb.com/download
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