MapR– PentahoBusinessSolutionsTheBenefitsofaConvergedPlatformtoBigDataIntegration

TomScurlockDirector,WWAlliancesandPartners,MapR

KeyTakeaways

1. We“focusonbusinessvalues”and“businessoutcomes,”PentahoandMapRtechnologiesfollowthesolutions.

2. Businesstransformationiscomplex. Companiesneedtoevaluatehowdatacanbeusedtogeneraterevenuestreamsusingaconvergeddataplatform.

3. Companiesthatinvestinoneconvergeddataplatformforlargedatavolumes,varietyandvelocitywillincreaserevenues,businessagility,productivityintoday’sdigitaleconomy.

Agenda

• Whythedatausedinbusinesstransformationiscomplex?

• Manypointsofsolutionsonmultipleclusters

• MapR – Convergeddataplatform

• Infrastructure:– Optimizedresourcesconsumption– MapR buildsabreakthroughtechnology

• Datareferencearchitecture

• Nextgenerationofenterpriseapplications:– Enabletransformationthroughconvergedapplications– ’To-Be’enterpriseapplicationsarchitecture– PentahoAndMapR solutions

WhytheDataUsedInBusinessTransformationisComplex?

Dataresidesinmultipleplaces

Changingregulatory

requirements

Dataismessyandincomplete

Dataisstructured,Semi-StructuredandUnstructured

Dataisinconsistent

ANALYTICALAPPLICATIONS

Businessinsight

OPERATIONALAPPLICATIONSBusinessperformance

NextGenerationofEnterpriseConvergedApplications

Next-Gen Applications

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

BusinessTransformationChallenges

ApplicationIntegration

B2BIntegration

FileIntegration ProcessIntegration

DataConsumption

DataSynchronization DataIntegration

MarketForces

Cloud Mobile

E-Commerce

Big Data

IoT

IndustryInitiatives

Fraud Detection in Real Time

360 Customers Visibility

Predictive Maintenance

Smart Logistics

Omni-ChannelOptimization

Hadoop&SparkCluster

DocumentDB

ClassicDataWarehouse

NoSQL

ApplicationServer

MessageMiddleware

IBMMainframe

ExpensivetoStitch|Fragile|LimitationsforSpeed,Scale,Reliability

JSON API

ODBC

JMSHBASE API

REST-APIODBC/JDBC

C-Levels

HDFS API BUSINESSCONCERN:

• DATABECOMESEXPENSIVE

• I/OSPEED• SCALE/PERF.• SECURITY• H.A.• COMPLEXITY• SINGLETENANT• CLUSTERSPRAWL

IT Budget

ESB / Data Integration Platform

ConvergedDataPlatformonASingleCluster

EngineeredassingleplatformFiles,Tables,Documents,andStreams

Enterprise-GradeCapabilitiesRunsatalowercost

ConvergedapplicationdevelopmentplatformScale-outtoanyworkload

“Connected”,notconvergedSeparatesolutionsperdatatype

OperationalconcernsExpensivetooperate

Datareplication,datamovementLimitedinscale

Hadoopcluster

Streamprocessing

Classicdatawarehouse

Messagingplatform

NoSQLdatabase

DocumentDatabase

Searchserver

ManyPointSolutionsonMultipleClusters

© 2017 MapR Technologies 10

MAPR CONVERGED DATA PLATFORM

Data Center

CONVERGED DATA PLATFORMHigh Availability Real Time Unified Security Multi-tenancy Disaster Recovery Global Namespace

EVENT DATA STREAMS

ANALYTICS & MLENGINES

OPERATIONAL DATABASE

CLOUD-SCALE DATA STORE

ON-PREMISE, IN THE CLOUD, HYBRID

Existing Enterprise Applications

Intelligent ApplicationsBatch & InteractiveAnalytics

IoT/Edge

ComplexEventProcessinginReal-TimeBusinessRulesEngine,ProcessAutomation,IdentifyAlerts,Patterns.

