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Christoph Morgen / SAP HANA Product Management, SAP SE
Srinivas Rapthadu / SAP HANA Competence Center, SAP Labs
Lucas Kiesow / HANA Services CoE, SAP Deutschland SE & Co. KG
DMM161 – Introduction to Data Modeling in
SAP HANA
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 2Public
Disclaimer
This presentation outlines our general product direction and should not be relied on in making a
purchase decision. This presentation is not subject to your license agreement or any other agreement
with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to
develop or release any functionality mentioned in this presentation. This presentation and SAP's
strategy and possible future developments are subject to change and may be changed by SAP at any
time for any reason without notice. This document is provided without a warranty of any kind, either
express or implied, including but not limited to, the implied warranties of merchantability, fitness for a
particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this
document, except if such damages were caused by SAP intentionally or grossly negligent.
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 3Public
Agenda
SAP HANA Modeling Overview
 SAP HANA Information Models
Hands-On Exercises Overview
 Workshop Scenario
 Section 1 – Designing Basic Attribute- and Analytic-Views
 Section 2 & 3 – Enhancing Attribute- and Analytic-Views
 Section 4 (Optional) – Advanced Multi-dimensional Model Scenarios
SAP HANA Modeling Overview
SAP HANA Information Models
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 5Public
SAP HANA Modeling Views - Overview
Data and Processing in SAP HANA
 In-Memory Data Stores: Column- and Row Store
 Optimized for Query and OLTP Workload
 SQL & OLAP Processing and specific Calculation Operators
 Application Function Library for specific Scenarios, e.g. Predictive Analytics
Modeling in SAP HANA
 Information Models (SAP HANA Views) are optimized for SAP HANA Engines and Calculation Operators
 Data/Columns are classified as Attributes or Measures in SAP HANA Views
 Attributes – descriptive data (known as Characteristics SAP BW terminology)
 Measures – data that can be quantified and calculated (known as key figures in SAP BW)
 No materialized aggregates
 Three levels of modeling: Attribute View > Analytic View > Calculation View
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 6Public
SAP HANA Modeling Views - Overview
Attribute Views
• Compose a dimensional view
with a series of attributes derived
from a collection of tables
e.g. Master Data Views
 Highly re-used and shared in
Analytic- and Calculation Views
 Used to build Hierarchies
 Hierarchies are key elements in
use with Analytic View for multi-
dimensional reporting
Analytic Views
• Combines Fact-Tables with
Attribute-Views to Star-Schema-
or OLAP Cube-like objects for
multidimensional reporting.
• Stores no aggregates and mass-
aggregates on the fly
• Hierarchies are key for multi-
dimensional access (navigation,
filtering, slicing and aggregation)
Calculation View
• Great flexibility for advanced use
• Approach to model custom
scenarios like
 Combined use of Multiple-Fact
Tables/Analytics Views
 Build Models on Normalized Data
 Re-Use and stack views
 Make use of custom scripted views
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 7Public
SAP HANA Modeling Views - Multidimensional Model Scenario
Analytical View Attribute View Column Table
Calculation View
Reporting Tools can usually
directly consume HANA
Calculation Views or
Analytic Views.
Multidimensional Tools
support Hierarchies for
Navigation, Filtering and
Aggregation and HANA
Prompts (Variables & Input
Parameters) for efficient
Pre-Filtering of Data.
Calculation Views
are usually build upon
Analytic-, Attribute-
Views, and Column
Tables
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 8Public
SAP HANA Modeling Views - Normalized Data Model Scenario
SAP HANA Calculation
Views provide the
means to model
sophisticate views based
on normalized data
structures.
See SAP Note 1857202
Complex Calculation
Views demand a
more explicit intent
and control of the
modeled set-based
data flow, i.e. slicing,
aggregation and
filtering of sets as
input to joins, unions
etc.
SAP HANA Calculation Views
typically feed data to Business
Applications, like SAP HANA
XS build Applications
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 9Public
The SAP HANA Studio Model Editor
The Graphical Model Editor
 Standardized graphical editing across
different SAP HANA view types
 Build of different common panels
– Scenario provides Overview
– Semantic node provides better summary
of output structure of the model
+ editor view of output objects
+ general view properties
– Logical-Join- and Data Foundation-Nodes
are specific to Attribute- and Analytic Views
– Calculation-Views are modeled in the scenario panel
based on a palette of node-objects like join, union, etc.
to compose a custom data flow.
