2. MARCO CONCEPTUAL
2.1 REFORMA AGRARIA
Now that you’ve developed and activated the required attribute views, you can now begin the process of designing an analytic view. In the analytic view, you’ll define the foundation table, join the foundation to attributes, and define measures to facilitate the aggregation of the results.
Designing the Data Foundation
Follow these steps to create an analytic view and define the Data Foundation node:
1. Right-click the INTERNETSALES subpackage, and choose New • Analytic View.
2. Using Table 6.33 as a reference, define the properties for the attribute view.
Property Value
Name AV_INTERNETSALES
Description The Internet Sales Analytic View Package saphana.commonattributes View Type Analytic View
Copy From Unchecked
SubType N/A
Table 6.33 Properties for the ATBV_BASE_DATE Attribute View
3. Click the Data Foundation node located on the left side of analytic view design window, as shown in Figure 6.40.
Figure 6.40 An Example Analytic View Design Window
4. In the Details pane, right-click the white space, and choose Add.
5. Using the provided Search field, enter “FACT_INTERNET_SALES”. Once found, click the FACT_INTERNET_SALES object listed in the Matching Items pane.
Click OK to add the table to the Details pane.
6. Highlight the columns using Table 6.34 as reference. To select multiple columns, hold down the (Ctrl) key, and click each column.
7. Right-click the highlighted columns, and choose Add to Output.
Column Name
FACT_INTERNET_SALES.SALESORDERNUMBER FACT_INTERNET_SALES.SALESORDERLINENUMBER FACT_INTERNET_SALES.CUSTOMERKEY
FACT_INTERNET_SALES.PRODUCTKEY FACT_INTERNET_SALES.ORDERDATEKEY FACT_INTERNET_SALES.FREIGHT
FACT_INTERNET_SALES.PRODUCTSTANDARDCOST FACT_INTERNET_SALES.TAXAMT
FACT_INTERNET_SALES.ORDERQUANTITY FACT_INTERNET_SALES.TOTALPRODUCTCOST FACT_INTERNET_SALES.EXTENDEDAMOUNT FACT_INTERNET_SALES.UNITPRICEDISCOUNTPCT FACT_INTERNET_SALES.DISCOUNTAMOUNT FACT_INTERNET_SALES.SALESAMOUNT FACT_INTERNET_SALES.UNITPRICE
Table 6.34 The Internet Sales Data Foundation Columns
Designing the Logic Joins
Follow these steps to define the logical joins between an analytic view and exist-ing attribute views:
1. Click the Logic Join node located on the left side of analytic view design win-dow.
2. In the Details pane, right-click the white space, and choose Add.
3. Using the provided Search box, search for the following attribute views:
E
E ATBV_CUSTOMER
E
E ATBV_PRODUCT
E
E ATBV_ORDERED_DATE_HIER
E
E DATBV_ORDERED_DATE
Click OK to add each attribute view to the Logical Join pane.
4. Join each attribute view to the Data Foundation table by right-clicking the white space in the Details pane and selecting Create Join.
5. Using Figure 6.41 as a reference, create the join between the Data Foundation table and the ATBV_CUSTOMER attribute view.
Figure 6.41 The Join between the Data Foundation Table and the Customer Attribute View 6. Using Figure 6.42 as a reference, create the join between the Data Foundation
table and the ATBV_PRODUCT attribute view.
Figure 6.42 The Join between the Data Foundation Table and the Product Attribute View
7. Using Figure 6.43 as a reference, create the join between the Data Foundation table and the ATBV_ORDERED_DATE_HIER attribute view.
Figure 6.43 The Join between the Data Foundation Table and the Ordered Date Hierarchy Attribute View
8. Using Figure 6.44 as a reference, create the join between the Data Foundation table and the DATBV_ORDERED_DATE derived attribute view.
Figure 6.44 The Join between the Data Foundation Table and the Ordered Date Derived Attribute View
9. Update the alias name for the columns in the derived attribute view ordered date. Because a derived attribute acts as an alias of an existing attribute view, it’s recommended that each of the columns in a referenced derived attribute view be aliased as well.
