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Transforming Your Business with Fast Data – Five Use Case Examples

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Page 1: Transforming Your Business with Fast Data – Five Use Case Examples

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TRANSFORMING YOUR BUSINESS WITH FAST DATA – FIVE USE CASE EXAMPLES

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Page 2: Transforming Your Business with Fast Data – Five Use Case Examples

page© 2016 VoltDB

OUR SPEAKER

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Dheeraj RemellaDir. 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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Page 5: Transforming Your Business with Fast Data – Five Use Case Examples

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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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Page 10: Transforming Your Business with Fast Data – Five Use Case Examples

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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 costs14

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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 set16

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 withReal-Time

Real-Time EventDetection

Real-TimeDecisioning 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

1.5 to 2.5 times 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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Page 27: Transforming Your Business with Fast Data – Five Use Case Examples

page© 2016 VoltDB

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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