Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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12.1 Terminology: BI objects and processes Here we provide you with some relevant terminology. ► InfoProvider In SAP BI, objects that can be analyzed are called InfoProviders. There are two classes of InfoProviders: those that contain physical data (such as InfoObjects, InfoCubes, and DSO objects) and those that do not (such as InfoSets, RemoteCubes, and MultiProviders). ► MultiProvider A MultiProvider provides access to data from several InfoProviders and makes it available for reporting and analysis. ► Aggregate A materialized, summarized view of the data in an InfoCube. This data can be read directly from the database during query execution without having to be built at run time. ► Roll-up This is the process of loading new data packets from an InfoCube into an existing aggregate. ► Condense This refers to using a program to compress the contents of an InfoCubE- fact table. ► SQL efficiency This refers to a method to simplify the analysis of a query against an InfoCube, as described in 10.2, “SQL efficiency for InfoCube” on page 211. ► Realignment This refers to the process of synchronizing (realigning) all related fields after a change is made to one data field. ► Tablespace partitioning This refers to the process of reducing the physical size of a large tablespace into multiple smaller physical parts, according to a common key field — frequently time-based. Partitioned tablespaces allow parallel access across multiple partitions. Partitioned tablespaces also allow the accessing of individual partitions without affecting other partitions. ► DB2 compression This process utilizes compression routines, supplied by IBM, to compress DB2 data at the hardware level. 256 Best Practices for SAP BI using DB2 9 for z/OS
► SAP compression This refers to the consolidation and summarization of data from an InfoCube’s F- fact table into its E- fact table. Compression results in the package ID being stripped off and all records summarized into the E- fact table. 12.2 SAP BI information model The SAP BI information model is a basic structural element in the SAP BI architecture. It is designed to enable support to a range of decision makers. To do this, it supports the following conceptual layers of data warehousing: ► Multidimensional models, which enable views of data required for analytics ► DataStore (DSO), to hold current data updates from the operational transaction systems of the business ► Data warehouse, to hold transformed and accurate data that has been integrated from the business processes across the enterprise to enable business decision making The information model is based on a fundamental building block called an InfoObject. InfoObjects may contain data about customers, sales orders, products, and so on. An InfoObject can be reused in other elements of the information model, such as an InfoCube, DSO, and InfoSource. These elements are discussed in 12.2.1, “Other key elements in the information model” on page 258. Refer to Appendix A, “Glossary for SAP BI” on page 281, for a complete overview of SAP BI definitions. InfoObjects also carry metadata that describes the data contained in the InfoObject, such as its origin, history, and technical properties. An InfoObject has three classes of metadata: Technical metadata This describes technical properties, such as data type and field length. User metadata This carries information about authorizations. Business definitions These form the basis for a common understanding of business terms, such as key performance indicators. Business definitions are particularly important in the SAP BI information model. They eliminate semantic discrepancies in data elements across systems and organizational units, and ensure that data is consistent and reliable. SAP BI contains several thousands of InfoObject templates that include business definitions. Chapter 12. Concepts, activities, and terminology 257
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Best Practices for SAP BI using DB2
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Note: Before using this information
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4.4 Extended sparse index and in-me
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11.3 SAP BI database and InfoCube l
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viii Best Practices for SAP BI usin
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Trademarks The following terms are
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The team that wrote this IBM Redboo
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Thanks to these authors from the Pr
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► Send your comments in an e-mail
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1.1 Product overview The business w
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In general, database administrators
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► Horizontal growth is achieved b
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8 Best Practices for SAP BI using D
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2.1 BI architecture Figure 2-1 show
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New term Old term Analysis authoriz
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negative values means missing stati
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- When calculating database statist
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If you specify a partitioning schem
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Important: Never partition a PSA ta
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Figure 2-10 DPSIs versus NPSIs Figu
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Finally, Figure 2-12 explains which
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Figure 2-14 shows how the snowflake
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► Realignment This refers to the
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3.1 Base recommendations - SAP BI i
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the F- fact table, the E- fact tabl
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34 Best Practices for SAP BI using
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4.1 Dynamic index ANDing for star s
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Figure 4-2 and Figure 4-3 on page 3
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4.2.1 Processing RUNSTATS for BI ta
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RUNSTATS will then be called over t
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DSNU610I -DB2T 081 16:36:03.85 DSNU
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Often the single-value based freque
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Several things worth noting are:
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The unit of a value specified to MX
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condition, generated by SAP BI upon
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There is a separate IBM Redbooks pu
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5.1 Load PSA In this section we dis
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5.1.1 Load PSA To load data into th
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After the source system, the DataSo
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Note the available tabs (registers)
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Figure 5-8 shows the provided optio
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Figure 5-10 shows the following:
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The PSA table name is created autom
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5.1.4 PSA partitioning Partitioning
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An InfoCube, whether data target or
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6.1 Types of InfoCubes Data targets
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After selecting at least one time d
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6.2.1 Repartitioning implementation
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You will then be presented with a w
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From within the monitor you are abl