10. In the Logical Join node, locate the Output pane on the right side. Expand the Attribute View • DATBV_ORDERED_DATE node.
11. Click each column in this node, and locate the Properties pane.
12. In the Properties pane, locate the Alias Name and Alias Label fields. Using Table 6.35 as a reference, update both alias fields with the same value.
Attribute View Column Name Alias
DATEKEY ORDERED_DATE_KEY
FULLDATEALTERNATEKEY ORDERED_DATETIME
CALENDARYEAR
CALENDARQUARTER ORDERED_CAL_QTR
CALENDARQUARTERYEAR CALENDARYEARMONTH
Table 6.35 The Alias Name and Alias Label Cross Reference Table
Attribute View Column Name Alias
ENGLISHMONTHNAME ORDERED_CAL_MONTH
WEEKNUMBEROFYEAR ORDERED_CAL_WEEKNUM
ENGLISHDAYNAMEOFWEEK ORDERED_CAL_DAYNAME
DAYNUMBEROFMONTH ORDERED_CAL_DOM
DAYNUMBEROFWEEK ORDERED_CAL_DOM
FISCALQUARTER ORDERED_FISCAL_QTR
FISCALYEAR ORDERED_FISCAL_YEAR
Table 6.35 The Alias Name and Alias Label Cross Reference Table (Cont.)
Note that for CALENDARQUARTERYEAR and CALENDARYEARMONTH, the col-umns don’t need an alias because they are hidden.
Designing the Semantic
In the Semantics node we’ll finalize the configuration of the analytic view by defining attributes and measures. We’ll also hide columns.
1. Click the Semantics node located on the left side of analytic view design win-dow. Figure 6.45 contains an example of the window you should now see.
2. Locate the Columns pane located just below the Details pane.
3. Click the Local tab in the Column pane.
4. Configure the Sales Order Number and Sales Order Line Number output columns as attributes. Using the (Ctrl) key, highlight both columns. In the Columns pane, locate the Attribute icon located on the right side header. Click the icon to convert the highlighted columns into attributes.
5. Configure the remaining columns as measures. Using the (Ctrl) key, highlight the remaining columns. In the Columns pane, locate the Measure Icon located on the right side header. Click the icon to convert the highlighted columns into measures.
6. Click the Shared tab in the Column pane. This pane allows you to manage the columns returned from the connected attribute views.
Figure 6.45 Semantics Node Window
7. Locate the NAMESTYLE, PRODUCTLINE, CAL YEAR, CAL QUARTER YEAR, and CAL YEAR MONTH columns. To the right of each column locate the Hidden column. Place a checkmark in the provided box to hide these columns.
8. When complete, save and activate the analytic view by clicking the Save and Activate button on the toolbar.
9. Preview the analytic view using the Data Preview (small magnifying glass image overlooking a table) icon located to the right of the Save and Activate icon.
The analytic view and attribute views are now ready for consumption by the SAP Business Objects tools. In subsequent chapters, we’ll examine how each SAP Business Objects tool connects; the analytic view will be the main connection point to the Internet sales data in SAP HANA. Users will be able to use this information view to analyze their Internet sales transactions by product, customer, and ordered date.
Let’s consider another case study that better explains why you need calculation views to facilitate multidimensional analysis. In the next case study, we’ll walk you through both the requirements and the solution for solving a common query issue using a calculation view.
