This slides describe diversified analytic solutions based on Spectrum Scale with various deployment mode, such as storage rich-server, share storage, IBM DeepFlash 150 and Elastic Storage Server. It deep dives several advanced data management features and solutions for BD&A workload derived from Spectrum Scale.

1. #ibmedge© 2016 IBM Corporation
A Diversified Analytic Solution
Based on Various Storage
Services
Sep 21, 2016

2. #ibmedge
Agenda
• Spectrum Scale Deliveries for Data Analytics
• Spectrum Scale HDFS Transparency Hadoop Connector
• Advanced Data Management and Protection for Analytics
• Spectrum Scale Data Analytics Deployment Models
• Solutions dedicated to Client’s Success
1

3. #ibmedge
Hadoop Data Analytics with IBM Spectrum Scale
• IBM Spectrum Scale is a
proven enterprise ready
alternative of HDFS for
Hadoop data analytics
• Completely compatible with
HDFS client API and
Hadoop applications
• Unified File, Object and
HDFS access interface
• Mature enterprise features
make it easy to manage and
protect data
2

4. #ibmedge
IBM Spectrum Storage and Computing for Data Analytics
• IBM Spectrum Storage family and
Spectrum Computing provide
integrated solution for Data
Analytics workload.
• Mature enterprise class and proven
features in Spectrum Storage
provides rich of data management,
monitor, security enhancement.
• Spectrum Computing provide high
performance and cost effective
services for Data Analytics
workload.
3
IBM Spectrum Control
IBM Spectrum Protect
IBM Spectrum Archive
IBM Spectrum Virtualize
IBM Spectrum Accelerate
IBM Spectrum Scale
IBM Spectrum Conductor
IBM Spectrum Conductor
With Spark
IBM Spectrum LSF
IBM Spectrum Storage IBM Spectrum Computing

5. #ibmedge
Spectrum Scale - Unified Software Defined Storage
4
Unified Scale-out Data Lake
• File In/Out, Object In/Out; Analytics on demand.
• High-performance native protocols
• Single Management Plane
• Cluster replication & global namespace
• Enterprise storage features across file, object & HDFS
Spectrum Scale
Expand to other Storage
Systems
(DeepFlash 150 or others)
Expand to server
local disk drives
……
FDR/QDR IB or 10/40 GBE
POSIX File-
System APISMB
Windows File
Sharing
NFS Object iSCSI
Linux / UNIX
File Sharing
Web & Swift / S3
Compatible
Block Storage
(Experimental)
Protocol Nodes
managed by
CES Framework
General
Applications
Elastic Storage
Server
HDFS
Transparency
External Hadoop /
Spark Cluster
And Cloud
Spectrum Scale
TCT

6. #ibmedge
IBM Elastic Storage Server and IBM Data Engine
5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
IBM Elastic Storage Server (ESS) is Powered by IBM Spectrum Scale RAID
delivers:
Analytics
• ESS
• Spectrum Computing
• IOP with Hadoop & Spark
• Watson Explorer
Hadoop &
Spark
• Spectrum Scale
• Spectrum Symphony
• Platform Cluster Manager
• IOP with Hadoop and Spark
NoSQL
• CAPI enabled Power System
SAP NANA
• ESS Certified
• IBM Power Certified
IBM Data Engine for:
 Optimal Solution for All Data Needs
 Single repository of data with unified file, Hadoop and object support
 Highest Reliability
 Mean Time to Failure many orders of magnitude longer than the life of
the hardware
 Highest Performance
 Faster than rated disk speeds
 Cost Effectiveness
 Less then 1 cent per GB per month

7. © 2016 IBM Corporation #ibmedge 6
Spectrum Scale HDFS
Transparency Hadoop
Connector

