the keys to digital transformation

© 2016 MapR Technologies 1© 2016 MapR Technologies 1

®

© 2016 MapR Technologies

The Keys to Digital Transformation

© 2016 MapR Technologies 2© 2016 MapR Technologies 2

© 2016 MapR Technologies 3© 2016 MapR Technologies 3© 2016 MapR Technologies© 2016 MapR Technologies

“We will disrupt ourselves through Data” Jamie Dimon

Chairman, President & CEO, JPMorgan Chase

How will data disrupt your company?

© 2016 MapR Technologies 4© 2016 MapR Technologies 4

IT Spending at an Inflection Point

Source: IDC, Gartner; Analysis & Estimates: MapRNext-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 $

© 2016 MapR Technologies 5© 2016 MapR Technologies 5

You Need to Make a Decision Now

In 4 years 90% of Data will be on next-gen technology

Source

© 2016 MapR Technologies 6© 2016 MapR Technologies 6

A Once-in-30-Year Re-Platforming of the Enterprise

• Critical infrastructure for next-gen applications• Data platform enabler for required Speed, Scale, & Reliability

NewApplications ExistingApplications

OpenSource AnalyticInnovations Legacy

DisruptiveDataPlatform

OnPremise PrivateCloud PublicCloud

HeterogeneousHardware

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$$

$

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© 2016 MapR Technologies 9© 2016 MapR Technologies 9

© 2016 MapR Technologies 10© 2016 MapR Technologies 10

© 2016 MapR Technologies 11© 2016 MapR Technologies 11

Event Driven Heath Care

®© 2016 MapR Technologies 12®© 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)

© 2016 MapR Technologies 13© 2016 MapR Technologies 13

$$$

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© 2016 MapR Technologies 16© 2016 MapR Technologies 16

Largest Biometric Database in the World

PEOPLE

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Driving Business Value

$19.4 MillionPer interviewed organization, average discounted benefits over three years

© 2016 MapR Technologies 18© 2016 MapR Technologies 18

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

© 2016 MapR Technologies 19© 2016 MapR Technologies 19

Traditional Applications Dictate Data •This slide is not conceptual.. Maybe the shape of a glass (rounded at bottom)

•The top says application•The bottom layer is data•ETL process with arrow pointing at it•Specialized schema with arrow pointing•Security with arrow….

Application

DataETL process

Specialized schemaSecurity

© 2016 MapR Technologies 20© 2016 MapR Technologies 20

Applications Dictate Data – Results in Silos

© 2016 MapR Technologies 21© 2016 MapR Technologies 21

The Keys to Digital TransformationI need a better wrap-up graphic. I’ll also change

text.

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

© 2016 MapR Technologies 24© 2016 MapR Technologies 24

Federated Approach Results in Complexity

Streaming

Real-time

Batch LoadsApps

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

© 2016 MapR Technologies 26© 2016 MapR Technologies 26

Comparing Data Platforms

S T O R A G E

D A T A

© 2016 MapR Technologies 27© 2016 MapR Technologies 27

H A D O O P

Comparing Data Platforms

D T A

C O M P U T E

D A T A

© 2016 MapR Technologies 28© 2016 MapR Technologies 28

Comparing Data Platforms

C O M P U T E

S P A R K

© 2016 MapR Technologies 29© 2016 MapR Technologies 29

Comparing Data Platforms

Issues: Enterprise Grade, Legacy Support, Integrated Analytics

C A S S A N D R A

D T A

C O M P U T E

D A T A

© 2016 MapR Technologies 30© 2016 MapR Technologies 30

Comparing Data Platforms

Issues: Enterprise Grade, Legacy Support, Integrated Analytics

C O M P U T E

D T AD A T A

Write In:–– –– –– –

O T H E R

© 2016 MapR Technologies 31© 2016 MapR Technologies 31

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 OptimizationGlobalSemi-conductorCompany

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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

© 2016 MapR Technologies 36© 2016 MapR Technologies 36

The Keys to Digital TransformationI need a better wrap-up graphic. I’ll also change

text.

2. Stream Processing

© 2016 MapR Technologies 37© 2016 MapR Technologies 37

Event-based Data Flows

web eventsetc…

machine sensorsBiometrics

Mobile events

© 2016 MapR Technologies 38© 2016 MapR Technologies 38

Streams Enable Real-Time Applications

FailureAlerts Real-time

application & network

monitoringTrending

now

WebPersonalized

OffersReal-time Fraud Detection

Ad optimizationSupply Chain Optimization

© 2016 MapR Technologies 39© 2016 MapR Technologies 39

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

© 2016 MapR Technologies 40© 2016 MapR Technologies 40

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.

© 2016 MapR Technologies 41© 2016 MapR Technologies 41

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

© 2016 MapR Technologies 42© 2016 MapR Technologies 42

Buffer & Save Hourly

FilesAggregate by minute Join

Batch Load

Batch Load

Streams Simplify Data Integration Pipelines

Analytics & BI

Filter Aggregate by Minute Join

© 2016 MapR Technologies 43© 2016 MapR Technologies 43

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

© 2016 MapR Technologies 45© 2016 MapR Technologies 45

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 theSystem of Record”

–Brad AndersonVP Big Data Informatics

© 2016 MapR Technologies 46© 2016 MapR Technologies 46

● Topics are logs

Logical ViewANATOMY OF A TOPIC

Partition 1

Partition N

C1P

PC2

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

© 2016 MapR Technologies 47© 2016 MapR Technologies 47

Streams Enable Event-based Microservices

Clicks Sessions Conversions

Sessionizer Converted?

© 2016 MapR Technologies 48© 2016 MapR Technologies 48

The Keys to Digital TransformationI need a better wrap-up graphic. I’ll also change

text.

3. Application Agility

© 2016 MapR Technologies 49© 2016 MapR Technologies 49

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.

© 2016 MapR Technologies 50© 2016 MapR Technologies 50

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

© 2016 MapR Technologies 51© 2016 MapR Technologies 51

Microservices Architecture

Message Broker

Processing Services

Processing Services

Message Broker

© 2016 MapR Technologies 52© 2016 MapR Technologies 52

Microservices Architecture

Message Broker

Processing Platform

Processing Platform

Streaming Data Platform

© 2016 MapR Technologies 53© 2016 MapR Technologies 53

Converged Microservices Architecture

MapR Converged Data Platform

ConvergedMessage and ProcessingServices

© 2016 MapR Technologies 54© 2016 MapR Technologies 54

Converged Platform Appeal for Developers: Agility

Flexibility

Agility

Simplicity

Real Time

© 2016 MapR Technologies 55© 2016 MapR Technologies 55

Appeal for Architects and Administrators

• Simplifies administration

• Avoids cluster sprawl

• Unifies security, data protection, disaster recovery

© 2016 MapR Technologies 56© 2016 MapR Technologies 56

Existing Environments – Complex Silos

© 2016 MapR Technologies 57© 2016 MapR Technologies 57

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

© 2016 MapR Technologies 58© 2016 MapR Technologies 58

Converged Data Platform

®© 2016 MapR Technologies 59®© 2016 MapR Technologies 59

The Keys to Digital Transformation

1. Data Convergence

2. Stream Processing3. Application Agility

®© 2016 MapR Technologies 60®© 2016 MapR Technologies 60

Thank You@norrisjack maprtech

jnorris@mapr.com

MapR

maprtech

mapr-technologies

®© 2016 MapR Technologies 61®© 2016 MapR Technologies 61

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