Free Your Analytics From Paralysis – Infrastructure Matters

40
0 Copyright 2016 FUJITSU Fujitsu Forum 2016 #FujitsuForum

Transcript of Free Your Analytics From Paralysis – Infrastructure Matters

Page 1: Free Your Analytics From Paralysis – Infrastructure Matters

0 Copyright 2016 FUJITSU

Fujitsu Forum 2016

#FujitsuForum

Page 2: Free Your Analytics From Paralysis – Infrastructure Matters

1 Copyright 2016 FUJITSU

Free Your Analytics From Paralysis – Infrastructure Matters

Gernot Fels

Head of Integrated Systems, Global Product Marketing, Fujitsu

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2 Copyright 2016 FUJITSU

Digitalization resounds throughout the land

Key benefits of digitalization

Business responsiveness

Customer retention and loyalty

Workforce productivity

Attract and retain talent

Digitalization is based on (big) data and its analytics

Know things you don’t know

Predict opportunities

Minimize risks

Take better and faster decisions

BUT:

Digitalization won’t work without data analytics.

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Big Data – what, why, how …

Volume

Variety

Versatility

Velocity

Value

New value by correlating data and new ways of analytics.

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Examples: What customers achieved …

Customer

Debt collection agency

Challenge

Identify 1.5M individuals not listed in payment plan

£2 billion of uncollected debt

Data analytics helped

Understand debtors (e.g. behavior, propensity to pay)

Create right payment plans

Outcome

Collect £12.7M within 9 months

Customer

Financial services provider

Challenge

How to retain more clients

Data analytics helped

Identify customer behavior

Develop effective contact strategy with correct up-sell message

Outcome

Increase customer value by 74%

Reduce churn by 17%

Making analytics pay Understanding each and every customer

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How to get from Big Data to Big Value?

A lot of questions …

Which use case?

Which business outcome?

Which data from which sources?

How to transform data into high quality?

What to look for?

Which questions to ask?

Which actions to trigger?

Which analytic methods?

How to visualize results effectively?

Which tools?

How to ensure data security and privacy?

What about data retention and deletion?

Creative ideas, deep knowledge of data, tools and intended results.

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Big Data Infrastructure Matters – If You Want to Avoid Paralysis

“There are organizations that feel paralyzed in their business operations. Analytics can help, but without the right infrastructure, paralysis will not go.”

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Big Data infrastructure: Challenges

Big Data changes the infrastructure conversation.

Exponential growth of

data

Keep processing time constant while volumes increase

Store and process large data volumes at affordable cost

Ad-hoc queries and real-time demands

Process continuously generated event streams

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Distributed Parallel

Processing

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Distribute data Move code to data Shared Nothing Scale-out on demand Data replication Affordable servers

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Dat

a in

mot

ion

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12 Copyright 2016 FUJITSU

Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Fast Response Platform

Result of pre-processing Smaller than initial data volumes All essential information Base for analytics

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

No random read / write Limited access possibilities

(D)FS

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Existing skills Structured data, row store Size affects response time Columnar store for acceleration

(D)FS

SQL DB

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Flexible data model (schema-less) Designed for being distributed Designed for scale-out Data replication

(D)FS

SQL DB

NoSQL

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

(D)FS

SQL DB

NoSQL

In-Memory Data and operations in RAM Real-time analytics Disk storage for persistence RAM mirroring

Dat

a in

mot

ion

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Dat

a at

res

t

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams Event Processing Platform

Dat

a in

mot

ion

Define rules (condition, action) Data streams flow through rules Latency, throughput Distributed engines (NW edge) Local decisions in real-time Fast

Response Platform

IoT

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Big Data infrastructure master plan

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Apps Services Queries

….

Visualization Reporting

Notification

Batch Processing Platform

Event Processing Platform

Dat

a at

res

t D

ata

in m

otio

n

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

Fast Response Platform

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How to Get to Your Infrastructure without Paralysis

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Building a data center infrastructure is complex

Tasks to be completed Select components from myriad of options

Procure, integrate

Sizing of CPU, RAM, storage, NW

Extensive testing (compatibility, bottlenecks)

Deep knowledge and skills to cope with technologies

Coordination among admins

Error-prone, time-consuming, risky, expensive

High maintenance effort

Is there a fast track to data center infrastructures?

