a hybrid technology platform for increasing the speed of operational analytics
DESCRIPTION
Track 1 d a hybrid technology platform for increasing the speed of operational analytics - ed lynchTRANSCRIPT
© 2009 IBM Corporation
A Hybrid Technology Platform for Increasing the Speed of Operational Analytics
Ed Lynch – Executive Client Technical Professional
Data Warehousing and Business Analytics for System z10 October 2012
© 2009 IBM Corporation
Speaker Biography
Ed Lynch is an Executive Client Technical Professional specializing in IBM’s System z Data Warehousing, Business Analytics, and Information Integration software products. Ed’s twenty-eight year career with IBM has spanned many areas of IBM, and has always involved IBM's Information Management (IM) products. His previous roles have included DB2 for z/OS Development and Management, DB2 Technical Marketing, Development and Delivery of IM Product Education, a Principal of Information Integration Design and Implementation Consulting Services, and DB2 Tools & Information Integration Technical Sales.
Currently, Ed is the lead Technical Specialist for North America’s System z Data Warehousing and Business Analytics, and Information Integration Software Technical Sales team.
Ed has worked extensively with DB2 across the various operating system platforms, InfoSphere Data Replication, InfoSphere Classic Replication Server, DB2 Analytics Accelerator, DB2 DataPropagator, InfoSphere Federation Server, InfoSphere Classic Federation Server, DB2 Connect, IBM Information Server, and IBM’s Business Analytics solutions. In his current role, he frequently works with product development in identifying and prioritizing product requirements, and developing product strategy. He also provides software technical sales support and works extensively with customers to create architectures using these products.
1-972-561-9975
© 2009 IBM Corporation
Abstract
With the wealth of data available today, organizations are no longer willing to relegate information to the back office. Modern organizations are demanding access to information. However, it is not enough to capture information, users must be quickly able to sift through these massive amounts of data, extract information and transform it into actionable knowledge. Systems today are enabling organizations to anticipate risk, identify threats, assess readiness, and match the risk assessment to the resources required to address them; all at the time of decision. They use a platform that provides the ability to react to changes decisively, based upon the facts of the situation, not in hours or days- but at the moment of opportunity. They optimize decisions based upon current weather conditions, past threats and behaviors and current resource availability to assure a successful operation.This session will review the architecture and benefits of a hybrid system of MPP and SMP technologies enabling the merging of fit for purpose and mixed workload capabilities into a single system. See how this hybrid system facilitates both transaction-oriented applications and analytics into a single platform for operational analytics. Find out why these enhancements are the next logical steps in creating a highly optimized environment, both in price and performance, that is designed to meet the wide range of analytic workloads that today's organizations need to accommodate.
© 2009 IBM Corporation44
DB2 Analytics Accelerator V3Further extending the features
Blending System z and Netezza
technologies to deliver unparalleled,
mixed workload performance for
complex analytic business needs.