A
Access control, 522 Access level, 553
Active Directory, 525, 544, 554, 556, 562 Adaptive Job Server, 552
Adaptive processing server, 552 Ad hoc reporting, 60, 61
Administration Console perspective, 90 Adobe Acrobat, 749
Adobe Flash, 683 Adobe Flex, 707, 710 Advanced analytics, 463
AdventureWorks Cycle Company, 109, 261, 313, 416, 443, 489, 507, 556, 635, 694,
Analytic privileges, 391, 540, 561, 592 assign restrictions, 396
attribute restrictions, 396 reference models, 395 Analytics appliance, 28
Analytic view, 358, 611, 714, 800, 801 calculated column, 367
copy from, 361 create, 359
Data Foundation node, 361, 362 Hidden column, 371
input parameter, 372 Label column, 371
Analytic view (Cont.) local attribute, 371 Logical Join node, 361, 364 measure, 358
Application Function Libraries (AFL), 477, 482
Application privileges, 541 Application request, 528 Application response, 528 Apriori
PAL algorithm, 478, 508, 511 AS_REQ, 528
Association
PAL algorithm, 478, 508, 511 Atomicity, consistency, isolation, and
durability (ACID), 32 compliance, 32
Attribute view, 344, 614, 801 calculated column, 350
Authentication, 521, 522, 529, 532, 536 authentication mode, 591
authentication service, 526, 527, 528 authentication service request, 528 authentication service response, 528 Authenticator, 526, 528
Authorization, 86, 521, 522, 523, 529, 540 Authorized for delegation, 528
B Building an SQL statement, 752 Bulk load, 176, 240
Business analytics, 464
Business Function Library (BFL), 477 security, 484
Calculation view, 374, 612, 714, 743, 800, 801
Central Management Console (CMC), 546, 547, 597, 714
Central processing unit, 31
Change data capture (CDC), 49, 240, 246 source based, 242, 244, 268
target based, 242 Columnar table, 29, 41, 616
Column profiling, 120, 125, 128, 142, 819 advanced, 131
basic, 131
Column store table, 83, 89 inserts, 81
Comma-separated value (CSV), 719, 749, 770 Cube, 326
the need for ad-hoc acccessaccess, 704 Data flow
business rules validation stage, 175, 269 driver stage, 169, 269
lookup stage, 171, 269 parsing stage, 170, 269 Data foundation, 604, 755 Data Foundation node, 347, 362 Data mart, 56, 58, 98, 101, 102
SAP HANA-specific, 105 Data mining, 463
Data model, 50, 58, 98
Data modeling in SAP HANA, 97
Data source name (DSN), 578, 591 Data standardization, 267 Datastore, 154
Data warehouse, 51, 56, 58 Decision tree
PAL algorithm, 477 Delegation, 528 Delta load process, 49
Denormalization, 102, 103, 105, 106, 268, 406, 410
Descriptive analytics, 464 Design-time object, 88 Desktop Intelligence, 751
Desktop Intelligence Compatibility Kit, 752 Detail, 755
Digital signature, 531 Dimension, 58, 324, 755 Dimension table, 77, 78, 164
create, 111
Direct Extract Connect (DXC), 48 Disparate systems, 142 Enterprise information management (EIM),
28, 56
Error handling, 177 ERROR LOG, 192
Error logging, 280 Errors, 156
Extract, transform, load (ETL), 37, 50, 73, 93, 140, 226, 236
File Transfer Protocol (FTP), 766 Flash, 682
H
Identity provider, 530, 531, 537, 539, 549 initiated SSO, 530
Inferred SQL, 754 Infommersion, 672
Information Design Tool (IDT), 585, 714, 755, 796
repository resource, 587 universe, 795
Information Space, 711, 714, 716 Creation Wizard, 717
index, 744