8. #ibmedge
We are transforming
• Storage & Compute integrated in the same node
• Compute node has to access Spectrum Scale
file system directly
• Build system from scratch
7
Hadoop Client
Hadoop
FileSystem API
Connector on
Spectrum Scale
API & POSIX
Spectrum Scale
Node
Hadoop Client
Hadoop
FileSystem API
Connector on
Spectrum Scale
API & POSIX
Node
Hadoop Client
Hadoop
FileSystem API
Connector on
Spectrum Scale
API & POSIX
Node
Hadoop Client
Hadoop
FileSystem API
HDFS Client
Node
Hadoop Client
Hadoop
FileSystem API
HDFS Client
Node
Hadoop Client
Hadoop
FileSystem API
HDFS Client
Node
HDFS RPC over network
Spectrum Scale
Connector Service
Node
Connector on
Spectrum Scale API
& POSIX
Spectrum Scale
Connector Service
Node
Connector on
Spectrum Scale API
& POSIX
Spectrum Scale
 Dedicate Hadoop compute cluster
 Compatible with more Hadoop components
and features, such as Impala, distcp, webhdfs
and so on.
 Enhanced security. Full Kerberos support.
 Improved performance. Leverage HDFS client
cache.

9. #ibmedge
Based on Hadoop RPC
8
Integrated system for data analytics
• Seamless supports Hadoop application without any changes
• Not necessary to mount file system in compute node
• Leverage HDFS client cache to improve performance and security
HDFS RPC Similar FileSystem method Implemented By
------------------------------------------------------------------ ------------------------------------------ ----------------------
rpc getBlockLocations(GetBlockLocationsRequestProto); getFileBlockLocations Spectrum Scale API
rpc getServerDefaults(GetServerDefaultsRequestProto); get default value from configuration file Spectrum Scale API
rpc create(CreateRequestProto)returns(CreateResponseProto); create POSIX I/O Interface
rpc append(AppendRequestProto) returns(AppendResponseProto); append POXIX I/O Interface
rpc setReplication(SetReplicationRequestProto) setReplication Spectrum Scale API
rpc setPermission(SetPermissionRequestProto) setPermission Spectrum Scale API
rpc setOwner(SetOwnerRequestProto) returns(SetOwnerResponseProto); setOwner Spectrum Scale API
rpc setSafeMode(SetSafeModeRequestProto) HDFS specific, can skip
……
……

10. #ibmedge
Data Integration with HDFS
9
https://hadoop.apache.org/docs/r1.2.1/distcp.html https://hadoop.apache.org/docs/r2.7.2/hadoop-project-dist/hadoop-hdfs/Federation.html
distcp
•Use MapReduce for large inter/intra-cluster data copying
between native HDFS and Spectrum Scale
•Use the same semantic as copying data between HDFS
•Locality awareness and data is also balanced
Federation
•Spectrum Scale storage can be co-exist with native
HDFS storage to service the same Hadoop workload
•Spectrum Scale storage can be added to exsiting HDFS
storage without any change.
•viewFs is invoked to provide single namespace view

11. © 2016 IBM Corporation #ibmedge 10
Advanced Data
Management and
Protection for Analytics

12. #ibmedge
Locality Awareness Data Recovery
11
• Map/Reduce jobs
• Map/Reduce job comprises of tasks and each task will process
one HDFS-called data block
• Disk failure or node failures won’t impact much on data locality in
native HDFS
• Hbase Hfile data locality
• If one node fails, the Hbase region server over that node is down
• The region server file will be assigned to other region server for
service
• The data locality of region files can only be restored when major
compaction happens if taking native HDFS
• Hive table files
• In native HDFS, no easy way to restore/control the hive table file’s
data locality
• Easy data locality restore in Spectrum Scale
• WADFG/mmchattr/mmresripefile
A B C D
Node A has all data
for Hbase region /
Hive hfile for better
performance
X
A B C D
When node A fails,
data have to migrate
to other node to
keep data safe
√
A B C D A B C D
√
Spectrum Scale
locality awareness
data recovery
HDFS natively won’t
recovery data to
original node

13. #ibmedge
Rack-awareness Data Locality for Shared Storage, IBM
DeepFlash and ESS
12
Benefit for:
• More than 1 rack in the file system
• One shared storage(or IBM DeepFlash and
ESS) per rack
• Top of Rack switch network
Advantages:
• Map/Reduce could be scheduled to the rack
where the input data are located
• Reduce the network traffic for inter-rack switch
• Performance gain from fast reading from local
rack
<property>
<name>gpfs.storage.type</name>
<value>rackaware</value>
</property>

