TZH300_EN_COL96

SAP HANA Implementationp
and Modeling
TZH300 Implementation and Modelingp g
Collection 096 Rev 14 V1

Agendag
SAP HANA Implementation and Modeling
• Unit 1: Data Provisioning using SLT
• Unit 2: Approaching SAP HANA ModelingUnit 2: Approaching SAP HANA Modeling
• Unit 3: Connecting Tables
• Unit 4: Advanced Modeling
• Unit 5: Processing Information Models
• Unit 6: Managing Modeling Content
• Unit 7: Security and Authorizations• Unit 7: Security and Authorizations
© 2012 SAP AG. All rights reserved. 2

Positioning and Key Concepts
Unit 1: Data Provisioning using SLT
Data Replication at a Glance
Overview on Configuration Aspects
SLT based transformation concepts
Transformation of Data
SLT based transformation concepts
Selective Data Replication
Extension of (target) table structure
Specific Considerations

Objectives
Positioning and Key ConceptsPositioning and Key Concepts
At the end of this Lesson you will be able to:At the end of this Lesson you will be able to:
• Explain the positioning of SAP LT Replication
ServerServer
• Describe the key concepts and features
• List the prerequisites and how to set up the
SAP LT Replication Server
• Name the benefits of the trigger-based replication
approach

Overview
This module covers the following topics:This module covers the following topics:
• Product name, positioning and key benefits
C i l t d ft hi t• Commercial aspects and software shipment
• Overview on key concepts, features and user interfaces
• Overview on key installation and configuration stepsy g p

Product Name
SAP LandscapeSAP Landscape
TransformationTransformation
Replication Serverp
for SAP HANA

SAP LT Replication Server for SAP HANA Leverages
Proven SLO TechnologiesProven SLO Technologies
Application Lifecycle ManagementSLO* t h l i h b d Application Lifecycle Management SLO* technologies have been used
since more than 10 years in
hundred of projects per year
K ff i f t SAP‘ Key offerings foster SAP‘s
Application Lifecycle Management
concept
SAP LT R li ti S SAP LT Replication Server - as a
new use case - leverages several
SLO technologies
*) System Landscape Optimization

Positioning and Key Benefits of SAP LT Replication Server
for SAP HANAfor SAP HANA
Key Benefits of the Trigger-Based Approach:
• Allows real-time (and scheduled) data replication, replicating only
relevant data into HANA
• Ability to migrate data into HANA format while replicating data in real-
time
• „Unlimited“ release coverage (from SAP R/3 4.6C onwards) sourcing
data from SAP ERP (and other ABAP based SAP applications)
• Leverages proven SLO technology (Near Zero Downtime, TDMS, SAP
LT)
• Simple and fast set-up of LT replicator (initial installation and
fi ti i l th 1d ) d f ll i t t d ith HANA d lconfiguration in less than 1day) and fully integrated with HANA modeler
UI
SAP LT Replication Server is the ideal solution for all HANA
customers who need real-time or scheduled data replication
sourcing from SAP and NON-SAP sources

Commercial Aspects and Software Shipmentp p
Commercial Aspects
SAP LT Replication Server for SAP HANA will be
part of SAP HANA software license modelpart of SAP HANA software license model
Software ShipmentSoftware Shipment
SAP LT Replication Server for SAP HANA will be
part of SAP HANA software shipment and fully
integrated into the SAP HANA modeler UIintegrated into the SAP HANA modeler UI

Overview - Trigger-Based Approach
SAP LT Replication Server does not have to be a separate
SAP system and can run on any SAP system with SAPSAP system and can run on any SAP system with SAP
NetWeaver 7.02 ABAP stack (Kernel 7.20EXT)
HANA Studio
Application Table
Trigger Based
Delta Recording
Replication
Engine
RFC
Connection
DB
Connection
Replication Configuration
SAP HANA systemSAP source system SAP LT Replication Server
Application Table
yy p
Connection(s) between source system and SAP HANA system are
defined as “Configuration” on the SAP LT Replication Server
Data load and replication are
triggered via SAP HANA Studio

Architecture and Key Building Blocks
Read module Structure mapping &
Transformation
RFC
Connection
Application table Logging table
DB trigger
Application table
Write module
DB
Connection
SAP HANA systemSAP LT Replication ServerSAP source system
Efficient initialization of data
replication based on DB trigger
Flexible and reliable replication
process, incl. data migration
Fast data replication via DB connect
LT replication functionality is fully
and delta logging concept
(as with NearZero downtime approach)
process, incl. data migration
(as used for TDMS and SAP LT)
LT replication functionality is fully
integrated with HANA Modeler UI

Architecture for Non-SAP Source Replication
Read module
Structure mapping &
T f ti
DB
Connection
DB trigger
Transformation
Application table
Write module
DB
Connection
SAP HANA systemSAP LT Replication ServerNon SAP source system
In a first step, SAP LT Replication Server transfers all metadata table definitions from the non-SAP
source system to the HANA system. From the HANA Studio perspective, non-SAP source replication
works as for SAP sources. When a table replication is started, SAP LT Replication Server creates
logging tables within the source system. As a difference, the read modules are created in the SAP LT
logging tables within the source system. As a difference, the read modules are created in the SAP LT
Replication Server. The connection the non-SAP source system is established as a database connection.

Multi System Support
System A
System B
Schema 1
Schema 2
Source systems are
connected to separate
HANA schema on the
same HANA System
SAP HANA systemSource systems
y
Source systems are
SAP HANA tS t
System A
System B
Schema 1
Schema 1
Source systems are
connected to separate
HANA systems.
Schema name can be
equal or differentSAP HANA systemSource systems equal or different
Source systems are
SAP HANA systemSource systems
System A
System B
Schema 1
Source systems are
connected to same
HANA system and
also the same
schema
SAP HANA systemSource systems schema

Set-up of LT Replication Server
Installation aspects
Source system(s): use respective DMIS add-on
LT replication server: use add-on DMIS_2010_1_700 with SP05;
other system requirements (NW 7.02; SAP Kernel 7.20EXT) apply
Apply SPS03 for SAP HANA 1 0
Configuration steps for SAP LT Replication Server
Apply SPS03 for SAP HANA 1.0
Define a schema for each source system
Define connection to source system
Define DB connection into SAP HANA
Define replication frequency (real-time; frequency for scheduled replication)
Define maximum number of background jobs for data replication
Set-up of data replication in SAP HANA
Select relevant source system
Start (initial load only and / or continuous replication)

Key Configuration Steps
Call SAP LT Replication Server
Configuration (Transaction: LTR)
Define configuration data

Starting the Data Replication
Choose data provisioning to
launch SAP HANA Modeler UI
1. 1. Select source system as defined in SAP1. 1. Select source system as defined in SAP
LT Replication Server; related system
information and schema will be displayed
2. 2. Use button Load and / or Replicate to set
th d t li tiup the data replication
3. 3. Use button Stop Replication to finish
replication
4 U b tt S d t li ti4. 4. Use button Suspend to pause replication
5. 5. Use button Resume to continue replication

Database - Supportability Matrix
SAP LT Replication Server for SAP HANASAP LT Replication Server for SAP HANA
D t b
Technical availability
Database
SAP Sources Non SAP Sources (*)
MSFT SQL Server Enterprise Edition OK OK
Oracle Enterprise Edition OK OK
IBM DB2 LUW/ UDB (DB6) OK OK
IBM DB/2 zSeries OK OK
IBM DB2 iSeries (former AS/400) OK Not yet supported
IBM Informix Planned for HANA SPS4 Not supportedo pp
SAP MaxDB OK OK
Sybase ASE Available until End of January 2012 Planned for HANA SPS4
For non-SAP source systems, the customer database license needs to cover a
permanent database connection with 3rd party products like SAP LT Replication
Server.
(*) Since a DB connection from SAP LT replication server to a non-SAP system is required, the OS/DB restrictions of SAP
NetWeaver 7.02 apply (see at http://service.sap.com/pam)

Roadmap at a Glance
SAP LT Replication ServerSAP LT Replication Server
• Several conceptual enhancements in
alignment with SAP HANA development
• Replication of tables into multiple HANA
systems
• User experience and usability
• More integrated monitoring capabilities, e.g.
status and replication logs in SAP HANA andstatus and replication logs in SAP HANA and
SAP Solution Manager
• Improved operations aspects
R li ti it• Replication repository
• Transactional integrity

Summary
You should now be able to:You should now be able to:
• Explain the positioning of SAP LT Replication
ServerServer
• Describe the key concepts and features
• List the prerequisites and how to set up the
SAP LT Replication Server
• Name the benefits of the trigger-based replication
approach

Objectives
Overview on configuration aspectsOverview on configuration aspects
• Describe the set-up of a configuration on the
• Explain the impact of the configuration set-up on the
data replication

Concept: Define Configuration / Schema
Schema 1
Trigger-based
delta recording
Replication
engine
RFC
Connection
Replication Configuration
DB
Connection
SAP HANA systemSAP source system SAP LT Replication Server yy p
A new configuration can be created in the LT Configuration and Monitoring Dashboard. In that step, the connection
between the source and the HANA system is established and the target schema will be created (if it doesn’t exist already).
Also replication control tables are created and table lists are replicated from the source system. In addition, the required
l d GRANT / REVOKE d d
roles and GRANT / REVOKE procedures are generated.

