hi perf analytics in sap netweaver 2004s
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Session ID: IM205High Per form anc eAnaly t ic s in SAP
Net Weaver 2004s
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Klaus Nagel, SAP AG
Alexander Peter, SAP AG
Ron Silberstein, SAP Labs, LLC
Marc Bernard, SAP Labs, LLC
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Learn ing Objec t ives
As a resul t o f t h is w ork shop, you w i l l be ab le t o : Explain the significant performance improvements delivered
with the High Performance Analytics capabilities deliveredwith SAP NetWeaver 2004s
Understand that HPA also reduces the maintenancesubstantially
Specify appropriate scenarios for HPA and their easy
application on top of existing data models and processes Understand SAPs strong commitment to investing in
performance innovations, simultaneously keeping TCO at aminimum
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Overview
HPA Configuration
HPA Administration
HPA in Detail
Summary
Appendix: Some Performance Features 2004s
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Ent erpr ise Business Int e l l igenc e: Chal lenges
Enterpr ise
Business
In te l l igence
RapidlyGrowing
Data Volumes
Requirements:
Speed andFlexibility
IncreasingNumber of
Users
Administration challenges Potential performance issues
Increasing demand for more,better, and actionableinformation
Natural data growth,RFID, POS, EDW,retention policies,
Right-time information,fast query execution,ad hoc queries,
More application areas,more casual users
Challe
nges
forres
ourc
e
allo
catio
n:ext
ensive
proa
ctiv
e
maint
enanc
eeffort
require
d
Challenges for resourceallocation: updating capacity
planning may result inadditional hardware investment
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Som e Ways t o Im prove Query Per form anc e
Per formanc e Feat ures
HighPerformanceAnalytics
Aggregates OLAP
Cache
ReportingAgent /
InformationBroadcasting
Summarized (or subset)
materialized views ofInfoCube data
Accelerates response for
similar queries by cachingquery result sets, andreading from cacheinstead of DB
Run popular queries in background
(off hours) and push summaryviews of updated data to users Information Broadcasting
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Trad i t iona l Aggregat es Back ground / Contex t
Genera l ly , aggregat es ... Are BI objects designed to improve query response times
Structurally look the same as an InfoCube
Are subsets of fact table data
(Typically) store summary data in new, smaller, child InfoCubes
Improve query response times by reducing the volume of data read
Are transparent to users
If an aggregate is available for a request, the system uses it automatically
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How Many Aggregat es Do You Need?
You have to dec ide w h ic haggregat es you need
To ensure good performance
Pre-calculate aggregates for main queriesand reports
Restrict scope and frequency of reporting
This is a trade-off between flexibility andperformance
You cannot create aggregatesfor all possible queries
Thus, the typical strategy is to buildsomewhat general aggregates that morequeries can utilize, but the performancebenefit per query is less significant
# characteristics
#aggreg
ates
Aggregate Combinations
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A Searc h Engine for Analy t ic s?
A new approac h The latest computer architectures make more intensive use of
increasingly inexpensive memory resources
Search engines deliver fast responses by giving read accesspriority over index updates
But can an engine first conceived for text search be used toimplement fast search for structured data in tables?
Yes SAP adapted its TREX search engine to implement
A c apab i l i t y fo r h igh per fo rmanc e ana ly t ic s !