14. #ibmedge
Transparent Data Compression
• Transparent compression for HDFS
transparency, Object, NFS, SMB and
POSIX interface.
• Improved storage efficiency
• Typically 2x improvement in storage efficiency
• Improved i/o bandwidth
• Read/write compressed data reduces load on
storage
• Improved client side caching
• Caching compressed data increases apparent
cache size
13
Traditional
Application (POSIX)
Spectrum Scale Node
Native Compression
Block Device / NSD
Disk
Object/NFS/SMB
HDFS Transparency
Vision
• Per file compression
• Use policies
• Compress low use data

15. #ibmedge
Transparent Data Encryption
• Native Encryption of data at rest
• Files are encrypted before they are stored
on disk
• Keys are never written to disk
• No data leakage in case disks are stolen
or improperly decommissioned
• Secure deletion
• Ability to destroy arbitrarily large subsets
of a file system
• No ‘digital shredding’, no overwriting:
Security deletion is a cryptographic
operation
• Use Spectrum Scale Policy to encrypted (or
exclude) files in fileset or file system
• Generally < 5% performance impact
14
Sequential Read Sequential Write
Traditional
Application (POSIX)
Spectrum Scale Node
(Encryption/Decryption)
IBM Security Key
Lifecycle Manager
Vormetric Key
ManagerBlock Device / NSD
Disk
Object/NFS/SMB
HDFS Transparency

16. #ibmedge
Spectrum Scale for Hadoop Security and Audit
• Spectrum Scale HDFS Transparency + IBM
Security Guardium Data Activity Monitor
• Spectrum Scale enables IBM Security Guardium
Data Active Monitor for Hadoop data access Audit
• Spectrum Scale HDFS Transparency support
Ranger
• Ranger is open source and used for security/audit
• IBM IOP 4.2 includes this component.
• Spectrum Scale HDFS Transparency fully
supports Kerberos
• ACL
• Supports NameNode Block Access Token
15
Spectrum Scale
Ranger
HDFS Transparency
Hadoop Platform
YARN MapReduceSpark
HBase
Zookeeper
Flume
Hive Pig
Sqoop
HCatalog
Solr/Lucene
kafka

17. #ibmedge
Transparent Cloud Tiering
Expanding storage to the cloud
| 16
• Archive to object or tape
• Tier to Spectrum Archive incl. dual copies for long term
retention
• Tier to Cleversafe as an option for disk at lower TCO
• Cognitive engine to move data based upon ‘heat’
or policy, even across the Internet
High capacity tiered storage, mixed data access
requirements for high-performance & retention
• Media storage for raw, processing, storage
• Digital surveillance for ingest, plus retention
• Technical Computing for clusters, data analytics
and aged results
System pool
(Flash)
Gold pool
(Disk)
Tape Library
Cloud Object Storage
OR
One cloud or tape per filesystem

18. #ibmedge
Data DR Derived from Spectrum Scale
• Hbase Cluster Replication
• WAL-based asynchronous way
• All nodes in both cluster should be accessible for each other
• Both clusters could provide Hbase service on the same time
• Only available for Hbase
• Supported over HDFS Transparency + Spectrum Scale
• Spectrum Scale AFM IW-based replication
• AFM IW-based replication from production cluster(cache site) to
standby cluster(home site)
• Both sites should be Spectrum Scale cluster for Hadoop
application failover
• Only one cluster can provide Hbase service(conflict in
assigning region servers if Hbase is up on both cluster)
• Spectrum Scale Active-Active DR
• 2 replica in production cluster; another replica in standby cluster
• Dedicated network for two clusters(10Gb bandwidth)
• Distance between two clusters is less than 100Km
• Can achieve RPO=0 in DR
17
Spectrum Scale
HDFS Transparency
Spectrum Scale
HDFS Transparency
AFM
WAN
Spectrum Scale
HDFS Transparency HDFS Transparency
Dedicated network

19. © 2016 IBM Corporation #ibmedge 18
Spectrum Scale Data
Analytics Solution
Deployment Models

