0 Copyright 2016 FUJITSU
Fujitsu Forum 2016
#FujitsuForum
1 Copyright 2016 FUJITSU
Free Your Analytics From Paralysis – Infrastructure Matters
Gernot Fels
Head of Integrated Systems, Global Product Marketing, Fujitsu
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.
3 Copyright 2016 FUJITSU
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
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.
6 Copyright 2016 FUJITSU
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
8 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
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
10 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
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
11 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
Dat
a in
mot
ion
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
13 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
No random read / write Limited access possibilities
(D)FS
Dat
a in
mot
ion
14 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
Existing skills Structured data, row store Size affects response time Columnar store for acceleration
(D)FS
SQL DB
Dat
a in
mot
ion
15 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
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
16 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
(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
17 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 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
18 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
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?
21 Copyright 2016 FUJITSU
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.
22 Copyright 2016 FUJITSU
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.
28 Copyright 2016 FUJITSU
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
29 Copyright 2016 FUJITSU
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
…
32 Copyright 2016 FUJITSU
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
33 Copyright 2016 FUJITSU
Analytics and Cloud
34 Copyright 2016 FUJITSU
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
35 Copyright 2016 FUJITSU
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
36 Copyright 2016 FUJITSU
Summary
37 Copyright 2016 FUJITSU
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
38 Copyright 2016 FUJITSU
Let’s have a look at our PRIMEFLEX video
39 Copyright 2016 FUJITSU