Creating a New Configuration for SAP Sources
General Data
D fi th li ti t t S h N i th HANADefine the replication target Schema Name in the HANA
system (if schema does not exist, it will be created
automatically)
Define the Number of Replay Jobs used for data load
and replicationand replication
Connection to the source system
SAP Source System: Use previously defined RFC
destination to source systemy
Non SAP Source System: Select the source database
system and set the required fields (see also next slides)
Connection to HANA systemy
•Define the User Name and Password which can be used to connect to the HANA system (see also next slide)
•Define the Host Name and Instance Number of the target HANA system
Table space assignmentp g
Optional: define table space for logging tables. If no table space is defined, logging table will be created in the same table space
as the original table. Own table space is recommended for easier monitoring of the table sizes of the logging tables
Replication Mode
p
Replication can be executed in Real-time mode or in Scheduled mode

Creating a New Configuration for Non-SAP
Sources
Overview on configuration aspects
To replicate from non-SAP source
system select Legacy and the
affected database systemaffected database system.
Depending on the database system,
dditi l i d i f ti dadditional required information needs
to be specified (e.g. for DB2 specify
the DB connection and the table
space name).
space name).
Start with transaction LTR

Results of Creating a New Configuration
When the popup to create a new configuration is closed by
pressing the OK button the following actions are performedpressing the OK button, the following actions are performed
automatically:
 Configuration settings are saved on the LT Replication Server
 New user and schema are created on the HANA system with the defined
t t h ( t f d if i ti h i d)target schema name (not performed if an existing schema is reused)
 Replication control tables (RS_* tables) are created in target schema
 User roles for the target schema are created:
 <target_schema>_DATA_PROV -> Role to manage data provisioning
 <target_schema>_POWER_USER -> Contains all SQL privileges of the target
schema
 <target_schema>_USER_ADMIN -> Role to execute authority procedures (see
below)
 A procedure to grant (RS GRANT ACCESS) or revoke A procedure to grant (RS_GRANT_ACCESS) or revoke
(RS_REVOKE_ACCESS) are created in the target schema
 Replication of tables DD02L (stores the table list) and DD02T (stores the
table short descriptions) is started automatically. Once those tables are
replicated , the HANA studio knows which tables are available in the sourcep ,
system
 SYS_REPL and table RS_REPLICATION_COMPONENTS are created
(if they don’t exist already based on a previous configuration)
 Replication is registered in table RS REPLICATION COMPONENTS
 Replication is registered in table RS_REPLICATION_COMPONENTS

Summary
• Describe the set-up of a configuration on the
• Explain the impact of the configuration set-up on the data
replication

Objectives
Data Replication at a GlanceData Replication at a Glance
• Explain the different option for data replication and the
related implicationsrelated implications

Overview
Data provisioning at a glanceData provisioning at a glance
• Load and replicate data
• Suspend and resume data replication
• Status monitoring in HANA Studio

Launch Data Provisioning UI
All further data provisioning steps
t d f th HANAare executed from the HANA
Studio. Therefore, switch to the
HANA Studio, choose the
perspective Information Modelerp p
and start the quick launch.
Select your system and start the
Data Provisioning via the link ing
section DATA.
If you cannot enter this screen or
do not see any data, verify if a data
provisioning role was assigned to
your user.

Start Load / Replication
Press the respective button to Replication includes load
and delta replication
Load the current data of a table from the source system
Replicate a table includes the load of the current data and the replication of all changes
in the source system
and delta replication
Choose a table from
the list or enter a
search string to
search for a specific
table.
Use the button AddUse the button Add
to select the table.
Once all relevant
tables are selected,
load / replication is
triggered when popup
is closed via button
Finish.

Start Load/Replication - Executed Activities
Read module Structure mapping &
transformation
RFC
Connection
HANA Studio
Application Table Logging Table
DB Trigger
Application Table
Write module
DB
Connection
Data provisioning can be managed via the HANA Studio modeler view using the data provisioning. If a
table is started for load or replication, the LT runtime objects (reader, mapping and transformation and
writer modules) are generated in the respective systems. If tables are selected for replication, also the
delta recording (logging table and DB trigger) is activated on the source system.

Stop / Suspend Replication
Press the respective button to
Stop the replication and also to stop delta recording for that table (deletes DB trigger!)
Suspend data replication but keep delta recording active
Resume a previously suspended data replicationp y p p
The table selection popup will look similar as for Load and Replication. In case of Stop Replication or
Suspend, only those tables can be selected which are already in replication mode. In case of Resume only
those tables can be selected which are in suspend mode.
Please be aware that in case a replication is stopped and started again, the
corresponding table will be dropped. The initial load must be repeated as delta
recording was deactivated for a certain time and changes might not be recorded.
So in case you only want to pause the delta replication use the mode Suspend andSo in case you only want to pause the delta replication, use the mode Suspend and
Resume as delta recording is not deactivated and the replication can be continued
without a need of a new initial data load.

Stop/Suspend Replication - Executed Activities
Stop data replication
Delete trigger & Log. tab.
Application Table Logging Table
DB Trigger Application table
Stop replication
Stop data replication
DB Trigger
Application table
Suspend replication
Continue data replication
DB Trigger
Application table
Resume replication

Status Monitoring in HANA Studio
The load / replication status can be monitored in the Data Load Management
ithi th d t i i i t l I thi ti th t t t f llscreen within the data provisioning tool. In this section, the current status of all
relevant tables of the selected source system / target schema is displayed.

Summary
• Explain the different option for data
replication and the related implicationsreplication and the related implications

Objectives
SLT based transformation conceptsSLT based transformation concepts
• Explain the basics of the SLT based transformation
conceptsconcepts
• Leverage the data processing and transformation process
• Explain which tables and fields has to be specify for a
transformation rule

Overview
• Details on the basic concept of SLT based transformation
O f f f• Overview on key tables and field to be specified for a
transformation
• Naming conventions to be used for a transformation rule
• Trigger conditions in detail

Concept
The main purpose of SAP HANA is reporting data of particular ERP content.
Generally this will be done with an one-to-one replication of ERP table into the
new database.
Depending on the customer-specific requirement, it’s sometimes necessary to
filter, to change, or to extend the original data within the load process into HANA.
Key Use Cases:y
• Conversion
 Change data and/or table-structure within datag
replication/loading
• Filtering
 Reduce number of records to be replicated

Concept
Currently, transformation & filtering topics
are only provided by project base.
(Only for SAP lead projects)

Concept - Data Processing
Start of Replication Cycle
Get next portion from source
1
2
RFC
Connection
…
Loop through portion
4
3
Map fields of a record to
sender structure
DB
Connection
…
S t SAP LT R li ti S
Data Portions
5 Add record to sender portion
SAP HANA t
Data Portions
Source system SAP LT Replication Server SAP HANA system
 The data for transformation will be split into portions (default: load: 10,000 lines, replication: 5,000 lines)
 The portions will be processed successively, mapped, and transferred to sender
 Extension of functionality (for example data conversion) is possible at several points of the process using
ABAP includes  Transformation rules
 Implementation of these rules by maintaining table IUUC_ASS_RUL_MAP of the LT Replication Server

