Webinar presented by Prateek Kapadia, CTO, Flytxt and Ryan Betts, CTO, VoltDB. Prateek and Ryan highlight the technology stack for Fast + Big data and iterative analytics using Hadoop and VoltDB. In addition, they will outline use cases for Big Data analytics with Hadoop, iterative analytics and machine learning using Spark and real-time analytics using VoltDB supplemented by Big Data + iterative analytics.

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VOLTDB AND FLYTXT PRESENT:
BUILDING A SINGLE TECHNOLOGY PLATFORM FOR
REAL-TIME AND ITERATIVE ANALYTICS ON FAST + BIG
DATA

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OUR SPEAKERS
Ryan Betts
CTO at VoltDB
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Prateek Kapadia
CTO at Flytxt

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VOLTDB OVERVIEW
Mike Stonebraker
FAST
World Record Cloud Benchmark:
YCSB (Yahoo Cloud Serving Benchmark) - 2.4 million tps (transactions per second)
Other Stonebraker Companies
Customers
3
Technology
• In-Memory (but data is durable to disk)
• Scale-Out shared-nothing architecture
• Reliability and fault tolerance
• SQL + Java with ACID
• Hadoop and data warehouse integration
• Open source and commercially licensed (24X7)
Founded by winner of the 2014 ACM Turing Award

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Collect Explore
AnalyzeAct
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Big Data analytic results:
1. Discoveries: seasonal predictions,
scientific results, long-term capacity
planning
2. Op.miza.ons: market
segmentation, fraud heuristics,
optimal customer journey

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FAST DATA – BIG DATA
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Fast Data Pipeline
Ingest Export
Big Data
Real-Time Analytics & Decisions
Fast Data: the velocity side of Big Data
Milliseconds

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DATA ARCHITECTURE FOR FAST + BIG DATA
Enterprise Apps
ETL
CRM ERP Etc.
Data Lake
(HDFS, etc.)
BIG DATA
SQL on
Hadoop
Map
Reduce
Exploratory
Analytics
BI
Reporting
Fast Operational
Database
FAST DATA
Export
Ingest /
Interactive
Real-time
Analytics
Fast Serve
Analytics
Decisioning
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“89% of marketers
surveyed plan to
compete primarily on
the basis of customer
experience by 2016.”
Source: Gartner 2014 survey, Companies > $50M in revenue

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FAST DATA SOURCES AND DRIVERS
Mobile
IoT
Social
Sensors
Logs
Data is doubling every two years
• 26 billion connected devices by
2020 (Gartner 2014)
• 37% of most data will be
processed at the edge in
milliseconds (Cisco IoT Study 12/11/14)
Mobile
IoT
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THE FAST DATA PIPELINE
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Calculations Serving of Results
Real Time, Per Event, Interactive

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STREAMING: REAL TIME ANALYTICS
• Operational analytics and monitoring
• RT analytics enabling user-facing applications
• KPI for internal BI/Dashboards
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STREAMING OPERATORS NEED STATE
Require State
• Filter
• Join
• Aggregate
• Group By
Stateless
• Partition
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REAL-TIME ANALYTICS
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Database
Metadata
(Dimension table)
Session state
(Fact table) • Operational analytics and
monitoring
• RT analytics enabling user-
facing applications
• KPI for internal BI/Dashboards
• In-memory MPP SQL over
ODBC/JDBC
• Cheap + correct materialized
views for streaming aggregations
SQL, Views
Ingest

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INTEGRATING WITH EXPORT TARGETS
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• Local file system export
• JDBC export
• Kafka export
• RabbitMQ export
• HDFS export
• HTTP export
• Extensible API

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EXPORT FORMATS
• CSV
• TSV
• Avro container
• Raw data

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DATA PIPELINES WITH EXPORT
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Database
Metadata
(Dimension table)
Session state
(Fact table)
• Filtering (ex: only RFID /
iBeacon readings that show
change from previous location).
• Sessionization
• Common version re-writing
• Data enrichment
• MPP streaming Export
• Row data, Thrift messages, CSV
• OLAP, HDFS and message queues
Export

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FLYTXT AND
VOLTDB
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FLYTXT OVERVIEW
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} Vision:
Create
>10%
measurable
economic
value
for
Communica8on
Services
Providers
through
Big
Data
Analy8cs
} Flytxt’s
internal
and
external
mone8za8on
solu8ons
increase
revenue,
reduce
churn
and
improve
customer
experience
} Dutch
company
with
corporate
office
in
Dubai,
global
delivery
centres
in
India
and
regional
presence
in
Mexico
City,
Johannesburg,
Singapore,
Dhaka
and
Nairobi.
Partners
Operators
Customers
and
Partners
Brands
Sample text
Awards & AchievementsVision, Mission & Impact

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FLYTXT’S INTEGRATED ANALYTICS SOLUTION ARCHITECTURE: BIG
DATA, ITERATIVE AND REAL-TIME ANALYTICS
Files
and
BI
outputs
Event
Filter
Trigger
Detector
Real
2me
Trigger
Engine
(RTE)
Processed
Events
Con2nuous
Insight
Engine
(CIE)
Data
Fusion
Engine
Batch
Analy2cs
Triggered
Rules
Scheduled
Rules
Rendering
Engine
Persistence
Store
KPIs,
Insights,
Recommenda2ons,
Ac2ons
&
Rules
Hadoop
2
Hadoop
2,
Hbase
Jboss,
Apache,
SMPP
sim,
Tomcat,
Ext
JS,
Hornet
Q
Hadoop
2,
Spark,
Pig,
Hive,
Mahout,
COIN-­‐OR,
Weka,
MLlib
VoltDB,
Drools
Response/
Input
Capture
Network
/
BSS
/
OSS
GUI
N/W
Integra2on
Network
/
BSS
/
OSS
Channels
Operator
Subscribers
Triggers
Lookup
Subscriber
Insights
Scan
Subscriber
Insights
Itera2ve
Analy2cs
2
Streaming
Data