More insight from your data
• Unprecedented response times for
“right-time” analysis
• Complex queries in seconds rather
than hours
• Transparent to the application
• Inherits all System z DB2 attributes
• No need to create or maintain indices
• Eliminate query tuning
• Fast deployment and time-to-value
© 2009 IBM Corporation5
DB2 Analytics AcceleratorDB2 Analytics AcceleratorTrainTrain--ofof--thought Analyticsthought Analytics
FASTComplex queries run
up to 2000x faster
while retaining single
record lookup speed
ApplianceNo applications to
change, just plug it
in, load the data,
and gain the value
Cost SavingEliminate costly
query tuning while
offloading complex
query processing
© 2009 IBM Corporation6
Introducing Introducing
DB2 Analytics Accelerator V3DB2 Analytics Accelerator V3Reducing the Cost of High Speed AnalyticsReducing the Cost of High Speed Analytics
Improve Productivity
Eliminate query tuning
Eliminate table indexing
Minimize storage admin
Consolidate
Reduced complexity
Reduced software costs
Reduced hardware costs
Lower Host Costs
Reduce storage costs
Offload query processing
Defer system upgrades
© 2009 IBM Corporation
Fast Time to Value
� IBM DB2 Analytics Accelerator
� Production ready - 1 person, 2 days
� Table Acceleration Setup 7 2 Hours
– DB2 “Add Accelerator”
– Choose a Table for “Acceleration”
– Load the Table (DB2 copy to Netezza)
– Knowledge Transfer
– Query Comparisons
� Initial Load Performance 7
�400 GB “Loaded” in 29 Min
570 million rows (Loads of 800GB to 1.3TB/Hr)
� Actual Query Acceleration 7 1908x faster
�2 Hours 39 Minutes to 5 Seconds
� CPU Utilization Reduction
�35% to ~0%
Actual customer results, October 2011
© 2009 IBM Corporation
8 12 October 2012
Performance & Savings
Accelerating decisions to the speed of business
Queries run faster
• Save CPU resources
• People time
• New Business
opportunities
Actual customer results, October 2011
Times
Faster
Query
Total
Rows
Reviewed
Total
Rows
Returned Hours Sec(s) Hours Sec(s)
Query 1 2,813,571 853,320 2:39 9,540 0.0 5 1,908
Query 2 2,813,571 585,780 2:16 8,220 0.0 5 1,644
Query 3 8,260,214 274 1:16 4,560 0.0 6 760
Query 4 2,813,571 601,197 1:08 4,080 0.0 5 816
Query 5 3,422,765 508 0:57 4,080 0.0 70 58Query 6 4,290,648 165 0:53 3,180 0.0 6 530
Query 7 361,521 58,236 0:51 3,120 0.0 4 780
Query 8 3,425.29 724 0:44 2,640 0.0 2 1,320
Query 9 4,130,107 137 0:42 2,520 0.1 193 13
DB2 Only
DB2 with
IDAA
DB2 Analytics Accelerator: “we had this up and
running in days with queries that ran over 1000
times faster”
DB2 Analytics Accelerator: “we expect ROI in
less than 4 months”
Advance to 32 minute mark for DB2
Analytics Accelerator section of keynote
© 2009 IBM Corporation
IBM DB2 Analytics Accelerator V3 Product Components
9
10Gb
OSA-Express3
10 GbE
Primary
Backup
CLIENT
Data Studio Foundation
DB2 Analytics Accelerator
Admin Plug-in
zEnterprise
Data Warehouse applicationDB2 for z/OS enabled for IBM
DB2 Analytics Accelerator
IBM DB2 Analytics Acelerator
NetezzaTechnology
Users/
Applications
Network
© 2009 IBM Corporation10
Deep DB2 Integration within zEnterprise
Data
Manager
Buffer
ManagerIRLM
Log
Manager
IBM
DB2
Analytics
Accelerator
Applications DBA Tools, z/OS Console, ...
. . .
Operational Interfaces(e.g. DB2 Commands)
Application Interfaces(standard SQL dialects)
z/OS onSystem z
Netezza
DB2 for z/OS
Superior availability
reliability, security,
Workload management
Superior
performance on
analytic queries
© 2009 IBM Corporation
SMP Hosts
Snippet BladesTM
(S-Blades, SPUs)
Disk Enclosures
Accelerator ServerSQL Compiler, Query Plan, Optimize,Administration2 front/end hosts, IBM 3650M3 or 3850X5clustered active-passive
2 Nehalem-EP Quad-core 2.4GHz per host
Processor & streaming DB logic
High-performance database engine streaming joins, aggregations, sorts, etc.