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Once your repartitioning process is
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Figure 6-14 is displayed once you h
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When you remodel an InfoProvider yo
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Figure 6-17 demonstrates how you wo
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At this step of the process you are
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6.3.2 Benefits of remodeling InfoPr
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6.4.3 Cumulative data versus non-cu
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Important: After the initial compre
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► Use DPSIs for the cube’s E- f
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7.1 Differences between DataStore a
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Figure 7-1 shows the initial status
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4. By selecting the Create button,
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Partitioning and clustering are bot
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7.4.2 Choosing and changing the clu
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7.4.4 Partitioning a DataStore obje
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From the ABAP Dictionary: Utility f
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Figure 7-9 DB2/390 Storage Attribut
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partitioning was used. The CREATE T
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, "/BIC/RECORDMODE" POSITION( 00289
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Upon completion of the job, you wil
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| "/BIC/DIVISION" VARGRAPHIC (00000
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| sql: | DELETE FROM DDSTORAGE | WH
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| "/BIC/CRMEM_VAL" DECIMAL (000017,
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| DDL time(___8): .........4 millis
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8.1 Troubleshooting general system
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You need to ascertain: ► Which us
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Further information about the subje
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8.2 Analyzing load performance In t
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We use transaction ST03 → Expert
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Click the Details tab of the RSMO w
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Select the component of interest to
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Figure 8-11 All statistical records
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Figure 8-13 Single statistical reco
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Loading from a flat file You can av
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and right-click to select the Simul
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Note: You must activate the saving
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5. In the right-hand panel of the w
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If your query has long run times th
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that the query runs against → Mai
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If the main component of query perf
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► Display Statistics Data (to sho
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Select the appropriate button, ente
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Figure 8-29 Statement details To ch
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The ratio (ST04 statement cache “
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Figure 8-32 Visual Explain - SQL te
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8.5 SAP Query Analysis Tool The SAP
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Display of sql access path SQL quer
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The access path is displayed hierar
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The table information tab shown in
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DB2 Plan Table Figure 8-42 DB2 plan
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Graphical view of each query block
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Now you see the graphical view of t
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8.5.4 Example of using the query an
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9.1 The BIA architecture Figure 9-1
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Figure 9-2 BIA t-shirt sizing The B
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From the hardware side, the BIA is
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9.7 Overview The improvement of the
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10.1 SQL efficiency for DSO Queries
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Page 242 and 243: 10.4 Indexing of InfoCubes The defa
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Page 246 and 247: 11.1 Background To be more competit
Page 248 and 249: 11.2.1 Hardware and software config
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Page 256 and 257: 11.4 Test case scenarios The goal o
Page 258 and 259: Figure 11-3 explains the data flow
Page 260 and 261: In addition, each cube, especially
Page 262 and 263: Measurement results Table 11-2 prov
Page 264 and 265: Figure 11-7 shows the average CPU u
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Page 276 and 277: Metadata plays a fundamental role i
Page 278 and 279: 12.3 Dataflow in SAP BI Data flow i
Page 280 and 281: 12.4 Information access As previous
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Page 286 and 287: 12.8 Aggregates 4. The dimension ta
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source object element Component of
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elements to be displayed in the Web
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Web application object catalog Web
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You insert one or more queries in t
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IT scenario The environment we used
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Checks under z/OS and DB2 We recomm
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To check that the SAPCL procedure w
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List of SAP BW OSS Notes Table C-1
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SAP BI on DB2 for z/OS 390016 BW Da
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Network configuration: static multi
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START CU091060 START CU0A1070 START
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Table name Average row length Total
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Table name Average row length Total
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Parameters BPK16K0 BPK16K1 VIRTUAL
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drwxr-xr-x 3 t11adm sapsys 2048 200
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Example D-6 shows a cube index crea
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B49/S_RTNSVAL 24 DECIMAL 17 2 N 4 N
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-rw-r--r-- 1 t11adm sapsys 2084482
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-rw-r--r-- 1 t11adm sapsys 547449 2
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Online resources These Web sites ar
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Help from SAP SAP Support and downl
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C Candidates for partiioning Data W
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M master data 155, 211, 258, 277 si
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star schema 265-266 also see InfoCu
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Best Practices for SAP BI using DB2
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Best Practices for SAP BI using DB2 9 for z/OS - IBM Redbooks

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