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HPA Capabi l i t y for SAP Net Weaver BI
High Per form anc e Analy t ic s (HPA)A new transparent approach based on TREXto boost BI query performance
Performance speedup factor between 10 and 100Without changing the BI user experience (transparent to users)
HPA Engine
Storage Query ProcessingDBMS TREXSAP NetWeaver Business Intelligence
Database
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Termino logy
TREX and HPA TREX : Search and classification engine, mainly for unstructured data
HPA is built using TREX technology (indexing, retrieving, compression...) to handle structured data
Note: HPA and TREX are two different installations
The HPA engine cannot be used for standard KMC functionality
A TREX installation cannot be used for high performance analytics
Inside info: technically there is a lot of commonality but TREX-HPA role
switching is not supported
Index
HPA index: Structure containing replicated data and indexes (one index
per table) that represents the BI star schema for the HPA engine Indexing: Transfer of InfoCube data to the HPA server and processing
and compressing it into the HPA index
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HPA Arc h i tec t ure
SAP NetWeaver BIHigh Performance
Analytics Capability
Data Acquisition
InfoCubes
BI AnalyticEngine
BusinessExplorer
Any Tool
AnySource
Query &Response
Indexing
HPA engine responds to queries:joins and aggregates in run time
HPA indexes loadedinto memory
HPA creates and storesindexes for InfoCubes
Q E t i
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Query Ex ec ut ion
Query ExecutionWithout HPA
Pre-calculation /Information Broadcasting
Query ExecutionWith HPA
Pre-calculation /Information Broadcasting
OLAP Cache
HPA
1
OLAP Cache
Aggregate
InfoProvider
2
3
4
The BI system checks the listed repositories (in theorder shown) to obtain the query result set
HPA R t i l
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HPA Rat ionale
High Performance Analytics
Very fast queryresponse time
Stable queryresponse time
High scalability Low maintenance
Performance
improvementsby factor 10 100
Independent of
DB optimizer,aggregates, ...
No aggregatemaintenance, minimized
roll-up/change run
Planned support of newhardware paradigms like
grid/adaptive computing
Increasedend-user
satisfaction/ExtendedBI reach
SignificantTCO
reduction
HPA S i
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HPA Sc enar ios
Ready for h igh data vo lumes Queries that routinely involve access to many millions of records and
may involve up to billions of records
Examples: retail, utilities, telephone companies
Chal lenging response t im e SLAs
Example: service level agreements for call center operators demandshort response times for good closure rates
Unpred ic t ab le t ypes o f quer ies
Far more different data sets and aggregations than traditionaloptimization and caching strategies can handle
Excellent response times for any drill-down, slice & dice,
Examples: on-demand reporting for different user groups,ad hoc analyses
HPA Impac t on Main tenance
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HPA Impac t on Main tenance
How HPA reduc es ma in t enanc e e f fo r t Aggregate maintenance
Automated caching strategies
Building database indexes
Improves implementation and administration Reduces TCO
The relational aggregate/caching approach is notobsoletewith HPA, and is suitable for some scenarios:
Smaller data volumes
Predictable query patterns
Relaxed response time constraints
Caching reduces potential high load on the HPA engine
HPA Test Resul t s
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HPA Test Resul t s
Pre l im inary Lab Test Resu l t sLab tests with real SAP NetWeaver BI customer data
MultiProvider with 9 InfoCubes
About 850 million records together Customer aggregates used
The 7 most important, critical queries tested
Improvement factor 25
Hardware example:
At SAPPHIRE 2005 in Boston, SAP Executive Board member Shai Agassi demonstrated the
new capability for high performance analytics by launching complex queries against abillion data records and getting results back in seconds
For the demonstration, the HPA engine ran under 64-bit Linux on 8 blades, each with dual3.6 GHz Intel Xeon processors and 8 GB of RAM, mounted above a HewlettPackardProLiant 640 server with 4 Intel Itanium processors running SAP NetWeaver 2004s BI under
HP-UX, and an HP StorageWorks filer, all in a standalone cabinet
BI Perform anc e Tec hniques : Speed vs Re usabi l i t y
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BI Perform anc e Tec hniques : Speed vs. Re-usabi l i t y
HPA of fers h igh re-usabi l i ty t oget her w i t h fast per formanc e in
a manner not possib le w i th o t her tec hn iques!