Implementation of Rules within Transformation
Process SLT based transformation conceptsProcess SLT based transformation concepts
Steps of implementation:p p
1. Create ABAP include containing the coding to change fields
 Usage of naming convention for parameter and table fields to
h d t t b li t dchange data to be replicated
2. Maintain table IUUC_ASS_RUL_MAP to use the defined rule
 Generally there are two types of transformations rules Generally, there are two types of transformations rules
Event-based rules
More flexible than parameter-based rules
Knowledge of data processing within the SAP LT Replication Server needed to
select the right event for the specific business scenario
Parameter-based rules
Less flexible than event-based rules
Easy to create by using parameters

Fields of IUUC_ASS_RUL_MAP
Fields of table IUUC_ASS_RUL_MAP (residing on SAP LT Replication Server)
MT_ID The scheme name has a corresponding mass transfer ID. Enter “SAP” if you want to create a
template for all mass transfer IDs created in future or enter an existing mass transfer ID if you
want to restrict the settings to a specific mass transfer.
To get the MT_ID for your schema name (=”Mass transfer XXX”), use Transaction: LTR
TABNAME Name of the table of sender system
BASISVERSION Enter the Basis Release of Source System (for example: 620, 700, … ). You can obtain it from:
S t  St t  Cli k ‘C t I f ti ’  SAP BASIS l )System  Status  Click on ‘Component Information’  SAP_BASIS release).
For non-Sap sources, use the SAP_BASIS release of the SLT system.
EVENT (not used for parameter rules)
Enter event (BOP, BOT etc.) for event-based rule.
FIELDNAME (not used for event-based rules)
Enter the name of the receiver field the mapping rule should be assigned to
IMP_PARAM_1..3 (not used for event-based rules)
Use those columns to define the import parameters you need. You can either set a sender fieldp p y
name into this column or a literal (use quotes for literals, for example, ‘1000’)
If no parameter has to be used within the rule, IMP_PARAM_1 has to be set to ‘DUMMY’!
INCLUDE Name of the include containing the mapping rule. Name can be defined individually.
Recommended naming convention: ZIUUC_<tab_name>_<fieldname> (that is,
ZIUUC_DD03L_AS4LOL)

Event-Based Rules
Start Load / Replication
BOP – Begin of Processing
BOP (Begin of Processing):
 Processed only once, before the data transfer really starts.BOP – Begin of Processing
DO
Get next portion from source system
BOT – Begin of Block
y , y
 Can be used to initialize certain global fields that might be used in subsequent
events (for example, fill internal mapping tables)
EOP (End of Processing):
 Processed only once after the data transfer is completedBOT Begin of Block
LOOP AT source
BOL – Begin of Loop
MOVE-CORRESPONDING
 Processed only once, after the data transfer is completed.
BOT (Begin of Block):
 Access to all data records of a portion read from the sender system
EOT (End of Block):
source TO target
BOR – Begin of Record
individual field mapping
 Access to all data records immediately before they are passed to the receiver
system
BOL (Begin of Loop):
 Like BOT if only one table is included in the conversion object; in case of objects
f
EOR – End of Record
EOL – End of Loop
ENDLOOP
with multiple tables, it can be applied to each specific table
EOL (End of Loop):
 Like EOT if only one table is included in the conversion object; in case of objects
with multiple tables, it can be applied to each specific table
Write to target system
EOT – End of Block
ENDDO
BOR (Begin of Record):
 This event is processed before the field mapping of the individual fields is started.
EOR (End of Record):
 This event is processed after the field mapping of the individual fields of a certain
EOP – End of processing
p pp g
data record has finished.

Parameters and Field Variables within Includes
Parameter Rules:
Sender (source) field:Sender (source) field:
Defined as import parameters in table IUUC_ASS_RUL_MAP fields IMP_PARAM_1/2/3
Addressed in the include using the following name:
IMP_PARAM_1: i_<fieldname>_1 (for example, i_mandt_1)
IMP PARAM 2: i <fieldname> 2IMP_PARAM_2: i_<fieldname>_2
IMP_PARAM_3: i_<fieldname>_3
If you have defined a literal the technical name will be:
IMP_PARAM_1: i_p1
IMP PARAM 2 i 2IMP_PARAM_2: i_p2
IMP_PARAM_3: i_p3
Receiver (target) field:
Defined in table IUUC_ASS_RUL_MAP field FIELDNAME
Add d i h i l d i h f ll i
SKIP_RECORD
can be used in the code
Addressed in the include using the following name:
e_<fieldname> (for example, e_mandt).
Event based Rules and Parameter Rules
Fields can be directly addressed using field symbols (event BOL and EOL only)
Sender (source) field of the table:
<wa_s_(tab_name)>-<field> (for example, <wa_s_mara>-mandt)
Receiver (target) field of the table:
( g )
<wa_r_(tab_name)>-<field> (for example, <wa_r_mara>-mandt)

Define the Trigger Condition
Trigger condition has to be defined in the table IUUC_SPC_PROCOPT.
Based on the database of the source system, different syntaxes are
adapted.
Field ActionField Action
MT_ID If you want to create a template for all mass transfer IDs created in future, enter
“SAP” or enter an existing mass transfer ID if you want to restrict the settings to a
specific mass transfer.
TABNAME Enter the name of the table in the sender system here.y
DBSYS Enter the database type of the source system.
LINE_NO You can specify multiple lines if the condition is too complex to be filled in only one
line.
LINE Enter the trigger condition here. Only when the data change fulfills the condition , itgg y g
will be recorded into the logging table for the SLT replication.
DBSYS Syntax Sample
ADABAS D fi ld1 ' l 0' AND fi ld2 IN ( ' l 1' ' l 2' ) AS4LOCAL 'N'ADABAS D field1 = 'value0' AND field2 IN ( 'value1', 'value2' ) AS4LOCAL = 'N'
DB2 ___."field1" = 'value0' AND ___."field2" IN ( 'value1', 'value2' ) ___."AS4LOCAL" = 'N'
DB6 ___.field1 = 'value0' AND ___.field2 IN ( 'value1', 'value2' ) ___.AS4LOCAL = 'N'
MSSQL field1 = 'value0' AND field2 IN ( 'value1', 'value2' ) AS4LOCAL = 'N'
ORACLE :___.field1 = 'value0' AND :___.field2 IN ( 'value1', 'value2' ) :___. AS4LOCAL = 'N'

In this example, we want to customize
the trigger directly in the source
system in a way that only changes to
the data with AS4LOCAL = ‘N’ will be
d drecorded.

Another example, filtering will be done
based on Company Code (BUKRS
field). Only company codes with the
value ‘1000’ will be replicated to the
HANA tHANA system
Please note: trigger filtering will only
work for replication phase. If it is
required to filter the table during the
initial load phase, an additional event
(or parameter) filter is also necessary!

Summary
• Explain the basics of the SLT based transformation concepts
• Leverage the data processing and transformation process
• Explain which tables and fields has to be specify for a
transformation ruletransformation rule

Objectives
Selective data replicationSelective data replication
• Explain the benefit of filtering
• Explain the concept of filtering within SLT
• Specify your own filtering rule

Business Example
Selective data replication / filteringSelective data replication / filtering
Reduce Number of Records to Be Replicated by Filter
DBRFC
Reduce Number of Records to Be Replicated by Filter
ConnectionConnection
SAP HANA systemSAP LT Replication ServerSource system SAP HANA systemSAP LT Replication ServerSource system
ImplementationScenarios Implementation
• Either by trigger adjustment in source system or by
rules (event-based or parameter-based)
Scenarios
 Replicating certain data only
 Only data of specific years should be used in HANA,
for instance
for instance
Replication of data active tables as they are in DDIC
tables DD02l, DD03l etc.