Slice of User Data
Swap and Mirror partitionsHigh speed data streamingHigh compression rate EXP3000 JBOD Enclosures12 x 3.5” 1TB, 7200RPM, SAS (3Gb/s)max 116MB/s (200-500MB/s compressed data)
e.g. 1000-12: 8 enclosures → 96 HDDs(32/128 TB)
Accelerator powered by Netezza 1000TM
Appliance
© 2009 IBM Corporation
IBM BladeCenter Server
Netezza DB Accelerator
S-Blade™ Components
Intel Quad-Core
Dual-Core FPGA
8 Cores/Blade
8 FPGA Processors/Blade
© 2009 IBM Corporation
Eliminating the I/O Bottleneck
Move the SQL to the hardwareE to where the data lives
“Just send
the Answer,
not Raw
Data”
© 2009 IBM Corporation
The Key to the Speed
FPGA
CoreCPU Core
UncompressProjectRestrict,
Visibility
Complex ∑∑∑∑Joins, Aggs, etc.
select DISTRICT,PRODUCTGRP, sum(NRX)from MTHLY_RX_TERR_DATAwhere MONTH = '20091201'and MARKET = 509123and SPECIALTY = 'GASTRO'
Slice of tableMTHLY_RX_TERR_DATA
(compressed)
Slice of tableMTHLY_RX_TERR_DATA
(compressed)
where MONTH = '20091201'and MARKET = 509123and SPECIALTY = 'GASTRO'
where MONTH = '20091201'and MARKET = 509123and SPECIALTY = 'GASTRO'
sum(NRX)sum(NRX) select DISTRICT,PRODUCTGRP,sum(NRX)
select DISTRICT,PRODUCTGRP,sum(NRX)
Zone MapZone Map
© 2009 IBM Corporation
Bringing Netezza AMPPTM Architecture to DB2 for z/OS
Advanced Analytics
DBA
Legacy Reporting
BI
FPGA
Memory
CPU
FPGA
Memory
CPU
FPGA
Memory
CPU
SMPHost
Disk
EnclosuresS-Blades™Network
Fabric
IBM DB2 Analytics Accelerator
DB2 for z/OS
AMPP = Asymmetric MassivelyParallel Processing
© 2009 IBM Corporation
Query Execution Process Flow
DB2 for z/OS
Optimizer
Accele
rato
r DR
DA
Requesto
r
DB2 Analytics Accelerator
Application
Application
Interface
Queries executed with DB2 Analytics Accelerator
Queries executed without DB2 Analytics Accelerator
Query execution run-time for
queries that cannot be or should
not be off-loaded to Accelerator
SPU
CPU FPGA
Memory
SPU
CPU FPGA
Memory
SPU
CPU FPGA
Memory
SPU
CPU FPGA
Memory
SM
P H
ost
© 2009 IBM Corporation
Workload-Optimized Query Execution
DB2 for z/OS andIBM DB2 Analytics Accelerator
OLTP-like queryOLTP-like query
Light ODS-query
Light ODS-query
Heavy BI QueryHeavy BI Query
Light BI QueryLight BI Query
DB2 Native Processing
DB2 Native Processing
User c
ontro
l and D
B2 h
eu
ristic
• Single and unique system
for mixed query workloads
• Dynamic decision for most
efficient execution platform
• New special register
QUERY ACCELERATION
– NONE
– ENABLE
– ENABLE WITH FAILBACK
• New heuristic in DB2
optimizer
Optimized processing for BI Workload
© 2009 IBM Corporation
DB2 for z/OS Accelerator
Accelerator Data Load
Accelerator
Studio
Ac
ce
lera
tor A
dm
inis
trativ
e S
tore
d
Pro
ce
du
res .
.
.
.
.
.
.
.
.
Table A
Part 1
Part 2
Part m
Table C
Table B
Table D
Part 1
Part 2
Part 3
Unload USS Pipe
Unload
Unload
USS Pipe
USS Pipe
CPU FPGA
Memory
CPU FPGA
Memory
CPU FPGA
Memory
CPU FPGA
Memory
Co
ord
inato
r
• 1 TB / h – can vary, depending on CPU resources, table partitioning, 7• Update on table partition level, concurrent queries allowed during load• V2.1 & V3 unload in DB2 internal format, single translation by accelerator
© 2009 IBM Corporation19
DB2 Analytics Accelerator V3Lowering the Costs of Trusted Analytics
• zEnterprise EC12 Support
Version 3 will support the zEnterprise
EC12, z196 and z114 System z
platforms
•Query Prioritization
Brings System z workload
management down to the individual
query being routed to the Accelerator
•High Capacity
Support has been extended to include
the entire Netezza 1000 line (1.28 PB)
•UNLOAD Lite
Reduces z/OS MIPS consumption, by
moving the preparation off System z.