Canned Report s
Query Cac he
Summ ary Tables
Database
HPA
Less
fast
Fastest
Performance
Lowest Highest
Re-usability
High re-usability Very few specific objects (or sets) can be used toaccelerate many different queries
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Overview
HPA Configuration
HPA Administration
HPA in Detail
Summary
Appendix: Some Performance Features 2004s
HPA Inst a l la t ion
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HPA Inst a l la t ion
Ins t a l la t ion for HPA in a box
During ramp-up of SAP NetWeaver 2004s, the underlying TREXsoftware for HPA will only be available preinstalled in a box
The box may be standalone or fit into an existing customer rack
The box will contain blade servers with 64-bit Intel processors
The OS for the blades is Linux SLES 9
The blades will be delivered with the complete HPA engine
preinstalled During the ramp-up phase, the box will contain Intel Xeon CPUs in
a choice of Hewlett-Packard or IBM hardware
Sizing for HPA (Ram p-Up Delivery)
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Sizing for HPA (Ram p Up Delivery)
Sm al l T-shir t Size
20 parallel user sessions
250 million rows total
500 byte / row
Mediu m T-shir t Size
50 parallel user sessions
500 million rows total
500 byte / row
Large T-shir t Size
100 parallel user sessions 1,000 million rows total
500 byte / row
Intel-based blade technology under 64-bit Linux SLES9
HPA Index Main t enanc e 1
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HPA Index Main t enanc e 1
Ac c ess ing HPA Index Ma int enanc e
Similar to aggregatemaintenance viacontext menu for aparticular InfoCube
Or direct access viatransaction RSDDV
HPA Index Main t enanc e 2
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de a e a c e
Conf igur ing RFC Dest inat ion for in t he BI syst em
Configure HPA setting to the SM59 setting(this needs only be done once)
HPA Rol l-up and Change Run
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p g
Roll-up
Update of newly loaded data to HPA index
Similar to roll-up process for aggregates
But there is only one HPA index to adjust
So the process is much easier and faster
Same process for HPA and aggregates: process chains do not need to bechanged after implementing HPA
Change Run
Update HPA index after master data/hierarchy changes
HPA index has same granularity as the InfoCubeNo c hanges needed fo r t ransac t ional da ta
HPA index includes some master data tables which have to be updated within
minimal runtime Adjustment of HPA index for key date changes of time-dependent attributes is
not necessary
Same process for HPA and aggregates: process chains do not need to bechanged after implementing HPA
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Overview
HPA Configuration
HPA Administration
HPA Engine in Detail
Summary
Appendix: Some Performance Features 2004s
Demo
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Demo
Zero Admin is t ra t ion
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HPA Goal : Zero Adm in is t ra t ion
HPA will feature automatic
Monitoring
Configuration
OptimizationSelf-repair
TREX Alert Server
Preconfigured checks
New checks can be added
Feeds any alerts to BI
TREX Admin Tool
Offers all admin options
Access to relevant optionsvia BI planned
Any alerts shownin portal view
SAP NetWeaver BI
TREX
HPAEngine
HPA Arc h i t ec t ure : H igh Avai lab il i t y and Sc alab i l i ty
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RFCServerRFCServer
IndexServerBackup
HPAEngine
Index
ServerMaster
IndexServer
Primary
HPAEngine
Index
ServerCohort
IndexServerNode
HPAEngine
Index
ServerCohort
IndexServerNode
HPAEngine
Name
ServerMaster
NameServer
Primary
NameServer
Backup
Filer
Index
ServerCohort
HPA Fast Index Update
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Queries Index updates
TREX
Main Index
Delta Index
Benefits: Smaller files
updated
Faster replication
HPA Engine
Queries runagainst both
indexes Merge delta indexes
as background job Triggered by size Per schedule Or manually
TREX Adm in is t ra t ion Tool
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The TREX admin tool can be used for many tasks
The task shown here is landscape configuration
Aler t s in TREX Adm in Tool
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Click here to view alerts
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Overview
HPA Configuration
HPA Administration in TREX
HPA Engine In Detail
Summary
Appendix: Some Performance Features 2004s
HPA Index Bui ld T ime
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SAP NetWeaver BI High PerformanceAnalytics Capability
Data Acquisition
InfoCubes
BI AnalyticEngine
BusinessExplorer
Any Tool
Parallel indexing of InfoCube datavia standard BI processes
Vertical decomposition& compression to minimize
memory footprint of HPA indexes
AnySource
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HPA Query Run T im e
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SAP NetWeaver BI High PerformanceAnalytics Capability
Data Acquisition
InfoCubes
BI AnalyticEngine
BusinessExplorer
Any Tool
AnySource
Aggregation on the fly
Merging and resultspreparation for BI queries
HPA Arc h i tec t ure: TREX In dex Server and HPA Engine
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HPA Engine Indexes BI InfoCubes
Materializes joins
Aggregation on the fly
Index Server
HPAEngine
Indexes
TREX
RFC
TREX Index Server
Indexes and searches
Contains HPA engine
Why HPA is so fastVertical decomposition
Horizontal partitioning
Smart compression
HPA Arch i t ec t ure : HPA Ver t ic a l Dec om posi t ion
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HPA
stores tables by column
To find all instances of an attributevalue:
Go to the attribute column
Read its row values
Classic a l DB
stores tables by row
To find all instances of an attributevalue:
Go to the first row
Check the attribute value Go to the next row
Check the attribute value
Repeat for each row in the table
Att1Att1
Tuple2
Tuple1Att2
Att2Att2
...
