The Keys to Digital Transformation

© 2016 MapR Technologies 1© 2016 MapR Technologies 1

®

© 2016 MapR Technologies

The Keys to Digital Transformation

© 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?


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 $


You Need to Make a Decision Now

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

Source


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


$$

$


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)


$$$


Largest Biometric Database in the World

PEOPLE


Driving Business Value

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


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


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


Applications Dictate Data – Results in Silos


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

text.

1. Data Convergence


The Secret is to Bring Analytics & Operations Together

Converged Applications

Complete Access to Real-time and Historical Data in One Platform

Historical Immediate


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


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)


Data Platform Requirements

Integrated Analytics

Enterprise Grade

Scale

Legacy Support

Transformational Applications

Real Time


Comparing Data Platforms

S T O R A G E

D A T A


H A D O O P


D T A

C O M P U T E

D A T A



C O M P U T E

S P A R K



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



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


C O N V E R G E D D A T A P L A T F O R M


Data in MotionData at Rest

C O M P U T E

D T AD A T A


Yield Management OptimizationGlobalSemi-conductorCompany


National Oilwell Varco (NOV)


Managing Risk/FraudAmerican Express

Fraud protection on $1 trillion

Decisions made in less than 2 milliseconds


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



text.

2. Stream Processing


Event-based Data Flows

web eventsetc…

machine sensorsBiometrics

Mobile events


Streams Enable Real-Time Applications

FailureAlerts Real-time

application & network

monitoringTrending

now

WebPersonalized

OffersReal-time Fraud Detection

Ad optimizationSupply Chain Optimization


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


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.


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


Buffer & Save Hourly

FilesAggregate by minute Join

Batch Load

Batch Load

Streams Simplify Data Integration Pipelines

Analytics & BI

Filter Aggregate by Minute Join


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


Flexible Communication Modes

Consumer Group

C

One to One Many to Many

Orders Clicks

P P P

C CC


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


● 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


Streams Enable Event-based Microservices

Clicks Sessions Conversions

Sessionizer Converted?



text.

3. Application Agility


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.


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


Microservices Architecture

Message Broker

Processing Services

Processing Services

Message Broker


Microservices Architecture

Message Broker

Processing Platform

Processing Platform

Streaming Data Platform


Converged Microservices Architecture

MapR Converged Data Platform

ConvergedMessage and ProcessingServices


Converged Platform Appeal for Developers: Agility

Flexibility

Agility

Simplicity

Real Time


Appeal for Architects and Administrators

• Simplifies administration

• Avoids cluster sprawl

• Unifies security, data protection, disaster recovery


Existing Environments – Complex Silos


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


Converged Data Platform

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

The Keys to Digital Transformation

1. Data Convergence

2. Stream Processing3. Application Agility


Thank You@norrisjack maprtech

[email protected]

MapR

maprtech

mapr-technologies


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