Information view, 344, 608, 616 In-memory database, 28, 34
Java database connectivity (JDBC), 403, 577, 754, 788, 792, 795
database classname, 792 URL, 792
JOIN_BY_SQL, 757, 776 Join Engine, 31, 630, 631
K
Kerberos, 86, 521, 522, 523, 525, 527, 530, 533, 534, 535, 543, 546, 550, 554, 556, 557, 560, 592
authentication, 546 SSO, 559
Key Distribution Center (KDC), 526, 527, 528 Key performance indicator (KPI), 667, 694 KeyTab, 527, 534, 551, 557
Knowledge discovery, 464 krb5.ini, 551, 558
L
Latency, 759
LDAP, 544, 545, 554, 562 Left outer join, 365
Metadata, 52, 60, 123, 149, 151, 153, 326, 815, 816
Microsoft Excel, 749 Miscellaneous
PAL algorithm, 478
Mobile, 682, 693, 703, 705, 769, 804 Mobile component, 683
MODELER, 397
Modeler perspective, 331 MOLAP, 326
Multidimensional model, 41, 48, 53, 251,
online analytic processing (OLAP), 324 star schema, 324
Object Linking and Embedding Database for OLAP (OLE DB for OLAP), 403, 577 ODBC manager, 578
OLAP Engine, 31, 630, 632
Online analytical processing (OLAP), 75, 95, 97, 98, 764
connection, 596 data storage, 77 hierarchy, 801 modeling, 98
modeling in SAP HANA, 102 multidimensional OLAP, 324, 326
Online transaction processing (OLTP), 76, 94, 97, 172, 406
Open database connectivity (ODBC), 403, 577, 754, 788, 795 Parallelization in SAP HANA, 92 Parallel processing, 52
implementation, 468, 472, 486, 487, 502, 513
Predictive Analysis Library (PAL), 477 implementation, 489
installation, 482 security, 484
Predictive model, 462, 472 maintenance, 473
Predictive Model Markup Language (PMML), 488
Preprocessing PAL algorithm, 478
Professionally authored dashboard, 705 compared to self-service analytics, 705 Profiling, 818
Q
Quality assurance, 670 Query as a Service, 514, 677 Query Browser, 674
Query drilling, 757, 762, 778 Query Panel, 674
Query processing engine, 31 Query stripping, 757, 764, 777 Quick Launch view, 332, 334
R
R installation, 484
Random access memory (RAM), 30 Real time, 158, 782
Relational connection, 596, 795, 796 Relational database management system
(RDBMS), 37, 55, 73, 92, 406 Relational OLAP (ROLAP), 327
Relationship profiling, 125, 134, 136, 137 Repository connection, 602
Requirements gathering, 666, 694 Restricted column, 368
Rich Internet Application (RIA), 756 Right outer join, 366
Role, 85, 391, 541, 562 Role-based security, 541 Row engine, 31 Row-level security, 391 Row store table, 83, 94
use case, 96
R (programming language), 478 Rserve, 479
installation, 482
S
SAP BusinessObjects, 28, 43, 56, 59, 704 SAP BusinessObjects Analysis for OLAP, 597 SAP BusinessObjects BI Mobile, 772, 769 SAP BusinessObjects CMS, 708
SAP BusinessObjects Credential Mapping, 715 SAP BusinessObjects Design Studio, 62, 684,
720 SAP BusinessObjects Explorer (BEx), 555,
703, 706, 750 backend services, 708 calculated measure, 718 connecting to SAP HANA, 714 creating an Information Space on SAP
HANA, 716
indexing on SAP HANA, 711 index structure and storage, 713 Information Space, 707 SAP BusinessObjects universe, 706, 714 SAP BusinessObjects Web Intelligence, 555,
749, 773
predictive model implementation, 514 SAP Business Suite application, 54 SAP Crystal Reports, 62, 555, 782, 783
analytic model, 800
SAP Crystal Reports (Cont.)