Overview
• The concept of filtering data for replication
D il d l f diff fil i• Detailed examples of different filtering use cases
• Examples for parameter and event-based rules

Concept - Filtering
Conditional filter (in SLT) by parameter-based or event-based rules
 To skip a record from load and replication macro SKIP_RECORD can be
used in the code of the include.
In fact, all data (means not filtered data) will be transferred from the source system to SAP LT
Replication Server but not forwarded to HANAReplication Server, but not forwarded to HANA
 means SLT system will read all data from the source and write only relevant data into HANA.
No database-specific knowledge needed
Valid for both initial load and replicationp
Selected delta replication (“Trigger Adjustments”)
Done by trigger adjustment directly of data base of the source system
 Will be implemented using table IUUC_SPC_PROCOPT.
For experts only
Database specificDatabase-specific
Will be implemented in source system
Decreases the total amount of data to be extracted from source system
Performance advantages because of reduction of triggered and transferred data
Only valid for replication data; it is not working for initial load.

Example: Filtering by Company Code Selective
data replicationdata replication
Realized with a
event-based rule
RESULT: Only records where
MJAHR(Fi lY ) 1998‘MJAHR(FiscalYear ) = ‚1998‘ were
transferred to HANA:

Example: Filtering by Fiscal Year Selective data
replicationreplication
Realized with a
t lparameter rule
RESULT: Only records where
MANDT = ‚800‘ and BUKRS = ‚3000‘
were transferred to HANA:

Example: Filter by Event-Based Rule (1) Selective data
Task: Filter and only load/replicate data with AS4LOCAL = ‘N’.

Example: Filter by Event-Based Rule (2) Selective data
For an event-based rule, “FIELDNAME” in table IUUC_ASS_RUL_MAP must be left
blank In addition you do not need to specify the import parametersblank. In addition, you do not need to specify the import parameters.

Summary
Selective data replication / filteringSelective data replication / filtering
• Explain the benefit of filtering
• Explain the concept of filtering within SLT
• Specify your own filtering rule

Objectives
Transformation of dataTransformation of data
• Describe the benefit of the transformation of fields
• Explain the concept of transforming data within SLT
• Specify your own transformation rule

Business Example
Transformation dataTransformation data
Reduce Number of Records to Be Replicated by Filterp y
DB
Connection
RFC
Connection ConnectionConnection
SAP HANA SystemLT Replication ServerSource System
Transformation
of Data
Scenarios Implementation
1. Create ABAP includes to define transformation rules
2. Maintain table IUUC_ASS_RUL_MAP
Scenarios
 To make certain fields anonymous  HR reporting
 To fill initial fields
To con ert nits or c rrenc and recalc late amo nts
3. Start data load / replication from HANA Studio To convert units or currency and recalculate amounts
and values

Overview
• The concept of transforming data for replication
D il d l f diff i h f i• Detailed examples of different use cases with transformation
needs
• Examples for parameter and event-based rulesp p

Example: Make Certain Fields Anonymous (1)
Scenario:
 Within the example, the field ‘name’ of a table should be filled by ‘xxxx’. This could be
done by parameter rules or event-based rules.
 Since each table of a sender system can only be replicated into one target table of
HANA, two identical tables in the source system were created to show the usage of both
kind of rules
Include of parameter rules: Z_Ex_1_PAR
Incl de of e ent based r les Z E 1 EVENTInclude of event-based rules Z_Ex_1_EVENT
1.
PARTNR MATNR NAME AMOUNT CURRENCY DATE
0000000001 1000Mr. ABC 100USD 20100201
0000000001 2000Mr. ABC 200USD 20110201
0000000002 1000Mr. XYZ 100USD 20110201
PARTNR MATNR NAME AMOUNT CURRENCY DATE
0000000001 1000XXXX 100USD 20100201
0000000001 2000XXXX 200USD 20110201
0000000002 1000XXXX 100USD 20110201
SAP HANA SystemSource System
0000000002 2000Mr. XYZ 200USD 20110301
0000000002 3000Mr. XYZ 300EUR 20100301
0000000002 1000XXXX 100USD 20110201
0000000002 2000XXXX 200USD 20110301
0000000002 3000XXXX 300EUR 20100301

Define Include for Event-Based Rule
Create include by T-Code SE38
Enter program name and press Create
Popup:
Enter title
Choose type INCLUDE Program
Choose status Test Program

Event-based rule:

Transformation without changing table structure
(scrambling of data)
The according Parameter rule would be:

Resulting data in HANA:
Parameter rule:
Event-based rule:

Summary
• Describe the benefit of the transformation of fields
• Explain the concept of transforming data within SLT
• Specify your own transformation rule

Objectives
Extension of (target) table structureExtension of (target) table structure
• Describe the benefit of extending target table structures
• Explain the concept of extending a table
• Specify your own extended structures

Business Example
Extension of Table Structures within Transformation
DB
Connection
RFC
Connection
SAP HANA systemSAP LT Replication ServerSource system
Scenarios
T k t i fi ld  HR ti
Implementation
D fi t bl f t t t t To make certain fields anonymous  HR reporting
 To fill initial fields or new fields of target table
 To convert units or currency and recalculate amounts
and values
1. Define tables of target structure
2. Maintain table IUUC_REPL_TABSTG
3. Create ABAP includes to define transformation rules
4 Maintain table IUUC ASS RUL MAP
4. Maintain table IUUC_ASS_RUL_MAP
5. Start data load / replication from HANA Studio

Overview
• The concept of extending target table structures
D il d l f diff i h i d• Detailed examples of different use cases with extension needs
• Examples for parameter and event-based rules

Concept
Use case:
Due to the fact that the main purpose of HANA is reporting, it could be required
that the data in HANA has to be enriched (that is, unified reporting currency)
within the transformation from sender system to HANAwithin the transformation from sender system to HANA.
Prerequisites
• Definition of table strucure to be used in HANA
 by maintaining table IUUC_REPL_TABSTG
• Definition of rules to fill up the new tables fields
 done by event-based rules or parameter rules

Concept
Steps to extend the table structure:
1. Create table of required structure either in sender system or in SLT system
acting as a template for HANA
M i t i t bl IUUC REPL TABSTG f th SAP LT R li ti S2. Maintain table IUUC_REPL_TABSTG of the SAP LT Replication Server:
MT_ID  same MT_ID like in IUUC_ASS_RUL_MAP
TABNAME  name of table in sender system to be replicated
TARGET TYPE  name of table with structure to be used as template for tableTARGET_TYPE  name of table with structure to be used as template for table
to be created in HANA
TARGET_TYPE_LCL  ‘X’: table of target type was created in LT-Replication Server
 ‚ ‚ : table of target type was created in sender system
TARGET NAME  name of table to be created and used for replication in HANATARGET_NAME  name of table to be created and used for replication in HANA
NO_DROP  data in HANA will not be deleted before the replication
ROW_STORE  target table in HANA will be created as row table instead of column table
RD_PORTION_SIZE  no of records per portion; empty means default value_ _ p p p y

Example: Calculate Field of Extended Structure (1)
Extension of (target) table structure and
calculation of values
Scenario:
Within the example the field ‘AMOUNT_IN_EUR’ of the extended table should be
calculated by a fixed exchange rate from field amount
The name of table, which reflects the target type is ZIUUC_EX_2_TRGT
Both rules can be used to calculate the new amount
Include of parameter rules: Z_Ex_2_AMOUNT_IN_EUR_PAR
Include of event-based rules Z_Ex_1_AMOUNT_IN_EUR_EVENT
PARTNR MATNR NAME AMOUNT CURRENCY DATE NAME AMOUNT CURRENCY Amount_in_EUR1.PARTNR MATNR NAME AMOUNT CURRENCY DATE
0000000001 1000Mr. ABC 100USD 20100201
0000000001 2000Mr. ABC 200USD 20110201
0000000002 1000Mr. XYZ 100USD 20110201
0000000002 2000Mr. XYZ 200USD 20110301
Mr. ABC 100USD 150
Mr. ABC 200USD 300
Mr. XYZ 100USD 150
Mr. XYZ 200USD 300
0000000002 3000Mr. XYZ 300EUR 20100301 Mr. XYZ 300EUR 300

Create table of target structure
Maintained table IUUC_REPL_TABSTG of LT Replication Server

Event-based rule:

Parameter rule

Resulting data in HANA:
Parameter rule:
Table ZIUUC_EX_2_PMOD
Event-based rule:
T bl ZIUUC EX 2 EMODTable ZIUUC_EX_2_EMOD