What’s New?
•High Performance Storage
Saver
Store a DB2 table or partition of data
solely on the Accelerator. Removes
the requirement for the data to be
replicated on both DB2 and the
Accelerator
• Incremental Update
Enables tables within the Accelerator
to be continually updated throughout
the day.
© 2009 IBM Corporation
Build a System z Trusted Analytic SystemBuild a System z Trusted Analytic SystemReduce the cost of host storage for historical data by 95%!Reduce the cost of host storage for historical data by 95%!
HistoricalMost data in an analytic
system is historical and not
subject to change. Most
data can be in a Storage
Saver and maintain trusted
performance and security
Low Latency DataTables and partitions that
require updating will be
able to be updated by
incremental update, table
load or partition load
High PerformanceAll aggregate queries run
at the same high speed as
any accelerator supported
query
© 2009 IBM Corporation
21
High Performance Storage SaverReducing the cost of high speed storage
DB2Table A
AcceleratorTable A
AcceleratorTable A
Tables can be resident on:1. DB2 Only
2. DB2 and Accelerator
3. Accelerator Only
Special Registers control behavior
� CURRENT QUERY ACCELERATION
� CURRENT GET_ACCEL_ARCHIVE
Managed by zParms
DB2Table A
SQL
Store historic data on the Accelerator only
When data no longer
requires updating, reclaim
the DB2 storage
High speed High speed
indexed indexed
lookups, best lookups, best
for OLTP for OLTP
type queriestype queries
High speed High speed
aggregate aggregate
lookups, best for lookups, best for
complex DSS type complex DSS type
queriesqueries
Mixed workload type queriesMixed workload type queries
Applications
© 2009 IBM Corporation
Save Over 95% of Host Disk Space for Historical Data
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
1Q
2Q
3Q
4Q
Year Year -7Year -2 Year -3 Year -4 Year -5Year -1
Historical Data
Current Data
One Quarter = 3.57% of 7 years of data
One Month = 1.12% of 7 years of data
One month = 2.78% of 3 years of data
© 2009 IBM Corporation23
High Performance Storage SaverReducing the cost of high speed storage
� Time-partitioned tables where:
– only the recent partitions are used in a transactional context (frequent data
changes, short running queries)
– the entire table is used for analytics (data intensive, complex queries).
� DB2 partitions are deleted after the High Performance Storage Saver are created
on the accelerator
DB2
#1
Accelerator
#1
Query from
Application
Or
Accelerator
#2
Accelerator
#3
Accelerator
#4
Accelerator
#5
Accelerator
#6
Accelerator
#7
No longer present on DB2 Storage
© 2009 IBM Corporation
24
The Evolution of a High Performance Storage SaverHigh Speed Access to Historical Data
DB2Table A
AcceleratorTable A
AcceleratorTable A
DB2Table A
Backup Backup
Table / Data
Creation
Accelerator
Load / Update /IU
Accelerator
Only
Archive
Only
© 2009 IBM Corporation
25
• Stored on both DB2 and Accelerator
storage
• Mixed query workload with transactions,
single record queries and record updates
with deep analytics, multifaceted,
reporting and complex queries.