If you dont havea predefined indexready you can wait
a long time
HPA Arc h i tec t ure: HPA Hor izonta l Par t i t ioning
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Fact TableFact Table
PhysicalIndex
Server
IndexPart 1
PhysicalIndex
Server
IndexPart 2
PhysicalIndex
Server
IndexPart N
LogicalIndex
Server
IndexMetadata
Storage
TREX Services
Index
Part 1
Part 2
...
Part N
Metadata
Fact Table
Part 1
Part 2
...
Part N
LargeTables
HPA Arc h i t ec t ure : Sm ar t Com press ion
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Compress ion
Dictionary-based compression
Size of table keys significantly reduced
Lab Resul t s Lab results on real data have shown compression factors up to 20
The HPA engine can handle huge data volumes in main memory without need for costly disk accesses
HPA Arc h i tec t ure: HPA Eng ine
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HPA Queri es
Automatically join required indexesas specified in the HPA index
Aggregate large amounts of data inparallel
Restrict aggregated rows withboolean query expressions
Process more than one booleanexpression at once
Very fas t aggregat ion a lgor i thm
Exploits integer coding formax speed and min I/O
Designed to run on latest blade
servers and grid landscapes
Optimizes usage of memory andcache resources
Optimized for BI
Compressed m em ory index
Many million documents can fit in1 GB memory
Stores by column (not by row)
Can split indexes horizontally for
parallel processing
Hybr id dynamic index
Enables indexing of huge volumesof structured data
Keeps only required columns inmemory
HPAEngine
TREX
Engine
Engine
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Overview
HPA ConfigurationHPA Administration in TREX
HPA In Detail
SummaryAppendix: Some Performance Features 2004s
HPA Sum m ary
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Query proc ess ing in mem ory
Sophisticated compression
Horizontal and vertical data partitioning
Enabled for grid and adaptive computing
Based on 64 bit architecture
HPA is t ransparent fo r t he app l ic a t ions and 3rd par ty t oo ls
Available for all applications and tools that
Use SAP NetWeaver BI for reporting and analytics
Are based on SAP NetWeaver 2004s
Easy migration from existing aggregate setup to HPA
Queries automatically use HPA
Data maintenance via BI, minimum admin for HPA
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Overview
HPA ConfigurationHPA Administration
HPA In Detail
SummaryAppendix: Performance Features 2004s
OLAP Cac he Enhancem ent s 1
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Delt a Cac hing
A query reads data from the OLAPCache as far as possible
Additional requests will be readfrom the InfoCubes (F) fact table
These additional requests willbe updated into the OLAP Cache
Restrictions
If the InfoCube is compressed, delta cannot be determined Cache entrywill be built up completely
OLAP Cache
Request 1
InfoCube (F)
Request 2
Request 3
Request 1
Request 2
Request 3
Request 4
Request 5
OLAP Cac he Enhancem ent s 2
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Delt a Cac hing
Setting per query
Transaction RSRT
Activation of deltacaching
OLAP Cac he Enhancem ent s 3
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Cac hing for Mul t iProvider Quer ies
Invalidation of the basis InfoProviders depends on the groupingtype setting
No InfoProvidergrouping: complete query
result is kept and will bediscarded if one if theInfoProvider changes
Grouping: one group storesall InfoCube results, other
objects in one or moregroups (uses deltacapabilities of infoCubes)
Grouping with
separate basisInfoCubes: every InfoCuberesult is stored separately
Separation of allInfoProviders
Query Ex ec ut ion
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Query Ex ec ut ion
All query splits can beparallelized
Splits of MultiProviderinto InfoProviders
Splits of InfoProviderinto Aggregates
Splits ofAggregates/InfoProviderinto E and F tables
Splits in analytical engine (constant selection, cell calculations) areparallelized with the exception of non-cumulative calculations
Number of parallel processes = min (6, max (0, #free processes 3))
not more than 6 parallel processes
F E
MultiProvider
InfoProviders
Aggregates
F/E tables
Rol l -up and Change Run 1
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Roll -up and Change Run
Parallelized across aggregates
Sequence (with regards to aggregate hierarchy) is obtained