Summary
• Describe the benefit of extending target table structures
• Explain the concept of extending a table
• Specify your own extended structures

Transformation Rules for Cluster Tables
Specific considerationsSpecific considerations
Apply SAP note 1662438 at first.
After implementing the note, you should follow the steps
(table BSEG as an example):( p )
1. For initial load, you should create one entry in IUUC_ASS_RUL_MAP for
BSEG and include program for BSEG table
2. For replication, you should create an addition entry in IUUC_ASS_RUL_MAP
for RFBLG and include program for RFBLG table

Date Field Format - Changes
Specific considerationsSpecific considerations
Table Setting : IUUC_ASS_RUL_MAP
Include : ZIUUC_ZGRPA_THOC
Records in source table Replicated records in HANA table

A di
– Client Specific vs. Cross Client Load / Replication
Appendix
– Type mapping from source system to HANA system
– Mapping multiple sources into one target HANA DB schemapp g p g

Cli t S ifiClient Specific vs.
Cross Client Load / ReplicationCross Client Load / Replication

Objectives
Client Specific vs Cross Client Load / ReplicationClient Specific vs. Cross Client Load / Replication
• Specify client specific or cross client load or replication for
your business modelyour business model

Overview
Client Specific vs Cross Client Load / ReplicationClient Specific vs. Cross Client Load / Replication
• The client specific load / replication procedure
Th li t l d / li ti d• The cross client load / replication procedure
• The advantage of client specific load

Concept
Client Specific vs Cross Client Load / Replication
Per default, the SAP LT Replication Server loads
and replicates all data of the defined tables
Client Specific vs. Cross Client Load / Replication
and replicates all data of the defined tables.
Client specific tables are also loaded and replicated
completely and not restricted to a certain client.
Data load can be restricted by define some filter
rules (described in the following slides) or by
activating the client specific load and replication.
Advantages of client specific load
• Trigger is restricted automatically to the relevant
client
• Data load can be restricted to a smaller amount of
data without manual defining filter rules
• If the first key field after the client field is selective
enough to be used in an access plan calculationenough to be used in an access plan calculation,
reading type 1 can be used without adding an
additional index.

U it 2Unit 2:
Approaching SAP HANA ModelingApproaching SAP HANA Modeling

Objectives
 Take into account Persistency Considerations
 Explain the different engine types in thep g yp
SAP HANA Architecture
 To choose the best views for the Information Model
 To discuss some General Recommendations

Overview
 Persistency Considerations
 SAP HANA Engine Overviewg
 Choosing Views for the Information Model

Persistency Considerations I
Before start creating tables on HANA database you should take a little time toBefore start creating tables on HANA database, you should take a little time to
think about your scenarios. Different scenarios have different requirements and
for that also different persistency models. Here is a small checklist you should
take into consideration:
take into consideration:

Persistency Considerations II
1: Column Table 2: Analytical View 3: Calculation
View (SQL)
4: Calculation View
(CE Functions)View (SQL) (CE Functions)
U
Good for quick
start with HANA.
Shall be used for
i l li ti
Most recommended
for analytical
purpose, where read
ti
Good for quick
build of scenarios
with complex
l l ti Th
Recommended for
analytical purpose using
complex calculation,
hi h t bUsage simple applications
and showcases.
operations on mass
data is required.
calculations. The
model is usually
simple and
contains only a few
fields.
which can not be
expressed in an analytic
view.
Pros
No additional
modeling required.
For most clients
easy to consume.
Very high performance
on SELECT.
Supported by
modeling. Well
optimized
Building calculation
views via SQL
syntax is easy.
Client queries can be well
optimized and parallelized.
Usually better performance
results than SQL.
optimized.
No support for
analytical privileges,
multi language and
Limitations in regards
to functions.
Client queries can
be less optimized
and could
Syntax is different
compared to well-known
SQL Language
Cons
multi language and
client handling.
Complex calculation
and logic shifted to
client side. In
general low
and could
significantly be
slower compared to
other models.
SQL Language.
general low
performance.

SAP HANA Engine Overview I
SQL OptimizerSQL Optimizer
Calculation EngineCalculation Engine
OLAP
Engine
OLAP
Engine
Join
Engine
Join
Engine Row Store EngineRow Store Engine
Column StoreColumn Store Row StoreRow Store

SAP HANA Engine Overview II
Calculation
ViewsViews
OLAPOLAP J iJ i
A l ti Att ib tOLAP
Engine
OLAP
Engine
Join
Engine
Join
Engine
Analytic
Views
Attribute
Views

General Recommendations
1. Avoid transfer of large result sets
Client ApplicationClient Application A G Y Z
between the SAP HANA DB and
client (e.g. by HAVING, TOP,
LIMIT, …)
2 Do calculation after aggregation
1
2
3
A G Y
2. Do calculation after aggregation
3. Avoid complex expressions
(IF, CASES…)
4 Reduce data transfer between the
4
5
OLAP
E i
OLAP
E i
Join
E i
Join
E i
A B C D G Y
4. Reduce data transfer between the
engines
5. Do aggregate data records
(e.g. using GROUP BY, reducing
5
6 7EngineEngine EngineEnginecolumns)
6. Join on Key Columns
7. Avoid calculation before
6 7
Column
Store
Column
Store
Row
Store
Row
Store
A B C D D Gaggregation on line item level
8. Filter data amounts as early as
possible in the lower layers
(CONSTRAINTS
8
8
(CONSTRAINTS,
WHERE clause…)
8

Choosing Views for the Information Model
Analyze Data inAnalyze Data in
HANA DB
Use Star-Use Sta
schema or
Aggregation?
Use
Only Joins and
Calculated
Yes
No
NoAnalytic View Expressions?
Try Graphical
Calculation
View
Use
Attribute View
Yes
No
OK
Not enough
View
Use
CalcScenario or
Scripted
CalcView with
CE F ti
Use
Graphical
Calculation View
OK
OK
Not enough
CE-Functions
Use
CalcScenario or
using CE-Functions
Use
Scripted
Calculation View or
Procedures
Procedures

Summary
 Take into account Persistency Considerations
 Explain the different engine types in thep g yp
SAP HANA Architecture
 To choose the best views for the Information Model
 To discuss some General Recommendations

U it 3Unit 3:
Connecting TablesConnecting Tables

Objectives
• Explain differences between Inner Join, Left Outer Join,
Right Outer Join, Full Outer Join, Text Join and
referential Join when connecting tables.
• Explain how using Standard Union and Union with
constant values.constant values.

Business Example
Sales Order Customer
State
We want to connect the Sales Order
table to the Customer table linked to
the State table.the State table.

Overview
How to connect tables using
• Inner Join• Inner Join
• Left Outer Join
• Right Outer Joing
• Full Outer Join
• Text Join
• Referential Join
• Union

Inner Join – Attribute View
Connecting Tables
Inner Join returns rows when there is at least one match in both sides of the join.
Connecting Tables
Inner is used even if it’s not added.
STATE SNAME
AL ALABAMA
1 WERNER 10 MI MI MICHIGAN
2 MARK 11 MI
3 TOM 12 TX
4 BOB 13 TX
C_ID CNAME AGE STATE
Attribute View
Customer (3 & 4) is not
returned due to no
corresponding entry (TX) in the
t t t bl

state table.

Inner Join – Analytical View
Analytical ViewAnalytical View
AMOUNT ORDER_ID C_ID
100 4 4
100 8 77 STATE SNAME
100 1 1 AL ALABAMA
Be aware that Inner Joins
lose facts with fragmented

100 1 1 AL ALABAMA
100 2 1 1 WERNER 10 MI MI MICHIGAN
100 3 2 2 MARK 11 MI
3 TOM 12 TX
4 BOB 13 TX
dimensions.
Order (4 & 77) lost due to no
corresponding customer and
state record
4 BOB 13 TX
state record

Inner Join and Design Time Filters
Design time filter applied (AGE < 13)
of left/central table
Design time filter applied to (STATE = MI)
on right table
Both design time filters are applied first
before the join is executedbefore the join is executed

Left Outer Join – Attribute View
Connecting Tables
Attribute View
Connecting Tables
• Left Outer Join returns all rows from the left
table even if there are no matches in the right Attribute Viewtable even if there are no matches in the right
table.
• This join is popular in Analytical Views whereby
h A ib i i j i d h f blthe Attribute view is joined to the fact table.
STATE SNAME
AL ALABAMA
2 MARK 11 MI2 MARK 11 MI
3 TOM 12 TX
4 BOB 13 TX


No matches for TX in the right table.