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FUJITSU Integrated System PRIMEFLEX

Definition Pre-configured, pre-integrated and pre-tested

combination of data center components

Servers, storage, network connectivity, software

Characteristics Own and partner technologies

Optimally designed, based on best practices & experience

Traditional converged and hyper-converged

Ready-to-run and reference architectures

Long track record, many customer references

Addressing use cases of high relevance

Reduce complexity, time, risk and cost.

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FUJITSU Integrated System PRIMEFLEX – Your fast track to data center infrastructures

DESIGN - Select from myriad of SW and HW products

INTEGRATE - Assemble involving multiple admin domains

TEST - Work off complex test matrix

DEPLOY Integrate into production environment

Do-it-yourself approach Time to production

DEPLOY Integrate into production environment

Integrated Systems approach

DESIGN - Based on best practices

INTEGRATE - Pre-integrated single order delivery

TEST End-to-end quality assurance

Pro

ject

sta

rt

Solution Development in factory At customer's site

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PRIMEFLEX meets Big Data

Data Sources Analytics Platform Access

Extract, Collect Clean, Transform Decide, Act Analyze, Visualize

Consolidated data Distilled essence Applied knowledge Various data

Dat

a at

res

t D

ata

in m

otio

n

DB / DW

Text, mail

Sensors

Multimedia

Social, web

Click streams

PRIMEFLEX for SAP HANA

PRIMEFLEX for Hadoop

PRIMEFLEX for Hadoop

PRIMEFLEX for Hadoop

...

...

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PRIMEFLEX for Hadoop

Ready-to-run and reference architectures

Cluster of PRIMERGY RX / CX in various sizes

For storage- and processing-intensive tasks

Scale-out for coping with large data volumes

Automated integration of new components

Rapid modeling (collect, analyze, visualize)

Self-service (open by business users)

In-memory (RDD) for speed

Real-time stream processing

Configuration tool

Analytical apps / templates (free)

Datameer (Visual Analytics)

Hadoop (HDFS, MapReduce, …)

Linux OS

PRIMERGY RX / CX

Network switches

The easy way to get in touch with Big Data.

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PRIMEFLEX for SAP HANA

Single and multi node

Various sizes

1st SAP validated multi-node

Single node available as VM

TDI support

Components SAP-certified

HA / DR options

SAP-certified components

Addressing all use cases

ERP, BW, B1, S/4HANA, BW/4HANA

Your fast track to real-time insights.

Internal storage or ETERNUS JX ETERNUS DX, NetApp FAS

NW switches

Single Node Multi-Node

SAP HANA DB

Linux

VMware vSphere

PRIMERGY RX / PRIMEQUEST certified for SAP HANA

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SAP HANA Vora™

What it is

In-memory query engine

Plugs into Apache Hadoop and Spark

Interactive analytics on data in Hadoop data lakes

Off-load data to Hadoop data lakes

How Fujitsu can help

PRIMEFLEX for SAP HANA

PRIMEFLEX for Hadoop optimized for SAP HANA Vora™

PRIMEFLEX for SAP HANA and PRIMEFLEX for Hadoop complement each other.

PRIMEFLEX for SAP HANA

PRIMEFLEX for Hadoop

“Voracious” HANA user

SAP HANA Vora™

Data lakes

Bridge between HANA and Hadoop

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PRIMEFLEX: Supported by flexible service options

Consulting

Design (configuration & sizing)

Deployment

Integration into production environment

Additional services (e.g. migration)

Lifecycle management

Solution Support

DC Services (Managed DC, Managed Hosting)

Any guidance and support you need.