improved HANA connections in 4.1, 806 mobile, 804
sharing in SAP BusinessObjects BI Launchpad, 803
sharing via OpenDocument, 803 _SYS_BIC naming conventions, 801 _SYS_BIC Views, 800
SAP Crystal Reports 2011, 783, 784, 790 connecting to SAP HANA, 788 Database Expert, 786, 790 database middleware, 788 Field Explorer, 787
retrieving data from SAP HANA, 792 SAP HANA JDBC connections, 791 SAP HANA ODBC connections, 789 show SQL Query, 787
user interface, 786
SAP Crystal Reports for Enterprise, 783, 784, 795, 807
SAP HANA connection options, 795 SAP HANA ODBC and JDBC connections,
796
SAP HANA relational connections, 796 SAP HANA universe connections, 799 user interface, 794
SAP Dashboards, 62
SAP Data Services, 27, 43, 50, 56, 147, 323 Auto Correct Load, 287, 301
batch job, 230, 261 BI scheduler, 254
built-in functions, 168, 202, 203, 204, 205, 207
Case transform, 188, 276 custom functions, 207
Data_Cleanse, 194, 195, 197, 248 data flow, 168
data flow processing, 169, 170, 171, 175 datastore, 151
Exec() function, 317 file format, 210, 211 job, 155
job execution controls, 232, 233 job recovery, 160, 163
job structure best practices, 262 Key_Generation, 287, 296
SAP Data Services (Cont.) Management Console, 257 Map_Operation, 189 Match Editor, 199
Match transform, 197, 198, 199, 250 merge, 279
metadata, 149
parallel execution, 158, 159 Query transform, 179
real-time job, 214, 215, 257, 313 reusability, 167
Table_Comparison, 186, 287, 288, 291, 295
transform, 168, 179, 181, 182, 183, 184, 187, 188, 189, 191, 192
Try/Catch, 263
Validation transform, 191, 281 work flow, 158
SAP Data Services Designer, 148
SAP Data Services Workbench, 215, 218, 216, 220
SAP GUI, 685
SAP HANA, 28, 665, 674, 693, 703, 705, 708, 719, 749, 781, 783, 786, 799
analytic privilege, 328 analytic view, 328 attribute view, 328
Calculation Engine, 368, 613, 630, 632, 633
SAP HANA (Cont.) software layers, 28 sizing, 30
table, 328, 335, 337 web application server, 35
SAP HANA Extended Application Services, 405
SAP HANA native, 36, 40 SAP HANA OLAP Engine, 350 SAP HANA-R integration, 478, 489
implementation, 497 installation, 482 security, 485
SAP HANA Studio, 53, 329, 334, 720, 801 Administrative perspective, 336
Quick Launch, 332, 333, 334 security, 333
system object, 332
SAP Information Steward, 125, 194, 815 SAP Landscape Transformation (SLT), 44, 124,
226, 227, 782 SAP Logon Ticket, 523
SAP Lumira, 479, 703, 704, 719, 720, 729 cell size warning, 726
SAP NetWeaver Business Warehouse 7.3 powered by SAP HANA, 36, 37 SAP NetWeaver BW, 30, 685, 693, 750 SAP NetWeaver BW Accelerator (BWA), 37 SAP Predictive Analysis, 470, 479, 489, 508,
720 SAP Sybase, 725, 726, 727
Hilo.db, 727 iqsrv15.exe, 726 Schema, 84, 110, 237 Scope of analysis, 778
scope of analysis panel, 762 Scrum, 666
SDK, 805
Security Assertion Markup Language (SAML), 85, 521, 522, 523, 529, 530, 536, 543, 544, 546, 547, 550, 554
assertion, 531, 539, 548, 549 Security credential, 522
Security Token Service (STS), 550 Self-service, 61, 668, 703, 705, 706 Self-service analytics, 703
OLAP, 704
Web Intelligence, 704 Self-service BI, 60
Semantic layer, 60, 674, 719 Semantics node, 347
Service Principal Name (SPN), 527, 534, 551, 557, 560
Service provider, 530, 531, 537 initiated SSO, 530
Service ticket, 527, 528 Session key, 526, 527, 528 SharePoint, 682
Sharing, 681, 690, 729, 766
Single Sign-On (SSO), 521, 523, 544, 592, 611, 715
Software as a Service, 530 Source independence, 266
Source system analysis (SSA), 58, 119, 120, 121, 123, 815
SQL privileges, 540
Staging, 233, 234, 235, 266 Star schema, 324
Ticket Granting Service (TGS), 526 request, 528
response, 528
Ticket Granting Ticket (TGT), 526, 527, 528 Time series
Universe, 60, 674, 719, 767, 799 predefined filter, 809
Universe Designer, 585 Universe Query panel, 808 UNIX, 789
User, 85
User name and password, 591 User provisioning, 521, 522, 542 User role, 87
Web service, 256, 514, 677 With Root Node property, 355
X
X509 authentication, 523 XS Engine, 28, 50, 405, 513, 541