Left Outer Join – Analytical View
AMOUNT ORDER_ID C_ID STATE SNAME
100 1 1 C ID CNAME AGE STATE AL ALABAMA
Customer (TOM) is
100 1 1 C_ID CNAME AGE STATE AL ALABAMA
100 3 2 2 MARK 11 MI
3 TOM 12 TX
100 4 4 4 BOB 13 TX
not returned due to no
corresponding sale item
record in sales order
table

100 4 4 4 BOB 13 TX
100 8 77
table.

Left Outer Join and Design Time Filters
Design time filter applied (AGE < 13)
of left/central table
Design time filter applied to (STATE = MI)
on right table
Filters are applied to both tables and thenFilters are applied to both tables and then
afterwards the join is executed.
Due to the left outer join TOM will be included in
the result set even though he resides in TX
g

Right Outer Join – Attribute View
• Right Outer Join returns all the rows from the
right table even if there are no matches in the Attribute Viewright table, even if there are no matches in the
left table.
Attribute View

STATE SNAME
AL ALABAMA
Alabama is included in the result set,
though there is no match in the left table.

2 MARK 11 MI
3 TOM 12 TX
4 BOB 13 TX

Right Outer Join – Analytical View
AMOUNT ORDER_ID C_ID
100 4 4
100 8 77 STATE SNAME
? ? ? ? ? ? AL ALABAMA
Right Outer Join
results in NULL measure.

? ? ? ? ? ? AL ALABAMA
100 1 1
100 3 2 2 MARK 11 MI
3 TOM 12 TX

3 TOM 12 TX
4 BOB 13 TX

Full Outer Join
• Full Outer Join is neither left nor right
it's both It includes all the rows- it's both. It includes all the rows
from both of the tables or result sets
participating in the Join.
• When no matching rows exist for
rows on the left side or right side of
the Join, you see NULL values.
• Full Outer Join is supported only in
Attribute Views and NOT within
Analytical Views.
STATE SNAME
? ? ? ? AL ALABAMA? ? ? ? AL ALABAMA
2 MARK 11 MI MI MICHIGAN
3 TOM 12 TX ? ?
4 BOB 13 TX ? ?
4 BOB 13 TX ? ?

Text Join
Connecting Tables
• Text Join are used to join a text
table to a master data table
Connecting Tables
table to a master data table.
• Text Joins acts as a Left Outer join
and can be used with SAP tables
where the language column
(SPRAS) is present.
• For each attribute it is possible toFor each attribute it is possible to
define a description mapping that
will be specific to the end users
language.

Referential Join
Relies on Referential Integrity Each entry in the left table MUST have
di t i th i ht t bla corresponding entry in the right table
Optimized for performance Join is only performed if at least one
field from the right table is requested.
Like an Inner Join when join is
executed
When field from both tables are
requested an inner Join is performed.
Only available in OLAP engine Referential join is a feature available
only in OLAP engine, when testing
Attribute Views outside the context of a
Analytical view then the Join EngineAnalytical view then the Join Engine
will perform a Inner Join.

Referential Join – Attribute View
*** Referential join is a feature available only in OLAP engine, when testing Attribute Views
outside the context of a Analytical view then the Join Engine will perform a Inner Join. As a
result TOM and BOM will not be returned.

Referential Join – Analytical View
*** Customer 77, TOM and BOB are not returned since C_ID is a Joined key field
resulting in an Inner Join. BOB has no corresponding Texas description and TOM has no
corresponding facts.
*** TOM is not returned due to no
corresponding facts in the sales tablecorresponding facts in the sales table
*** The Amount includes all facts including Customer 77 and BOBs
order even through master records do not exist reason is when
only non-key fields are selected from the left table, all joins to other
tables will be omitted
tables will be omitted

Referential Join – Using MDX
Like Inner Join
No referential integrity
of data in both tables
No Join processed
p

Calculation View- Join vs. Union
Caution!! Do not JOIN Analytical Views, this could lead to performance
i li ti I t d U i ith t t l h ki ithimplications. Instead use Union with constant values when working with
multiple fact tables

Unions
• Unions are used to combine the result-set of two or more SELECT statements.
• The Union operation is popular for combining plan and actual values in CO-PA.
• Note that Unions are not supported in modeled artifacts (Attribute Views or Analytical Views)
d l b li d i C l l ti Viand can only be realized in Calculation Views.
• Refrain from Joining Analytical views; rather use Unions with Constant values.
• Unions with Constant values are supported within Graphical Calculation Views and the
UNION operator can accept 1..N input sources.
• Whereas a Script Based calculation view’s comparable CE UNION ALL function can onlyWhereas a Script Based calculation view s comparable CE_UNION_ALL function can only
accept 2 input sources at a given time.

Standard Union

Union with Constant Values

Summary
• Explain differences between Inner Join, Left Outer Join,
Right Outer Join, Full Outer Join, Text Join and referential
Join when connecting tables.
• Explain how using Standard Union and Union with
constant valuesconstant values.

Creating Attribute Views
Unit 4: Advanced Modeling
Creating Restricted & Calculated Measures
Using Hierarchies
Using Filter Operations
SAP HANA SQL - Introduction
Using Filter Operations
Using Variables
SQLScript and Procedures
Using Currency Conversion

Objectives
Creating Attribute ViewsCreating Attribute Views
 Explain how to create derived attribute views,
 Explain how to create shared attribute views,p ,
 Explain how to create calculated attributes,
 Explain how to create time characteristics based attribute views,
 Explain how to create stand alone text tables.Explain how to create stand alone text tables.

Overview
 Derived Attribute Views,
 Shared Attribute Views
 Calculated Attributes
 Time Characteristics Based Attribute Views
 Stand Alone Text TablesStand Alone Text Tables

Concept Attribute views
Calculation
Views
OLAPOLAP JoinJoin
Analytic Attribute
EngineEngine EngineEngine
a y c
Views
Attribute
Views

Derived Attribute Views
© 2012 SAP AG. All rights reserved. 128© 2012 SAP AG. All rights reserved. 128

Shared Attribute Views

Calculated Attributes

Time Characteristics Attribute View

Creating Stand Alone Text Tables

Summary
 Explain how to create derived attribute views,
 Explain how to create shared attribute views,p ,
 Explain how to create calculated attributes,
 Explain how to create time characteristics based attribute views,
 Explain how to create stand alone text tables.Explain how to create stand alone text tables.

Objectives
Using HierarchiesUsing Hierarchies
 Explain how to implement leveled hierarchies,
 Explain how to leverage parent / child hierarchies,Explain how to leverage parent / child hierarchies,
 Explain how to create attribute based hierarchies.

Overview
 Implement Leveled Hierarchies
 Leverage Parent Child HierarchiesLeverage Parent Child Hierarchies
 Create Attribute Based Hierarchies

Concept Using Hierarchies
For example, consider the TIME attribute view with YEAR, QUARTER, and
Hierarchies in HANA could only
p , , ,
MONTH attributes. You can use these YEAR, QUARTER, and MONTH attributes
to define a hierarchy for the TIME attribute view as follows:
be used by reporting tools using
MDX connectivity

Using Hierarchies
Let's have a look at the following hierarchy, a minimal example :
g y p
be used by reporting tools using
MDX connectivity

Implement Leveled Hierarchies
Level Hierarchies are hierarchies that are rigid in nature, where the root and theg ,
child nodes can be accessed only in the defined order. For example,
organizational structures, and so on.
This page describes step-by-step
how to create a SAP HANA
Database hierarchy view byDatabase hierarchy view by
means of the SAP Hana Modeler.
Create a table providing somep g
hierarchy source data :

If necessary, create ay,
package in the Modeler.
Create an attribute view
in the Modeler :

Select the source table
as basis for the view :

Select the attributes
that should be part of
the source view :

Switch to the
Hierarchies tab, press
the Create button and
define hierarchy name
and structure type,
currently leveled :

Add the source
attributes to the
hierarchy. For a leveled
hierarchy, add the
attributes to the
hierarchy in the correct
level order from top to
bottom :

Validate the hierarchy,y,
close, and save. And
then activate the
attribute view :

Leverage Parent Child Hierarchies
Value hierarchies are hierarchies
that are very similar to BOM
(parent and child) and Employee
Master(Employee and Manager).
The hierarchy can be explored
based on a selected parent, and
there are cases where the child
can be a parent. This hierarchy is
derived based on the value.
If you want to create a parent-
child hierarchy, set the
child/successor attribute's
property "Principal Key" to True :

Switch to the
Hierarchies tab, press
the Create button and
define hierarchy name
and structure type,
currently parent-child :

For a parent-childp
hierarchy, select the
child/successor
attribute as Key
Attribute and the
parent/predecessor
attribute as Parent
attribute :
Then, validate, close,
d ti t thsave and activate the
attribute view.