MapR– EnterpriseConverged DataPlatform

CONVERGEDDATAPLATFORMHighAvailability RealTime UnifiedSecurity Multi-tenancy DisasterRecovery GlobalNamespace

EVENTDATASTREAMS

ANALYTICS&MLENGINES

OPERATIONALDATABASE

CLOUD-SCALEDATASTORE

ContainerApps. OperationalIntelligenceML,StreamingAnalytics

EventEnabledIntelligentApplications

Data Producers

LegacyApps.(ERP,CRM,BI)

HDFSAPI POSIX,NFS HBaseAPI JSONAPI KafkaAPIJDBC/ODBC

On-Premise, In the Cloud, Hybrid

Smart Logistics

Fraud DetectionIn Real Time

Predictive Maintenance

Supply Chain Visibility

Omni-ChannelCustomer Engagement

(PUB/SUB)

RESTAPI

OptimizedResourceConsumption

Linux File System(general purpose, slower than MapR-FS,

leaves HA up to other engines)

Storage Hardware

HDFS (append-only)

Java Virtual Machine

HBase(excessive writes)

Java Virtual Machine

Storage Hardware

Every layer contends for more CPU and memory

Efficient architecture frees up resources: shared HA, DR, and I/O systems

Java Virtual Machine

Kafka(separate cluster)

X

Replace with speed, connectivity, HA/DR

Replace with less I/O and RAM consumption

Eliminate layer

Replace with fullread-write

MapR-FS + MapR-DB + MapR Streams

Fast, Efficient, Direct I/O

Eliminate layer

X

Hadoop: MapR:

OptimizedforSpeed

Supportsparallelprocessingoflargescaleanalyticsandmachinelearningacrossdata.

MapR– BuiltwithBreakthroughTechnologyInnovativearchitecturedeliversuncompromisingscale,speedandavailability

OptimizedforAvailability

Providesadvancedcapabilitiesincludingself-healinganddisasterrecoverytosupportcontinuousdataaccess.

OptimizedforScale

Enableshighscaleprocessingbyorganizingunderlyingdataintolargedistributedcontainerstoscaletotrillionsoffiles.

MapR DataReferenceArchitecture

ANALYTICALAPPLICATIONS

Businessinsight

OPERATIONALAPPLICATIONSBusinessperformance

NextGenerationofEnterpriseConvergedApplications

Next-Gen Applications

CompleteAccesstoReal-timeandHistoricalDatainOnePlatform

CentralDataProcessing&Aggregation

Ad-hocanalysis

OtherDataSources

Real-timeanalysis

Reporting

Streaming

Stream

Topic Replicating

DataCentersWorldwide

Stream

Topic

Stream

Topic

Data Ingesting

‘To-Be’EnterpriseApplicationArchitecture:GloballyDistributed

StageBusinessAgility:Accelerateslarge scale deployment.

StageInfrastructure Agility:Offers ETLOffloadingAnddataoptimization toreduceTCO

StageApplications Agility:Deliversevent-enabled apps.,monitoringbusiness decisions inreal-time.

MapR - ConvergedDataPlatform

JourneywithMapRConvergedDataPlatform

BusinessIntelligence:Re-useofexisting BIapps.(Tableau,MicroStrategies,Cognos,Crystal,Custom Apps.,etc).

Next-GenApps:Operational Intelligence,Streaming Analytics,MachineLearning,IoT Predictive Maintenance.

On-Prem/Cloud:DataReplication,HAbetween datacentersinreal-time.

Micro-Services: Docker,Containers,Images,Kubernates,Docker Swam.

DataFabric:Fast TCOreduction indataappliances andservers,DataOff-loading &Optimization.

Adop

tion+Bu

sine

ssValue

Benefits:

• Unified Files,Tables,Events Streamsunder one admin.

• On-Premise,Any Clouds• Next GenApps,Micro-Services• DataNormalization for structure,semi-

structure &non-structure datafrommultiple APIsources

• Multi-tenancy• Supports Hadoop Distributed File

System &Eco-System.• Shareresources• DataLocality• Supports No-SQL,HBASEDB• Massive Parallel Processing• Fully Read/Write inreal-time• ExtremeDataScalability,HA,Security,

HighPerformance&Relibility.• Geo-Replication

1

2

3

AnalyticalDatabase

ModalityData

DNAVariant

ClinicalData

PatientSatisfaction

EHR/EMR

PACS/Imaging

Administrative

FinancialData

CloudAnnotation

PentahoandMapRDataintegration,orchestrationandanalytics

ConvergedDataPlatform

ValidateCleanse

Standardize

Blend&Ingest

MapReduceSpark

MachineLearning

Process&Refine

AnalyzeOptimize

Orchestrate

Deliver

Redshift

VirtualizedData

Reports Discovery

Visualization Predictive

DataasaService

EmbeddedApplications