IBM Big Data 2014

DW Modernization

© 2014 IBM Corporation

모든 기업이 정보계 시스템을 가지고 있습니다.

© 2014 IBM Corporation2

그러면 Data Warehouse의 Modernization

무엇이라 생각하십니까?

인메모리 DB, Stream Computing, Appliance 및 하둡 등 새로운 기술을접목하여 DW system의 기능을 강화하는 것입니다.

DW Modernization이란 ?

Actionable insight

Deep

Data types

+

Real-time processing & analytics

Machine andsensor dataImage and

Decision

management

© 2014 IBM Corporation3

Information Governance

Exploration, landing and

archive

Trusted dataReporting & interactive analysis

Deep analytics

& modeling

+

+Transaction andapplication data

Enterprise content

Social data

Image and video

Third-party data

Predictive

analytics

and modeling

Reporting,

analysis,

content

analytics

Discovery and

exploration

Operational systems

Information

Integration

Data

Matching &

MDM

Security

&

Privacy

Lifecycle

Managemen

t

Metadata &

Lineage

데이터 유형이 다양화되고 DW 환경에 대한 최적화 및 DW 시스템의 TCO 절감이 요구됩니다.

빅데이타 이전의 DW 현재의 DW

트랜잭션 크기 Large Larger and growing

데이터 크기 Small to medium Medium to large

데이터 소스 Few Many

DW 환경이 변하고 있습니다.

© 2014 IBM Corporation4

데이터 보관Regulatory and only necessary data

Everything

데이터 접근Some data on-line with restore from off-line

Always on-line

분석의 레벨 Aggregated and summarizedActual data for individual customers

Analysis possibilities Infer from summary dataCompare individuals with others in same or similar demographic groups

실시간 분석을 위한 비용

High to prohibitiveAffordable (probably essential for consumer products)

모두 “YES”라면 DW Modernization이 첫걸음입니다.

• 운영 효율성을 위해 빅데이타와 DW 기능을 통합하고자 합니까?

• 저장, 관리 및 라이센스 비용을 최적화하기 위해 거의 사용되지않는 데이터를 하듭과 같은 새로운 기술를 이용하여 관리하고자합니까?

© 2014 IBM Corporation5

• 스토리지 비용을 절감하기 위해 스트림 컴퓨팅을 이용하고있으신지요?

• 보다 정교한 분석을 위해 정형, 비정형 및 스트림 데이터를 통합분석하시나요?

• 성능을 저하시키고 관리 비용을 높이는 Cold 데이터 또는 거의조회되지 않는 데이터가 많으신가요?

DW Modernization은 빅 데이터 기술을 활용합니다.

Pre-Processing Hub Query-able Archive Exploratory Analysis

Information Integration

StreamsReal-time processing

BigInsightsLanding zone for all data

BigInsights

Can combine with

unstructured information

1 2

Find and view the data

Data Explorer

Data Explorer

3

© 2014 IBM Corporation6

Integration

Data Warehouse

Data Warehouse

Data Warehouse

6

BigInsights

StreamsOffload analytics for microsecond

latency

6 © 2013 IBM Corporation

DW Modernization를 위한 주요 기능 및 Value

DW Modernization의 주요 기능 Business / IT Value

Core Hadoop Cost effective and infinitely scalable

Advanced File System Enterprise grade resilience and performance

Visualization and Exploration Accelerated time-to-value for analytics

Streaming Analytics In motion data analysis at any scale

Text, Machine and Social data Accelerated ROI with new data types

Data Explorer adds

value immediately

© 2014 IBM Corporation77

Text, Machine and Social data Accelerated ROI with new data types

Realtime Data Replication Instant data updates from OLTP applications

Self-Service Data Movement Simpler tools for moving data in/out

ELT/ETL Transformations Empowers “Hadoop Data Hub” architecture

Data Cleansing and Matching Greater confidence and trust in analytics

Security and Privacy Active monitoring and masking of sensitive data

BigSQL for Data Access Structured data access from within Hadoop

Comprehensive Archival Immutable, verifiable and compressed data

Guardium runs

native in Hadoop

DataStage generates

MapReduce XForms

IBM Big Data 및 분석 아키텍처 - IBM Watson Foundation

실시간분석Zone

전사 DW, 마트 및

어플라이언스

zone탐색 및

다양한데이터수집,

운영데이터

All Data신규/향상된

어플리케이션

무슨 조치를취해야만하는가?

어떤 일이일어나고있는가?