Create Attribute Based Hierarchies

Summary
 Explain how to implement leveled hierarchies,
 Explain how to leverage parent / child hierarchies,Explain how to leverage parent / child hierarchies,
 Explain how to create attribute based hierarchies.

Objectives
Creating Restricted & Calculated MeasuresCreating Restricted & Calculated Measures
 Understand the benefits of Restricted Measures
 Use Restricted Measures
 Understand when to use Calculated Measures
 Create Calculated Measures

Overview
Using Filter OperationsUsing Filter Operations
 Concepts for Restricted & Calculated Measures
 Using Restricted MeasuresUsing Restricted Measures
 Creating Calculated Measures

The benefits of Restricted Measures
What is a Restricted Measure?
C t M th A t
• As the name implies it is a
measure that does not give the
complete picture of a measure, it
Country Month Amount
DE 2010‐01 12.345,00
DE 2010‐02 15.678,00
DE 2010‐03 25.814,00
DE 2010‐04 21.586,00
DE 2010‐05 21 861 00
Restricted by Country:
p p ,
is restricted to a subset of the
original measure.
• The benefit of a Restricted
DE 2010 05 21.861,00
DE 2010‐06 11.258,00
DE 2010‐07 12.387,00
DE 2010‐08 13.589,00
DE 2010‐09 12.345,00
DE 2010‐10 15.678,00
DE US Difference
209.941,00    205.838,00    4.103,00
• The benefit of a Restricted
Measure is, that it expands the
modeling options in a view, giving
the modeler the possibilities of
DE 2010‐11 25.814,00
DE 2010‐12 21.586,00
US 2010‐01 21.861,00
US 2010‐02 11.258,00
US 2010‐03 12.387,00
Country Q1 Q2
DE 53.837,00    54.705,00
US 45.506,00 41.612,00
Restricted by Months:
creating objects that can be easily
reported on or reused.
US 2010‐04 13.589,00
US 2010‐05 12.345,00
US 2010‐06 15.678,00
US 2010‐07 25.814,00
US 2010‐08 21.586,00
US 2010 09 21 861 00
US 45.506,00    41.612,00
US 2010‐09 21.861,00
US 2010‐10 11.258,00
US 2010‐11 12.387,00
US 2010‐12 25.814,00

Using Restricted Measures
Picture the example in the right table.
Cost Type Amountp g
You have a transactional table with
cost data items, with each cost type
split on a different line.
Cost Type Amount
Purchasing Price € 1 200
If you want to find out the shipping
cost you could create an Analytic View
Shipping Cost € 80
VAT € 346
with Cost Type as an Attribute, and
Amount as a Measure.
Y ld th t i t t b
Processing Cost € 150
You could then restrict your report by
reporting on Cost Type, setting the
Attribute filtered on Cost Type =
“Shipping Cost”
Margin € 300
“Shipping Cost”.

In order to utilize a Restricted Measure, you can instead create one which
, y
already limits the results to Shipping Costs only within the measure itself.

The Attribute you filter the Restricted
y
Measure does not have be limited to
one single Attribute.
You can set it restricted to multiple
attributes depending on your reporting
requirements.
There are also multiple operators to
choose from.

Further, the Restricted Measure does not
have to be a straight sum of the Measure
that it is based on.
The Aggregation Types that you have
available are:
 SUM SUM
 MIN
 MAX

When to use Calculated Measures
 In a data model sometimes not all
Measures available in the base data will
give your users sufficient information for
reporting if you just provide the basereporting if you just provide the base
Measures in your views.
 SAP HANA has a type of Measure
available called Calculation Measures
where the modeler is able to include
calculations already within the view in
order to help reporting or furtherorder to help reporting or further
modeling.

When to use Calculated Measures
Cli t A li tiCli t A li ti
 When you include calculations in
Client ApplicationClient Application A G Y Z
your views using Calculated
Measures you take advantage of
the speed of SAP HANA letting the
database engine perform the
Do calculation
after aggregation
database engine perform the
calculations, instead of doing
these calculations in your end
client reporting tool.
OLAP
Engine
OLAP
Engine
Join
Engine
Join
Engine
A B C D G Y
 Having ready defined calculations
in views can also help simplifying
reporting by unifying calculations EngineEngine EngineEnginep g y y g
having them calculated in the
same way for all users instead of
having users or developers create
their own versions of the Avoid calculation
Column
Store
Column
Store
Row
Store
Row
Store
their own versions of the
calculations.
Avoid calculation
before aggregation
on line item level

Creating Calculated Measures
A Calculated Measure is defined in the view and when you create one you can
use the calculations, mathematical functions etc. available in the editor.
,

Product Units Price
Total Sales
(Units * Price)
Keyboards 100 € 40 € 4 000Keyboards 100 € 40 € 4 000
LCD Screens 50 € 300 € 15 000
Network Switches 75 € 90 € 6 750
VOIP Telephones 200 € 65 € 13 000
Servers 30 € 2 000 € 60 000
SUM: 455 € 2 495 € 1 135 225
For certain measure it is not possible to perform the calculations when the
measures are already aggregated. The aggregated granularity of for example
Price does not mean anything
Price does not mean anything.

For these types of
Measures you can
predefine the
Calculated Measure
to calculate each
individual item before
aggregating.
This is done by
l ti th tiselecting the option
“Calculate Before
Aggregation”.

This way you can be sure
Product Units Price
Total Sales
(Units * Price)
This way you can be sure
that you end up with a
correct sum as the
calculation are performed
Keyboards 100 € 40 € 4 000
LCD S 50 € 300 € 15 000 calculation are performed
on the correct granular
level.
LCD Screens 50 € 300 € 15 000
Network Switches 75 € 90 € 6 750
VOIP Telephones 200 € 65 € 13 000
Servers 30 € 2 000 € 60 000
SUM: € 98 750

Objectives
Using currency ConversionUsing currency Conversion
 Understand the benefits of Restricted Measures
 Use Restricted Measures
 Understand when to use Calculated Measures
 Create Calculated Measures

Objectives
 Explain how to compare constraint filter and WHERE clause,
 Explain how to create client dependant views,p p ,
 Explain how to model domain fix values.

Overview
 Compare constraint filter and WHERE clause,
 Create client dependant views,p ,
 Model domain fix values.

Filter Operations
Client ApplicationClient Application
Reduce data transfer between
the engines by using Filter
O ti lik
OLAP
E i
OLAP
E i
Join
E i
Join
E i
Operations like :
 Using a Constraint or WHERE
clause
C ti Cli t D d t ViEngineEngine EngineEngine  Creating Client Dependant Views
 Using Model Domain Fix Values
Column
Store
Column
Store
Row
Store
Row
Store

Compare Constraint Filter & WHERE Clause
Constraint filter :
• Is defined on design time on a
tabletable,
• The filter applies on the table
before the query starts to
WHERE clause :
• Is defined on runtime in the
execute,
• Normally faster than WHERE
clause as the results set is
• Is defined on runtime in the
SQL query,
• The filters applies on the
f
Vs
clause, as the results set is
reduced before proceeding
with the query execution plan,
e.g. constraints applied before
results set of a query.
e.g. constraints applied before
a table join is executed.

