Presented by Jack Norris, SVP Data & Applications at Gartner Symposium 2016. Jack presents how companies from TransUnion to Uber use event-driven processing to transform their business with agility, scale, robustness, and efficiency advantages. More info: https://www.mapr.com/company/press-releases/mapr-present-gartner-symposiumitxpo-and-other-notable-industry-conferences

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© 2016 MapR Technologies
The Keys to Digital Transformation

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“We will disrupt ourselves through Data”
Jamie Dimon
Chairman, President & CEO, JPMorgan Chase
How will data disrupt your company?

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IT Spending at an Inflection Point
Source: IDC, Gartner; Analysis & Estimates: MapR
Next-gen consists of cloud, big data, software and hardware related expenses
(100,000)
(80,000)
(60,000)
(40,000)
(20,000)
-
20,000
40,000
60,000
80,000
100,000
120,000
2013 2014 2015 2016 2017 2018 2019 2020
Next Gen vs. Legacy Shrinkage ($M)
$120
100
80
60
40
20
(20)
(40)
(60)
(80)
(100)
In Billions
Total $ Growth of IT Market Next-Gen Growth Legacy Market Growth/Shrink in $

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You Need to Make a Decision Now
In 4 years 90% of Data will be on next-gen technology

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A Once-in-30-Year Re-Platforming of the Enterprise
• Critical infrastructure for next-gen applications
• Data platform enabler for required Speed, Scale, & Reliability
New Applications Existing Applications
Open Source Analytic Innovations Legacy
Disruptive Data Platform
On Premise Private Cloud Public Cloud
Heterogeneous Hardware

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$
$
$

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Event Driven Heath Care

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© 2016 MapR Technologies 12MapR Confidential
Data Warehouse Offload: Cost per Terabyte Comparisons
vAugmenting legacy
infrastructure can save over
$1.5K or 79% in annual op ex
per TB and slash the cap ex
per TB by $4.8K or 95%
vLegacy: Typical net Exadata
pricing with rack, storage
server, and DB bundle
software
§ Assumes 100% use of available
storage
$0.00
$0.25
$0.50
$0.75
$1.00
$1.25
$1.50
$1.75
$2.00
Legacy MapR
Annual Op Ex per TB ($000)
Hardware HW Maintenance Licenses License Support MapR Technology
$0.00
$0.65
$1.30
$1.95
$2.60
$3.25
$3.90
$4.55
$5.20
Legacy MapR
Cap Ex per TB ($000)

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$
$
$

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Largest Biometric Database in the World
PEOPLE

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Driving Business Value
$19.4 Million
Per interviewed organization, average
discounted benefits over three years

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Legacy Business Platforms Are Two Islands
• IT must integrate all the products
• Inability to operationalize the insight rapidly
• Can’t deal with high speed data ingestion and processing
• Scale up architecture leads to high cost
Message
Bus
Specialized Storage
Operational Applications
J2EE
AppServer
Relational
Database
Specialized Storage
Analytical Applications
Analytic Database ETL Tool BI Tool

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Traditional Applications Dictate Data
Application
Data
ETL process
Specialized schema
Security

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Applications Dictate Data – Results in Silos

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The Keys to Digital Transformation
1. Data Convergence

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The Secret is to Bring Analytics & Operations Together
Converged Applications
Complete Access to Real-time and
Historical Data in One Platform
Historical Immediate

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Before
• Complex and Slow
• Multiple Versions of the Truth
• Difficult Governance
• High TCO
• Real-Time Data to Action
• Single Data Copy
• Easy Governance
• Low TCO – Scales Horizontally
Analytics
Operations
HTAP (Hybrid Transaction/Analytical Processing) – Gartner Group 2015
Data
Data
Data
Converged

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Federated Approach Results in Complexity
Streaming
Real-time
Batch Loads
Apps
Streaming Cluster
(TIBCO, IBM, Kafka)
Open Source
Analytics
(Hadoop, Spark)
Enterprise Storage
(System of Record)
Operational Cluster
(Hbase, Cassandra)

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Data Platform Requirements
Integrated Analytics
Enterprise Grade
Scale
Legacy Support
Transformational
Applications
Real Time

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Comparing Data Platforms
S T O R A G E
D A T A

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H A D O O P
Comparing Data Platforms
D T A
C O M P U T E
D A T A

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Comparing Data Platforms
C O M P U T E
S P A R K

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Comparing Data Platforms
C A S S A N D R A
D T A
C O M P U T E
D A T A

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Comparing Data Platforms
C O M P U T E
D T AD A T A
Write In:
–– –– –– –
O T H E R