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What PRIMEFLEX customers have achieved

80% reduction in provisioning new services

5x faster time to market

5x more services deployed per year

Cluster setup within 30 min instead of 3 days

99% reduction in setup time

72x faster to operation 70% reduction in deployment cost

30% reduction of infrastructure cost

35% less annual operation expenses

30% savings on overall IT budget

25% reduction in power and cooling

40% reduction in DC footprint

45% improvement in resource utilization

90% reduction in unplanned downtime

40% less time needed for system maintenance

60% of IT shifted from maintenance to innovation

From 0 to VM within 10 min

Figures may vary across PRIMEFLEX line-up and projects

2x more productivity and revenue

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HPDA (High Performance Data Analytics)

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HPDA (High Performance Data Analytics)

Typical characteristics

Structured data

Single source

Smaller volumes

Demand for high computing performance

Modeling and simulation: Objectives

Validate theories and designs

Build & test virtual prototypes

Increase prototyping coverage and quality

Use cases across industries

Crash simulation (automotive)

Testing new materials (construction and building)

Drug development against cancer (pharmaceutical)

Exploring cosmic origins and the universe

Complex scientific, engineering, analytics tasks to solve.

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HPC – Foundation for HPDA

Working principle

User defines jobs using ISV software

Handover job to head node

Head node distributes jobs to compute nodes

Parallel execution of jobs on compute nodes

Compute nodes return results to head node

Head node returns overall result to user

Parameters affecting cluster size

Size of object (model)

Mesh size (accuracy of model)

Expected processing time

Middleware to bring cluster to life and make HPC accessible

LAN

Shared storage (Parallel File System)

Head node (management)

Man

agem

ent

NW

(IP

)

Hig

h-s

pee

d N

W (

IB)

Few to 1000s of compute nodes (job execution)

Job request

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Parallel MW Scientific Libraries

Compilers, other tools

Parallel FS

Co-processor support (GPGPU and XEON Phi)

OS (RHEL, CentOS) and drivers

HPC Gateway

Workload Manager

Cluster deployment & management

Servers (PRIMERGY RX, CX, BX)

Network Switches (IB)

Storage (ETERNUS DX) H

PC C

lust

er S

uit

e (H

CS)

...

PRIMEFLEX for HPC: Addressing HPDA

Various flavors

Ready-to-run & reference architectures

With and without apps pre-installed

Intel Cluster Ready certified

HPC Gateway = simplification of HPC

Desktop-like look & feel

HPC accessible for non-IT users

Job submission from hrs to min

Multi-app support

Multi-cluster locations

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Analytics and Cloud

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Big Data and HPDA: Delivered from the Cloud?

Cloud is the answer to infrastructure on demand

Ever increasing data volumes (Big Data)

Varying performance needs (HPDA)

Infrastructure alternately used for Big Data and HPDA

Infrastructure not permanently needed (at full extent)

Public or private or … ?

Cloud can be the foundation for Big Data and HPDA.

Public Cloud

Hybrid Cloud

Private Cloud

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PRIMEFLEX for Red Hat OpenStack

Reference architecture

Private Cloud IaaS

Based on Red Hat Linux OpenStack

Optional upper layer cloud management SW (Catalog / Workload / Cloud Watch Manager)

Optional hyper-scale ETERNUS CD10000

Deliver (Big Data / HPC) infrastructure on demand

Take advantage from design of PRIMEFLEX for Hadoop / SAP HANA / HPC

Deploy flexible, open, cost-effective private cloud platform in most reliable way.

RHEL OpenStack Platform

Hypervisor

Apps

Upper layer cloud management

PRIMERGY CX / RX

Network switches

ETERNUS CD / DX

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Summary

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Summary

Data analytics is key prerequisite for digital transformation

Changing the way companies make decisions, do business, succeed or fail

Infrastructure matters

Various concepts for different use cases

PRIMEFLEX – Fast track to DC infrastructures

Big Data

HPDA

Cloud

End-to-end services

Sourcing options

Free your analytics from paralysis? Have a word with FUJITSU.

Experience across

industries

Choice Digitalization with confidence

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Let’s have a look at our PRIMEFLEX video

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