Create Client Dependant Views
Define « Default Client » as
« dynamic » in properties of your
views (Attribute Views and Analytic
Views)
Define the value of the « Session
Client » in the definition of the UserClient » in the definition of the User
ID. The variable constraint is
substituted at runtime by the client
that is set for the current user
© 2012 SAP AG. All rights reserved. 172© 2012 SAP AG. All rights reserved.
running a query on the model:
172

Model Fix Values

Summary
 Explain how to compare constraint filter and WHERE clause,
 Explain how to create client dependant views,p p ,
 Explain how to model domain fix values.

Objectives
Creating VariablesCreating Variables
• Explain SAP HANA Variables
• Create Variables• Create Variables

Overview
• Creating Attribute Value Variables
• Creating Static Value Lists• Creating Static Value Lists
• Creating Variables with Formulas
• Creating Date Variables

SAP HANA Variables Concepts
 You use variables to filter data atClient ApplicationClient Application You use variables to filter data at
runtime. You assign values to
these variables by entering the
value manually, or by selecting it
f th d d li t
Client ApplicationClient Application
from the drop-down list.
 Using variables means that you
do not need to decide the
restriction on the value of
attributes at the design time.
 You can apply variables in the
OLAP
Engine
OLAP
Engine
Join
Engine
Join
Engine You can apply variables in the
analytic and calculation views.
 If a calculation view is created
i l ti i ith
EngineEngine EngineEngine
using an analytic view with
variables, those variables are
also available in the calculation
view but cannot be edited.
Column
Store
Column
Store
Row
Store
Row
Store

Variables Types
The following types of variables are supported:
Type Description
AttributeValue Use this to apply a filter when the value of the filter comes from
an attribute.
Currency Use this during currency conversion to specify the targetCurrency Use this during currency conversion to specify the target
currency.
Date Use this to apply a filter when the value of the filter is in date
format, for example, to specify the date during currency, p , p y g y
conversion.
Static List Use this to a apply filter when the value of the filter comes from a
user-defined list of values.

Creating Attribute Value Variables
The Variable type called ”AttributeValue”The Variable type called AttributeValue
restricts the results in the view for the
selected Attribute.
You select the Attribute in the view that
you want to filter on, and you also
define the:
Selection Type: Whether selections
should be based on intervals, ranges or
single valuessingle values.
Multiple Entries: Whether mutliple
values of the selection types should beyp
allowed.

When displaying the values of a View with an Attribute Value Variable includedWhen displaying the values of a View with an Attribute Value Variable included,
you will be asked to enter the values for the Attribute Values defined. The Value
Help Dialog you can help you find the selections you are looking for.

The Variable Values selectedThe Variable Values selected
will restrict the results as
required when you click the
”OK” button.OK button.
Should the user however need
to display all values, bypassing
the selection criteria completely,
it is possible to do so by
selecting the “Cancel” button in
th “V i bl V l ” di lthe “Variable Values” dialog.

Creating Static Value Lists
When you want to limit theWhen you want to limit the
possible values to select for a
varaible it can be useful to
define the Variable as adefine the Variable as a
”StaticList” type.
When defining this type of
variable you can enter the list
of values at design-time.

Creating Static Value Lists
During refresh of a ”StaticList” type of Variable when the user clicks on ”Find” theDuring refresh of a StaticList type of Variable, when the user clicks on Find , the
Value Help Dialog will display the list of values defined during variable creation.

Creating Variables with formulas
A variable does not necessarily need toA variable does not necessarily need to
restrict the data of a view.
It can also take input from the user and
process it, returning dynamic data
based on the user selection.
We now want to see how we can createWe now want to see how we can create
a function in a Calculated Measure
based on the value of a Variable.

What we first need to doWhat we first need to do
is to create a variable to
be used in a calculation.
The variable can be of
any type, for example a
StaticList type.
In this example to the
right the user can
choose either ”Gross” orchoose either Gross or
”Net” as the selection.

We also need somewhere toWe also need somewhere to
call the variable from.
In for example a Calculated
Measure, we can reference
the result of the user selected
variable.
This is done by calling it
within double dollar signs,
see example:see example:
if('$$VAR_GROSS_OR_NET$$'='Gross',"GROSS_AMOUNT","NET_AMOUNT")

Creating Date Variables
The Variable type”Date” can be usefulThe Variable type Date can be useful
when you for example want an input
date from the reporting user in order to
create further calculations on.create further calculations on.
Whatever input is selected in the
variable can be used as a basis for
extended calculations.

Selecting the Variable Type as: ”Date” will make it easier for the endSelecting the Variable Type as: Date will make it easier for the end
user to select a date by utilizing a calendar dialog for selecting the
appropriate date.

Calculated AttributeVariable
The date variable selected can then beThe date variable selected can then be
used in for example a Calculated
Attribute so as to give dynamic values.

Objectives
• Explain SAP HANA Variables
• Create Variables• Create Variables

Objectives
SAP HANA SQLSAP HANA SQL
 Explain the language elements used in SAP
HANA SQL statements.Q

Overview
• Overview SQL Language Elements
• Identifiers• Identifiers
• SQL Data Types
• Predicates and Operators
• Functions and Expressions
• SQL Statements - Examples

SQL - Definition
• Standardized Class Description Example
language for
communication with a
relational database.
p p
DDL
Data Definition
Language
CREATE, ALTER,
DROP TABLE
• Used to retrieve,
store or manipulate
information in the
DML
Data Manipulation
Language
SELECT, DELETE,
INSERT, UPDATE
information in the
database.
DCL
Data Control
Language
GRANT, REVOKE

SQL language elements
Identifiers • Used to represent names used in SQL statementIdentifiers • Used to represent names used in SQL statement
Data types • Specify the characteristics of a data value
Expressions • Clause that can be evaluated to return values
Operators • Used for calculation, value comparison or to assign values
Predicates
• Specified by combining one or more expressions or logical
operators and returns one of the following logical or truth
values: TRUE, FALSE, or UNKNOWNvalues: TRUE, FALSE, or UNKNOWN
Functions • Used in expressions to return information from the database

Comment and Code page
C tComments
 --
double hyphensdouble hyphens
 /*/
<……>
*/
Codepage
 The SAP HANA database supports Unicode to allow use of all The SAP HANA database supports Unicode to allow use of all
languages in the Unicode Standard and 7 Bit ASCII code page without
restriction.

Identifiers
TRAINING
Undelimited
Identifiers
• must start with a letter
• cannot contain any symbols other
than digits or an underscore "
TRAINING
Training
(treated with upper case!)
1TRAININGg _
TRAINING%
• enclosed in the delimiter
d bl t
"TRAINING"
" i i (
Delimited
Identifiers
double quotes
"<identifier>"
• can contain any character including
special characters.
"Training" (case
sensitive)
"1Training"
"Training%"p g
• "_SYS_" is reserved exclusively
for database engine
Limitations
for database engine
• Role name and user name must be
specified as undelimited identifiers.
• Maximum length for the identifiers
i 127 h t
is 127 characters.
199

SQL Data types
Classification Data Type
Datetime types DATE, TIME, SECONDTIME, TIMESTAMP
Numeric types TINYINT, SMALLINT, INTEGER, BIGINT,
SMALLDECIMAL, DECIMAL, REAL, DOUBLE, FLOAT
Character string types VARCHAR, NVARCHAR, ALPHANUM, SHORTTEXT
Binary types VARBINARY
Large Object types BLOB, CLOB, NCLOB, TEXT

Predicates
Comparison Predicates <expression>p
{ = | != | <> | > | < | >= | <= }
[ ANY | SOME| ALL ]
{ <expression_list> | <subquery> }
Range Predicate <expression1> [NOT] BETWEEN <expression2>
AND <expression3>
In Predicate <expression> [NOT] IN { <expression_list>
| <subquery> }
E i t P di t [NT] EXISTS ( < b > )Exists Predicate [NT] EXISTS ( <subquery> )
LIKE Predicate <expression1> [NOT] LIKE <expression2>
[ESCAPE <expression3>][ p ]
NULL Predicate <expression> IS [NOT] NULL

TZH300_EN_COL96

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