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C O N V E R G E D D A T A P L A T F O R M
Comparing Data Platforms
Data in MotionData at Rest
C O M P U T E
D T AD A T A

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Yield Management Optimization
Global
Semi-conductor
Company

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National Oilwell Varco (NOV)

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Managing Risk/FraudAmerican Express
Fraud protection on $1 trillion
Decisions made in less than 2 milliseconds

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Big Data is Generated One Event at a Time
“time” : “6:01.103”,
“event” : “RETWEET”,
“location” :
“lat” : 40.712784,
“lon” : -74.005941
“time: “5:04.120”,
“severity” : “CRITICAL”,
“msg” : “Service down”
“card_num” : 1234,
“merchant” : ”Apple”,
“amount” : 50

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The Keys to Digital Transformation
2. Stream Processing

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Event-based Data Flows
web events
etc…
machine sensors
Biometrics
Mobile events

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Streams Enable Real-Time Applications
Failure
Alerts Real-time
application &
network
monitoring
Trending
now
Web
Personalized
Offers
Real-time Fraud Detection
Ad optimization
Supply Chain
Optimization

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Streams Enable Real-time Processing
● Ops dashboards
● Failure alerts
● Breach detection
● Real-time fraud detection
● Real-time offers
● Push notifications
● Location analytics
● Trending now
● News feed

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Producers ConsumersEvents_Topic
A stream is an unbounded sequence of events carried from a
set of producers to a set of consumers.
Events
What’s a Stream?
Producers and consumers don’t have to be aware of each other, instead they
participate in shared topics. This is called publish/subscribe.

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Events are delivered in the order they are received, like a queue.
Unlike with a queue, events are persisted even after they’re delivered.
Streams vs Queues

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Buffer &
Save Hourly
Files
Aggregate
by minute
Join
Batch
Load
Batch
Load
Streams Simplify Data Integration Pipelines
Analytics
& BI
Filter
Aggregate
by Minute
Join

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Examples of Producers & Consumers
Social Platforms
Servers
(Logs, Metrics)
Sensors
Mobile Apps
Other Apps &
Microservices
Alerting Systems
Stream Processing
Frameworks
Databases &
Search Engines
Dashboards
Other Apps &
Microservices

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Flexible Communication Modes
Consumer Group
C
One to One Many to Many
Orders Clicks
P P P
C CC

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JSON DB
(MapR-DB)
Graph DB
(Titan on
MapR-DB)
Search Engine
(Elastic-Search)
Transforming the Health Care Ecosystem
Electronic Medical Records
“The Stream is the
System of Record”
–Brad Anderson
VP Big Data Informatics

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● Topics are logs
Logical View
ANATOMY OF A TOPIC
Partition 1
Partition N
C1P
P
C2
C2b
● Consumers belonging to same
group divide partitions
● Producers write to the head of
the log
● Consumers read at their own
pace, each with a cursor
● Partitions used for load
balancing
● Producers split events based
on key

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Streams Enable Event-based Microservices
Clicks Sessions Conversions
Sessionizer Converted?

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The Keys to Digital Transformation
3. Application Agility

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Microservices Overview
• Microservices is an approach to application
development in which a large application is built
as a suite of modular services.
• Each module supports a specific business goal
and uses a simple, well-defined interface to
communicate with other modules.

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Event-Driven Microservices
Microservices combined with Streams
and a Converged Platform provides:
• Application development across file, database,
document and streaming services
• Ultra scale, utility grade performance
• Greater efficiency and simplicity than alternative
architectures
• Integrated data-in-motion and data-at-rest and
continuous and low latency processing

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Microservices Architecture
Message Broker
Processing
Services
Processing
Services
Message Broker

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Microservices Architecture
Message Broker
Processing
Platform
Processing
Platform
Streaming Data
Platform

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Converged Microservices Architecture
MapR Converged Data Platform
Converged
Message and
Processing
Services

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Converged Platform Appeal for Developers: Agility
Flexibility
Agility
Simplicity
Real Time

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Appeal for Architects and Administrators
• Simplifies administration
• Avoids cluster sprawl
• Unifies security, data protection, disaster recovery

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Existing Environments – Complex Silos

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A P P
D A T A
D A T A
A P P
A P P
A P P
D A T A
Road to Digital Transformation

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Converged Data Platform

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The Keys to Digital Transformation
1. Data Convergence
2. Stream Processing
3. Application Agility

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ThankYou
@norrisjack maprtech
jnorris@mapr.com
MapR
maprtech
mapr-technologies

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