Discovery andexploration

왜 그러한 일이발생했는가? Cognitive

IBM Watson Foundations

© 2014 IBM Corporation8

스zone

데이터 거버넌스 zone

탐색 및적재, 보관

zone

운영데이터Zone

어떤 일이발생할 수있을까? ? Predictive analytics

and modeling

취해야만하는가? Decision

management

발생했는가? Reporting, analysis,

content analytics

CognitiveFabric

Systems Security

On premise, Cloud, As a service

Storage

IBM Big Data & Analytics Infrastructure

Actionable insight

IBM Big Data 분석을 위한 오퍼링

Deep analytics & modeling

Data typesINFOSPHERE STREAMS & INFOSPHERE DATA REPLICATIONINFOSPHERE STREAMS & INFOSPHERE DATA REPLICATION

Real-time processing & analytics

Machine andsensor data

Image and video

Predictive

Decision management

INFOSPHERE INFOSPHERE BIGINSIGHTSBIGINSIGHTS

DB2, INFORMIXDB2, INFORMIX

SPSS MODELERSPSS MODELER

SPSS MODELER SPSS MODELER GOLDGOLD

PUREDATA PUREDATA ANALYTICSANALYTICS

DB2 DB2 WAREHOUSEWAREHOUSE

PUREDATA PUREDATA

© 2014 IBM Corporation9

INFORMATION SERVER, MDM, G2, GUARDIUM, OPTIMINFORMATION SERVER, MDM, G2, GUARDIUM, OPTIMInformation governance

Exploration, landing and

archive

Trusted dataReporting & interactive analysis

modeling

Transaction andapplication data

Enterprise content

Social data

Third-party data

Predictive analytics

and modeling

Reporting, analysis, content

analytics

Discovery and exploration

Operational systems

COGNOS BICOGNOS BICOGNOS TM1 COGNOS TM1

DATA EXPLORERDATA EXPLORERSPSS ANALYTIC SPSS ANALYTIC

CATALYSTCATALYST

DB2 BLUDB2 BLUPUREDATA PUREDATA ANALYTICSANALYTICS

PUREDATA PUREDATA OPERATIONAL OPERATIONAL

ANALYTICSANALYTICS

DW Modernization을 통해 기업은…

�다양한 데이터 소스와 결합을 통한 분석

�DW 환경의 최적화

�복잡하고 정교한 분석이 가능한 성능

© 2014 IBM Corporation10

�실시간 의사 결정이 가능한 Business Insight

�DW 시스템의 TCO 절감

�복잡하고 정교한 분석이 가능한 성능

Next Steps.

새로운 유형의데이터 분석이필요합니다.

기존 DW 환경을최적화하고싶습니다.

DW 시스템에 대한TCO를 절감하고

싶습니다.

© 2014 IBM Corporation11

IBM CVE 진단 Workshop을 통해

DW Modernization을

시작하십시오.

CVE 진단 워크샵 개요

ClientClient--Value Engagement (CVE) : Value Engagement (CVE) : 고객고객 측면의측면의 Value Value 산출산출

Identify Identify

ChallengesChallenges

Identify Identify Technical & Technical & Business Business ChallengesChallenges

Determine Determine

CostsCosts

Determine Determine Current Current (As(As--Is)Is)CostsCosts

Determine Determine

CostsCosts

Determine Determine Future Future (To(To--Be) Be) CostsCosts

Technical Technical Solution Solution BlueprintBlueprint

CVE CVE CVE CVE Final Final

ResultsResults

기술적/업무적인 요건 파악(Problems/Challenges)

향후 개선 방안 및 비용절감 을 위한 솔루션 제안

CVE 분석 보고서작성 및 보고

현재 Process 및 소요비용 정의 측정 결과에 따른

© 2014 IBM Corporation12

현재 Process 및 소요비용 정의 측정 결과에 따른As-Is & To-Be 비용 비교 (ROI)

78,000 78,000 7,800

65,000 65,000 6,500

39,000 39,000 35,100

182,000 182,000 49,400Totals

Heavy Users

Medium Users

Light Users

Queries/YrQuery Wait Time / YearTotal Min

All Queries

Total Min

All Queries

End User Avg Wait Time / Query

Comparison

1.00 1.00 1.00

0.10 0.10

0.90

Heavy Users Medium Users Low Users

Min

ute

s

Current Process PureData for Analytics

정형화된 질문 (엑셀 장표) : 고객 인터뷰 진행

ROI 산출(3,5년 기준)

CVE 총 2주 ~ 3주 소요(고객 투자 : 주당 1~2 시간 )

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