20. #ibmedge
In-place Data Analytics
19
A A A
Existing System Analytics SystemData ingest
Export result
A A A
Existing System
Spectrum Scale File System
File Object
Analytics System
HDFS
Transparency
Traditional Analytics Solution
In-place Analytics Solution
• Build analytics system from scratch, not only
for compute but also for storage
• Add storage and compute resource at the
same time no matter it’s required
• Native HDFS doesn’t support native POSIX
• Lacks of enterprise data management and
protection capability
 Can leverage existing Spectrum Scale storage
 Unified interface for File and Object analytics
 POSIX compatibility
 Mature enterprise data management and
protect solutions derived from Spectrum
Storage family and 3rd part components
 Transparency Cloud Tiering

21. #ibmedge
Storage-rich Server Based Deployment Model
20
S FServer License FPO License
Storage-Rich Servers
• IBM Power
• Commercial X86
S FS S F
Spectrum Scale BD&A Cluster
10 GigE / 40 GigE / InfiniBand (Spectrum Scale I/0)
10 GigE / 1 GigE (Application, Management)
Spectrum Scale FPO
Spectrum Scale HDFS
Transparency
Hadoop
Application
HDFS RPC
Spectrum Scale FPO, Hadoop and
Application are installed in all nodes
in cluster.
Write data to directly to local disk
when RPC destination is local node.
Spectrum Scale FPO
• Use the same binary but with different deployment model
and configuration.
• Spectrum Scale (FPO) has capability to store data and
metadata in different Storage Pool (set of disks) to provide
high performance and availability.
• The same locality as HDFS. Saves the 1st replica in local
disks and stripe other replica(s) to other failure group.
• Optimized for READ.
• WRITE data has to go through network.
FPO storage pool configurations:
• AllowWriteAffinityDepth
• Enable FPO storage pool (horizontal block allocation type)
• WriteAffinityDepth
• 0 indicates that each replica is to be striped across the disks in a cyclical
fashion. (General GPFS)
• 1 indicates that the first copy is written to the writer node. The second copy is
written to a different rack. The third copy is written to the same rack as the
second copy, but on a different half. (FPO)
• 2 indicates that the first copy is written to the writer node. The second copy is
written to the same rack as the first copy, but on a different half.
• BlockGroupFactor
• Effective block size = file system block size * BlockGroupFactor

22. #ibmedge
SAN Shared Storage Based Deployment Model
• Compute Nodes only needs to install Hadoop components, like
Hbase, and HDFS client to service Analytics workload.
• Spectrum Scale cluster is built on SAN shared storage.
• Spectrum Scale HDFS Transparency Connector (includes
NameNode and DataNode) are deployed in Spectrum Scale
NSD Servers or dedicate client nodes.
21
 Software defined storage supports various platform, such as
IBM Power and commercial X86, and storage system, such
as IBM DeepFlash 150 and other RAID system.
 No data recovery when compute node or Spectrum Scale
Transparency node (NameNode or DataNode) failed.
 Easy to extend storage capacity, I/O performance, Analytics
performance separately.
 Leverage SAN shared storage to provide high performance
and high availability derived from FC and InfiniBand (and
RDMA) and Spectrum Scale replication.
S Server License
S S S
FC Switch / InfiniBand (RDMA)
10 GigE / 1 GigE
RAID Storage
Compute Nodes
• IBM Power or X86
• Only Hadoop services and
HDFS client
Spectrum Scale BD&A Cluster
• HDFS Transparency
NameNode/DataNode
• IBM Power or X86
C Client License
C C C

23. #ibmedge
IBM DeepFlash 150 Based Deployment Model
22
• IBM DeepFlash 150
• 3U chassis starting at 128TB up to 512TB
• 8 to 64 8TB flash cards
• Up to 2M raw IOPS
• sub 1ms latency
• 12GB/s throughput
• Data platform for Analytics (Hadoop,
Spark, SAP Hana)
• Faster time to insights
• Load and off load data to and from memory faster
• Shared data platform for multiple instance and
forms of analytics
IBM DeepFlash 150
S Server License
S S S
SAS Connection
10 GigE / 1 GigE
RAID Storage
Compute Nodes
• IBM Power or X86
• Only Hadoop services and
HDFS client
Spectrum Scale BD&A Cluster
• HDFS Transparency
NameNode/DataNode
• IBM Power or X86