• Full table, full partition update, Incremental
update from DB2 data
• Same high speed query access
transparently through DB2
Storage options to match data needsOptimized in both price and performance for differing workloads
• Only stored on Accelerator storage (Less
Cost)
• Optimized performance for
deep analytics, multifaceted, reporting
and complex queries
• Only full table update or full partition
update from backup
• Same high speed query access
transparently through DB2
Dual Disk StoreSingle Disk Store
Database Resident PartitionsHigh Performance Storage Saver
FunctionalityCost The right mix of cost and functionality
© 2009 IBM Corporation26
The zEnterprise Hybrid SolutionMixed Workloads for Next Generation Business Analytics
Operational
ApplicationsAnalytic
Applications
Mixed Workload
Applications
Transaction Processing Data warehousing Operational BI
Shared Everything DB Shared Nothing DB Hybrid DB
High volume business transactions and batch reporting running concurrently
Low volume complex queries context switching
High volume business transactions and batch reporting running
concurrently with complex queries
© 2009 IBM Corporation27
Incremental Update
ELT or ETLELT or ETLTable or Table or
Partition UpdatePartition Update
Data Data
ReplicationReplicationIncremental Incremental
UpdateUpdate
OLTP OLTP
ApplicationApplicationData Data
WarehouseWarehouseDB2 Analytics DB2 Analytics
AcceleratorAccelerator
Synchronizing data to lower data latency
from days to minutes/seconds
© 2009 IBM Corporation
Option 1: Full Table Refresh
� Changes in data warehouse tables typically
driven by scheduled (nightly or more
frequently) ETL process
� Data used for complex reporting based on
consistent and validated content (e.g., weekly
transaction reporting to the central bank)
� Multiple sources or complex transformations
prevent propagation of incremental changes
� Full table refresh triggered through DB2 stored
procedure (scheduled, integrated into ETL
process or through GUI)DB2 z/OS Query OptimizerDB2 z/OS Query Optimizer
DB2 for z/OS databaseDB2 for z/OS database
DB2 native
processing
DB2 native
processingAccelerator
processing
Accelerator
processing
Operational Analytics, Reports, OLAP, 7Operational Analytics, Reports, OLAP, 7
Continuous
Query
Processing
Continuous
Query
Processing
Full table refresh
Changes / Replacement
ET
L P
roce
ss
ET
L P
roce
ss� Queries may continue
during full table refresh
for accelerator
© 2009 IBM Corporation
Option 2: Table Partition Refresh
� Changes in data warehouse table typically driven by “delta” ETL process (considering only changes in source tables compared to previous runs) or by more frequent changes to most recent data
� Optimization of Option 1 when target data warehouse table is partitioned and most recent updates are only applied to the latest partition
DB2 z/OS Query OptimizerDB2 z/OS Query Optimizer
ChangesDB2 for z/OS databaseDB2 for z/OS database
ET
L P
rocess
ET
L P
rocess
DB2 native
processing
DB2 native
processingAccelerator
processing
Accelerator
processing
Operational Analytics, Reports, OLAP, 7Operational Analytics, Reports, OLAP, 7
Continuous
Query
Processing
Continuous
Query
Processing
May
April
March
February
January
Partition refresh
Replic
ation
Replic
ation
� Maintains snapshot semantics for consistent reports
� Queries may continue during table partition refresh for accelerator
� Table partition refresh triggered through DB2 stored procedure (scheduled, integrated into ETL process or through GUI)
© 2009 IBM Corporation
Option 3: Incremental Update
� Changes in data warehouse tables typically driven by replication or manual updates
– Corrections after a bulk-ETL-load of a data warehouse table
– Continuously changing data (e.g. trickle-feed updates from a transactional system to an ODS)
� Reporting and analysis based on most recent data
� May be combined with Option 1 & 2 (first table refresh and then continue with incremental updates)
DB2 z/OS Query OptimizerDB2 z/OS Query Optimizer
ChangesDB2 for z/OS databaseDB2 for z/OS database
DB2 native
processing
DB2 native
processingAccelerator
processing
Accelerator
processing
Operational Analytics, Reports, OLAP, 7Operational Analytics, Reports, OLAP, 7
Continuous
Query
Processing
Continuous
Query
Processing
Incremental Update
Rep
licati
on
Rep
licati
on
Ap
plicati
on
Ap
plicati
on
� Incremental update can be configured per database table
© 2009 IBM Corporation
Now expandable to 960 cores and 1.28 petabytes
002 005 010 015 020 030 040 060 060 100
Cabinets 1/4 1/2 1 1 1/2 2 3 4 6 8 10
Processing Units 24 48 96 144 192 288 384 576 768 960
Capacity (TB) 8 16 32 48 64 96 128 192 256 320
Effective Capacity (TB)*
32 64 128 192 256 384 512 768 1024 1280
.......