Several change runs can run in parallel if there is no common
InfoCube Settings can be made in the
Batch Manager Framework(transaction RSBATCH)
Rol l -up and Change Run 2
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Ex am ple for para l le l c hange run/ro l l -up
First step: calculate all basis aggregates (if there are more processesavailable than basis aggregates, not all processes are used)
Once one job is finished, the system checks for the next level ofaggregates
The following graphic is simplified as not all jobs end at the sametime some jobs might be entangled
3 available parallel
processes
Step 1InfoCube
Basis
Aggregates
Aggregates
BA1 BA2
A1 A2 A3 A4
BA1 BA2
Step 2A1 A2 A3
Step 3 A4
Compression
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Compression
General Optimizations
Inserts will be used automatically in case InfoCube is empty or therespective time value is new
Automatic DB parallelism via MERGE/UPSERT (also implemented in
DB6)
Generation of compression programs has been reducedperformance improvement especially for small requests
OLAP Cac he Enhancem ent s 4
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Selec t ive Dele t ion
Deleting in partitions
System automaticallydetects a databasepartition which matches
a subset of the recordsto be deleted
Scenario: you archive abunch of records and
expect performanceimprovement bydropping completepartitions
Recommendation: usethis flag for large datasets; for small sets theselective deletion might
work faster than the initial check for partitions
Par t i t ion ing
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Enhanced part i t ion ing
Additional database platforms support partitioning
DB2 / UDB: Multi-dimensional Clustering
Clusters can be built out of several keys (e.g. month and org unit)
Benefits: improved query performance, small indexes, fast insertion/deletion, less
table reorganization Supported tables: PSA, Fact tables and DataStore object tables
Further SAP optimizations: automatic statistics (no job needs to be scheduled),automatic tablespace enhancements
DB2 / AS400: Range Partitioning
Maximum 1000 partitionsMS SQL Server: Range Partitioning
Fact tables (E/F), PSA
ORACLE
Partitioning is not used if partitioning option is not installed this is necessary forSAP NetWeaver installations without usage type BI
Repart i t i on ing 1
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Repar t i t ion ing
Scenarios: after the initial partitioning you want someadditional partitions; after archiving data you want to mergesome earlier partitions
Three use cases of repartitioning tool
Append new partitions
Merge partitions
Complete repartitioning
Repart i t i on ing 2
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Repar t i t ion ing
Append new partitions
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Repar t i t ion ing
Merge partitions
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Repar t i t ion ing
Complete repartitioning
MonthFiscal Year Variant
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Dat a Load Per formanc e Improvem ent s
New Data Transfer Process
High scalability by enhanced parallelism
DataStore Object
Write-optimized DataStore object
Improved activation process
Improved rollback process
Centralized Monitoring
via Administration Cockpit Pro-active support of SAP
NetWeaver BI administrator instatus tracking andperformance optimization(data load, reportingand planning)
Enhanced data load & queryperformance statistics
SAP Net Weaver BI Near-L ine St orage St rat egy
AccessFrequency
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q y
Age of Data
Online
Near-Line Storage
Near-Line St oragein SAP NetWeaver 2004s BI
SAP NetWeaver BI analyses havedirect access to archived data in
various storage media
Availability of historic data whilereducing costs
Physical decoupling of frequentlyused and less frequently or rarely
used data
Reloading of data into theInfoCube or DataStore Objectonly necessary in exceptional
cases
Furthe r Informat i on
P bl i W b
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Re la ted Work shops /Lec t u res a t SAP Tec hEd 2005IM251, Enterprise Data Warehousing Enhanced Administration &Monitoring in SAP NetWeaver 2004s, 2h Hands-on
IM252, Enterprise Data Warehousing Enhanced Modeling Capabilities inSAP NetWeaver 2004s, 2h Hands-on
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