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BIG DATA ANALYTICS USE CASE: BEST FIT PRODUCT RECOMMENDATION
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Profile,
Usage
Data
Fusion
Engine
Batch
Analy2cs
Persistence
Store
Hadoop
2
Con2nuous
Insight
Engine
(CIE)
Best
Fit
Product
Recommenda2on
Usage
Product
U8lity
Business
Constraints
Model
Ranking
Context
1
Context
2
Context
3
Recommended
Offers
P1
P2
P3
P3
P2
P1
P5
P4
P7
Ranking
based
on
Similari8es
Objec8ve:
Recommend
best
fit
product
to
subscribers
based
on
usage
and
business
objec8ves
Recommended
Offers
Recommended
Offers
Hadoop
2,
Hbase

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0
5
10
15
20
Offer-­‐1
Offer-­‐2
Offer-­‐3
Offer-­‐4
Offer-­‐5
Conversion
Rate
in
Jul-­‐2014
in
%)
0
5
10
15
20
25
Conversion
Rate
in
Jul-­‐2014
(in
%)
Circles
Rule-­‐based
campaign
Best-­‐fit
recommenda8on
(fair)
CASE
STUDY:
CONTEXTUAL
PRODUCT
RECOMMENDATION
FOR
TIER
1
OPERATOR
Recommenda2on
Personas:
CLV
(HVC,
MVC,
LVC),
Vola8le,
Early
Adopter,
Frequent
Handset
Changer,
Heavy
Data
user,
Social
Media
Fan,
Bollywood
Fan,
Music
Fan,
Sports
Fan,
poten8al
iPad
buyer,
Interna8onal
Caller
Etc…….
Objec2ves:
Cross
sell,
Upsell,
S8mulate
recharge/
usage/Service
adop8on
Etc…
Offers:
Data
Plan,
3G
plan,
VAS
usage,
Interna8onal
Calling
packs,
Bundle
offers,
Recharge
s8mula8on,
Seeding,
ebill
subscrip8on
etc……
Channels:
IVR,
In
store,
Retailer,
WAP
portal,
Customer
care
portal

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ITERATIVE ANALYTICS USE CASE: MICRO-SEGMENTATION
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Files
and
BI
outputs
Data
Fusion
Engine
Itera2ve
Analy2cs
Persistence
Store
Spark,
MLlib
Con2nuous
Insight
Engine
(CIE)
Micro-­‐segmented
Offers
Gaussian
Mixture
Model
Segment
Offers
Segment
Offers
Segment
Offers
Soi
Clustering
S1
P3
S2
P8
S4
P9
Clustering to enable
micro segmentation for
personalized offers
Hard Clustering
Soft Clustering
Hadoop
2,
Hbase

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USE CASE: REAL-TIME ANALYTICS SUPPLEMENTED BY BIG DATA,
ITERATIVE ANALYTICS
Files
and
BI
outputs
Event
Filter
Trigger
Detector
Processed
Events
Con2nuous
Insight
Engine
(CIE)
Data
Fusion
Engine
Analy2cs
Engine
Triggered
Rules
Scheduled
Rules
KPIs,
Insights,
Recommenda2ons,
Ac2ons
&
Rules
Hadoop
2
Hadoop
2,
Hbase
Hadoop
2,
Spark,
Pig,
Hive,
Mahout,
COIN-­‐OR,
Weka,
MLlib
VoltDB,
Drools
Itera2ve
Analy2cs
Persistence
Store
Big
Data+
Itera2ve
analy2cs
Real-­‐2me
analy2cs
Real-­‐8mes
Ac8ons
Balance
Threshold
Recharge
Usage
Network
Behaviour
Preference
Recommenda8on
Contextual
Needs
Streaming
Data

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CASE STUDY: REAL-TIME TRIGGER BASED MICRO-SEGMENTED
OFFERS
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Drop
in
overall
ARPU
Drop
in
data
usage
ARPU
Slabs
VAS
SMS
Voice
usage
DATA
Outgoing
Incoming
Long
term
inac2vity
Short
term
inac2vity
Product
Affinity
Local
Long
distance
Drop
in
O/G
MoU
Drop
in
balance
Leg-­‐wise
Usage
Segments
Usage
Behaviour
Client
Objec2ve
• Improve
customer
engagement
for
ARPU
enhancement
Solu2on
• Marke8ng
Program
based
on
usage
behaviour
driven
micro-­‐segmenta8on
and
tripwire
monitoring
Data
Analyzed
• Customer
usage
history,
ARPU
charts,
spend
palerns
and
preferences
Impact
• 2%
increase
in
month-­‐on-­‐
month
revenue
• 28%
higher
revenues
&
MOU

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QUESTIONS?
• Use the chat window to type in your questions or hashtag #VoltDBFlytxt
• Know more about Flytxt
• Visit www.Flytxt.com
• Try VoltDB yourself:
Ø Free trial of the Enterprise Edition:
• www.voltdb.com/download
Ø Try VoltDB in the Cloud
Ø Amazon’s Cloud Formation
Ø Open source version is available on github.com
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THANK YOU!
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