1 10
Capacity = User Data spaceEffective Capacity = User Data Space with compression *: 4X compression assumed
Predictable, Linear Scalability throughout entire family
Low Latency, High Capacity Update
PureData System for Analytics
© 2009 IBM Corporation32
Connectivity Options
Multiple DB2 systems can connect to a single Accelerator
A single DB2 system can connect to multiple Accelerators
• residing in the same LPAR
• residing in different LPARs
• residing in different CECs
• being independent (non-data sharing)
• belonging to the same data sharing group
• belonging to different data sharing groups
Multiple DB2 systems can connect to multiple Accelerators
Full flexibility for DB2 systems:
Better utilization of Accelerator resourcesBetter utilization of Accelerator resources
ScalabilityScalability
High availabilityHigh availability
DB2 DB2
DB2
DB2 DB2
The same table can be stored in the multiple Accelerators(except High Performance Storage Saver tables)
© 2009 IBM Corporation
Analytics Accelerator Table Definition and Deployment
33
DB2 for z/OS DB2 Analytics Accelerator
IBM Data Studio Client
AcceleratorAccelerator
StudioStudio
DB2 CatalogDB2 Catalog
Accelerator Accelerator
Administrative Administrative
Stored ProceduresStored ProceduresNetezza CatalogNetezza Catalog
� The tables need to be defined and deployed to the Accelerator before data is loaded and queries sent to it for processing.
� Definition: identifying tables for which queries need to be accelerated
� Deployment: making tables known to DB2, i.e. storing table meta data in the DB2 and Netezza catalog.
� IBM DB2 Analytics Accelerator Studio guides you through the process of defining and deploying tables, as well as invoking other administrative tasks.
� IBM DB2 Analytics Accelerator Stored Procedures implement and execute various administrative operations such as table deployment, load and update, and serve as the primary administrative interface to the Accelerator from the outside world including Accelerator Studio.
© 2009 IBM Corporation
• All user data and temp space mirrored
• Disk failures transparent to queries and transactions
• Failed drives automatically regenerated
• Bad sectors automatically rewritten or relocated
Shielding Against Disk Failures
Primary
Mirror
Temp
© 2009 IBM Corporation
Shielding Against S-BladeTM Failures
• S-Blade failure is automatically detected
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S-Blades
© 2009 IBM Corporation
Shielding Against S-BladeTM Failures
• Drives automatically reassigned to active S-Blades within a chassis
• Read-only queries (that have not returned data yet) automatically restarted
• Transactions and loads interrupted
• Loads automatically restarted from last successful checkpoint
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S-Blades
© 2009 IBM Corporation
SYSPLEX
Tables of App 1
Tables of App 2
Tables of App 3
Tables of App 4
Tables of App 5
DSG Member 1 DSG Member 2App 1
App 2
App 3
App 4
App 5
Switch Switch
Short Range
Accelerator Instance 1
Disaster Recovery Option 1 – Table Loaded in One Accelerator (1 of 2)
Short Range Short Range
Short Range
Long
Range
Tables of App 1
Tables of App 2
Tables of App 3
Accelerator Instance 2
Tables of App 4
Tables of App 5
Tables of App 1
Tables of App 2
Tables of App 3
Tables Created
but Not Loaded
© 2009 IBM Corporation
Accelerator Instance 1
SYSPLEX
Tables of App 1
Tables of App 2
Tables of App 3
Tables of App 4
Tables of App 5
DSG Member 1 DSG Member 2App 1
App 2
App 3
App 4
App 5Switch Switch
Short Range
Short Range Short Range
Short Range
Long
Range
Tables of App 4
Tables of App 5
Tables of App 1
Tables of App 2
Tables of App 3
Already CreatedMust LOAD