24. #ibmedge
ESS Based Deployment Model
23
C C C
10 GigE / 1 GigE
Spectrum Scale BD&A Cluster
ESS
E E
InfiniBand (RDMA) / 40 GigE / 10 GigE
C Client License
Compute Nodes
• IBM Power or X86
• Only Hadoop services and
HDFS client
• Compute Nodes only needs to install Hadoop components, like
Hbase, and HDFS client to service Analytics workload.
• Spectrum Scale cluster is built on IBM Elastic Storage Server (ESS).
• Spectrum Scale HDFS Transparency Connector (includes
NameNode and DataNode) are deployed in dedicate client nodes
which joined into Spectrum Scale cluster in ESS.
 ESS provides high performance and reliability powered by
Spectrum Scale RAID
 Software De-Cluster RAID
 Erasure code ready
 End-to-end checksum
 Supports FDR/QDR InfiniBand and RDMA
 ESS can also service other workload, such as OpenStack at
the same time

25. © 2016 IBM Corporation #ibmedge 24
Solutions dedicated to
Client’s Success

26. #ibmedge
Analytics on Docker with Spectrum Scale
25
Unified Data Plane for Docker’s Orchestration
Fileset X Fileset Y
Fileset Z
Image
File A
Image
File B
Image
File C
……
Docker
Container A
SharedPriv.
Docker
Container B
SharedPriv.
Docker
Container C
SharedPriv.
Fileset X Fileset YVirtual Data Plane across
Docker instances
Multi-tenant Storage-
Centric Services in
Cloud
Managed by
Docker’s
Orchestration
IBM Spectrum Computing
 GPFS File (w/ file clone) as the
runtime Image
 GPFS Fileset as shared volume
between Docker containers
 Shared Fileset build up the virtual
data planes across containers.
 Spectrum Scale control-plane can
transparently control the virtual data
plane
 Achieve/Snapshot/DR can be done
at Fileset level.
Disk
Tape
ESS
Single name space
Share Nothing
Cluster
Spectrum Scale AFM
Spectrum Scale
TCT

27. #ibmedge
Solutions for Data Warehouse – SAP HAHA
26
node03
Spectrum Scale FPO + SAP NAHA
node01 node02
shared filesystem - GPFS
HDD flash
data01 log01
DB partition 1
- index server
- statistic server
- SAP HANA®
studio
- SAP HANA®
DB
worker node
HDD flash
DB partition 2
- index server
- statistic server
- SAP HANA®
DB
worker node
SAP HANA® DB
data02 log02
HDD flash
DB partition 3
- index server
- statistic server
- SAP HANA®
DB
worker node
data03 log03 Key advantages from Spectrum Scale:
 Disk failure tolerance
 Node failure tolerance
 Data locality/data locality restore
 Fast repair when bringing down disk back
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
FC5887
IBM Elastic Storage Server is SAP NAHA
Certified Enterprise Storage

28. #ibmedge
Solutions for Data Warehouse – IBM DB2
• Each Data Module
• Has a set of local disks
• Hosts a set of Partitions (1-N)
• 3 Module building block
• GPFS File system over building block
• One or Two replicas
• Failure of any one Failure Group does not affect GPFS
operation
• High Availability
• Distributed or mutual failover
• Data fully visible across DPF cluster
• Data locally available within building block
• Optional - local RAID for disk
• Avoid rebuild due to disk failure
27
Spectrum Scale + IBM DB2 DPF / DashDB

29. #ibmedge
Bill Analytics System for a Telecom Operators
28
Data Collection
Sub-system
ETL System
Network equipment's data collection
Internet activities capture and
pre-processing (merge, etc)
Real time
sales/marketing
sub-system
Power
Linux
Power
Linux
Power
Linux
Spectrum Computing
Spectrum Scale FPO
Spectrum Scale Multi-cluster
Power
Linux
Power
Linux
Power
Linux
Spectrum Computing
Spectrum Scale FPO
Built on 2013 runs Hadoop 1.x Built on 2015 runs Hadoop 2.x
NoSQL
database
Import aggregated results
into NoSQL database
Customer
Behavior
Subsystem
Other
business
sub-system
HBase MapReduce MapReduce Spark HBase Hive
Power
Linux
 More than 100 Nodes
 Process 40 TB data each day
 Spectrum Scale FPO manages more than
2600 disks (2.6 PB raw capacity) in this
system.
Spectrum Scale and Spectrum Computing is the
most stable Big Data system we have.
Spectrum Scale POSIX access interface and
Multi-cluster feature resolves data balance
issue and provides 5X performance
improvement in data loading phase.
-- Client IT Architecture

30. #ibmedge
ESS Services Hadoop and HPC Mixed Workload
IBM Power E880
IBM Elastic Storage
Server (ESS)
Compute
Nodes
HDFS
Transparency
Nodes
Hadoop Dev Cluser Hadoop Test Cluser Hadoop PROD Cluser HPC Cluser
Spectrum Scale Cluster
Key Advantages
- IOP with Value-Add (Spectrum Scale + Spectrum Computing)
- Share the same ESS storage for both Hadoop clusters and HPC cluster
- Deploying several Hadoop clusters over one file system
- Mixed workloads, scaling storage and computing capability separately

31. #ibmedge
Notices and Disclaimers
30
Copyright © 2016 by International Business Machines Corporation (IBM). No part of this document may be reproduced or transmitted in any form without written permission
from IBM.
U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM.
Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of
initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. THIS DOCUMENT IS
DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL IBM BE LIABLE FOR ANY DAMAGE ARISING FROM THE
USE OF THIS INFORMATION, INCLUDING BUT NOT LIMITED TO, LOSS OF DATA, BUSINESS INTERRUPTION, LOSS OF PROFIT OR LOSS OF OPPORTUNITY.
IBM products and services are warranted according to the terms and conditions of the agreements under which they are provided.
IBM products are manufactured from new parts or new and used parts. In some cases, a product may not be new and may have been previously installed. Regardless, our
warranty terms apply.”
Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice.
Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers
have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary.
References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in
which IBM operates or does business.
Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials
and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or
their specific situation.
It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and
interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such
laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer is in compliance with any law

32. #ibmedge
Notices and Disclaimers Con’t.
31
Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not
tested those products in connection with this publication and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products.
Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. IBM does not warrant the quality of any third-party products, or the
ability of any such third-party products to interoperate with IBM’s products. IBM EXPRESSLY DISCLAIMS ALL WARRANTIES, EXPRESSED OR IMPLIED, INCLUDING BUT
NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
The provision of the information contained h erein is not intended to, and does not, grant any right or license under any IBM patents, copyrights, trademarks or other intellectual
property right.
IBM, the IBM logo, ibm.com, Aspera®, Bluemix, Blueworks Live, CICS, Clearcase, Cognos®, DOORS®, Emptoris®, Enterprise Document Management System™, FASP®,
FileNet®, Global Business Services ®, Global Technology Services ®, IBM ExperienceOne™, IBM SmartCloud®, IBM Social Business®, Information on Demand, ILOG,
Maximo®, MQIntegrator®, MQSeries®, Netcool®, OMEGAMON, OpenPower, PureAnalytics™, PureApplication®, pureCluster™, PureCoverage®, PureData®,
PureExperience®, PureFlex®, pureQuery®, pureScale®, PureSystems®, QRadar®, Rational®, Rhapsody®, Smarter Commerce®, SoDA, SPSS, Sterling Commerce®,
StoredIQ, Tealeaf®, Tivoli®, Trusteer®, Unica®, urban{code}®, Watson, WebSphere®, Worklight®, X-Force® and System z® Z/OS, are trademarks of International Business
Machines Corporation, registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM
trademarks is available on the Web at "Copyright and trademark information" at: www.ibm.com/legal/copytrade.shtml.

33. © 2016 IBM Corporation #ibmedge
Thank You