Tables of App 1
Tables of App 2
Tables of App 3
App 1
App 2
App 3
Disaster Recovery Option 1 – Table Loaded in One Accelerator (2 of 2)
Accelerator Instance 2
© 2009 IBM Corporation
SYSPLEX
Tables of App 1
Tables of App 2
Tables of App 3
Tables of App 4
Tables of App 5
DSG Member 1 DSG Member 2App 1
App 2
App 3
App 4
App 5
Switch Switch
Short Range
Disaster Recovery Option 2– Table Loaded in Two Accelerators (1 of 2)
Short Range Short Range
Short Range
Long
Range
Tables of App 4
Tables of App 5
Tables of App 4
Tables of App 5
Tables of App 1
Tables of App 2
Tables of App 3
Tables of App 1
Tables of App 2
Tables of App 3
Accelerator Instance 1 Accelerator Instance 2
© 2009 IBM Corporation
SYSPLEX
Tables of App 1
Tables of App 2
Tables of App 3
Tables of App 4
Tables of App 5
DSG Member 1 DSG Member 2App 1
App 2
App 3
App 4
App 5Switch Switch
Short Range
Short Range Short Range
Short Range
Long
Range
Tables of App 4
Tables of App 5
Tables of App 1
Tables of App 2
Tables of App 3
App 1
App 2
App 3
Disaster Recovery Option 2 – Table Loaded in Two Accelerators (2 of 2)
Tables of App 4
Tables of App 5
Tables of App 1
Tables of App 2
Tables of App 3
Data Already
Available
Accelerator Instance 1 Accelerator Instance 2
© 2009 IBM Corporation
Why Both?Marrying the best of both worlds
IBM
System z
IBM
PureData N1001
Very diverse workloadVery focused workload
Capitalizing on the strengths of both platforms while driving to the most
cost effective, centralized solution - destroying the myth that transaction
and decision systems had to be on separate platforms
Mixed Workload SystemFocused Appliance
© 2009 IBM Corporation
Flexible Integrated SystemTrue Appliance Custom Solution
• Mixed workload system z with operational transaction systems, data warehouse, operational data store, and consolidated
data marts.• Unmatched availability, security and
recoverability• Natural extension to System z to enable
pervasive analytics across the organization.
• Speed and ease of deployment and administration
Tailored to your needsA Hybrid Solution
• Appliance with a streamlined
database and HW acceleration for
performance critical functionality
• Price/performance leader
• Speed and ease of deployment and
administration
• Optimized performance for
deep analytics, multifaceted, reporting
and complex queries
Mixed Workload SystemFocused Appliance
IBM System z with
IBM DB2 Analytics Accelerator
IBM
Netezza
FlexibilitySimplicity The right mix of simplicity and flexibility
© 2009 IBM Corporation
43
Next StepsOpportunity
Data
Consolidation
(ISAS +
Accelerator)
Operational BI
(ISAS +
Accelerator)
New Workload
or Winback
(ISAS +
Accelerator)
Operational Reporting
(Accelerator)OR
Existing z Warehouse
Workload
Assessment
Warehouse
Collaboration
Workshop
Optional POC
© 2009 IBM Corporation44
The Ultimate Consolidation Platform
Transaction Systems
(OLTP)
Data Warehousing
Business Intelligence
Predictive Analytics
z/OS: Recognized leader in mixed workloads with security,
availabilityand recoverability
Netezza: Recognized leader in cost-effective high speed deep analytics
Data Mart Data Mart Data Mart Data Mart
Data Mart Consolidation
Together:Destroying the myth that transactional and decision support
workloads have to be on separate platforms
Bringing it all together
• Better Business Response
• Reduced Costs
• More Available
• More Secure
• Reduced Data Movement
• Better Governance
• Reduced Data Latency
• Reduced Complexity
• Reduced Resources
System z PR/SMRecognized leader in mixed
virtualization and workload isolation
© 2009 IBM Corporation
Learn More7
Visit the Data Warehousing &
Business Analytics Webpage
http://www.ibm.com/software/data/businessintelligence/systemz/