Model Scenario General View Properties, Semantic Information
Logical Join of Data Foundation & Dimensions
Data Foundation
Hands-On Exercises Overview
Workshop Scenario
Section 1 – Designing Basic Attribute- and Analytic-Views
Section 2 and 3 – Enhancing Analytic-Views
Section 4 (Optional) – Advanced Scenarios
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 11Public
Exercises Scenario
The Enterprise Procurement Data Model
Primary Entities:
 Sales Orders
 Deliveries
Supporting Entities:
 Products
 Address
 Business Partner
Solution Content:
Exercise work area location:System Information: Exercise Data:
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 12Public
Exercises Scenario
The Enterprise Procurement Data Model
Two Primary Entities:
• Sales Orders
• Purchase Orders
Supporting Entities:
• Employees
• Partners (Customers, Suppliers)
• Addresses
• Texts
• Products
Infrastructure Entities
• Currency Rates
• Unit of Measures
See Apendix
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 14Public
Exercises Section 1
Designing Basic Attribute- and Analytic-Views
1.1 Build a Customer
Attribute View
1.2 Build an Analytic View
with SalesOrders and
Customer Attribute View
1.3 Explore the
Analytic View with
SAP Lumira
1.4 Enhance with
Calculated Attributes
Analytic View
Attribute View
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 15Public
Exercises Section 2
Enhancing Analytic-Views
2.1 Build a Product
Attribute View
(incl. Text Join, Hierarchies)
2.3 HANA Variables
for Prompted Filtering
2.2 Build a Analytic View with
SalesOrders, Products, Time
and Customer Information
(+join Order and OrderItem Data)
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 16Public
Exercises Section 3
Enhancing Analytic-Views
3.1 Using prompted
HANA parameters for
dynamic calculations
3.2 Using HANA Unit
Conversion (Optional)
3.3 Using HANA
Currency Conversion
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 17Public
Exercises Section 4 (Optional Section)
Advanced Multi-dimensional Model Scenarios
4.1 Using Temporal
Joins which changing
attribute data
4.2 Combining Actuals-
and Plan-Measures in
Calculation Views
Hands-On Time.
Now it’s your time! Good Luck!
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 19Public
SAP HANA General Modeling Principles
Column
Store
Analytical
Views
Attribute
Views
Calculation Views
Client / Application
A B C D
A B C D G Y
A G Y
A G Y Z
Filter data amount as early as
possible in the lower layers
(CONSTRAINTS, WHERE Clause,
Analytical Privileges..)
Aggregate data records (e.g using
GROUP BY, reducing Coulmns)
Avoid transfer data of large resultsets between the HANA DB
and client application
- Do calculation after aggregation.
- Avoid Complex expressions (IF, CASE, ... )
Join on Key Columns or Indexed Columns
Avoid calculations before
aggregation on line item level
Reduce data transfer between views
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 20Public
Further Information
SAP Education and Certification Opportunities
www.sap.com/education
Watch SAP TechEd Online
www.sapteched.com/online
SAP Public Web
scn.sap.com http://scn.sap.com/community/developer-center/hana
www.sap.com www.saphana.com
Related Workshops/Lectures at SAP TechEd 2014
DMM270 – Advanced Data Modeling in SAP HANA, Hands-On Workshop
DMM103 – New and Best Practices for Data Modeling with SAP HANA, Lecture
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 21Public
SAP d-code Virtual Hands-on Workshops and SAP d-code Online
Continue your SAP d-code education after the event!
SAP d-code Online
 Access replays of keynotes, Demo Jam, SAP d-code
live interviews, select lecture sessions, and more!
 Hands-on replays
http://sapdcode.com/online
SAP d-code Virtual Hands-on Workshops
 Access hands-on workshops post-event
 Starting January 2015
 Complementary with your SAP d-code registration
http://sapdcodehandson.sap.com
© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Thank you!
Contact information:
Christoph Morgen
SAP HANA Product Management
SAP SE | Dietmar-Hopp-Allee 16 | 69190 Walldorf | Germany
christoph.morgen@sap.com | www.sap.com
23© 2014 SAP SE or an SAP affiliate company. All rights reserved.
Feedback
Please complete your session evaluation for
DMM161.
Thanks for attending this d-code session.
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 23Public
© 2014 SAP SE or an SAP affiliate company. All rights reserved. 24Public
© 2014 SAP SE or an SAP affiliate company. All rights reserved.
No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an
SAP affiliate company.
SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE
(or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark
information and notices.
Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors.
National product specifications may vary.
These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its
affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or
SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing
herein should be construed as constituting an additional warranty.
In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or
release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future
developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for
any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-
looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place
undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

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DMM161 HANA_MODELING_2015

  • 1. Public Christoph Morgen / SAP HANA Product Management, SAP SE Srinivas Rapthadu / SAP HANA Competence Center, SAP Labs Lucas Kiesow / HANA Services CoE, SAP Deutschland SE & Co. KG DMM161 – Introduction to Data Modeling in SAP HANA
  • 2. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 2Public Disclaimer This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.
  • 3. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 3Public Agenda SAP HANA Modeling Overview  SAP HANA Information Models Hands-On Exercises Overview  Workshop Scenario  Section 1 – Designing Basic Attribute- and Analytic-Views  Section 2 & 3 – Enhancing Attribute- and Analytic-Views  Section 4 (Optional) – Advanced Multi-dimensional Model Scenarios
  • 4. SAP HANA Modeling Overview SAP HANA Information Models
  • 5. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 5Public SAP HANA Modeling Views - Overview Data and Processing in SAP HANA  In-Memory Data Stores: Column- and Row Store  Optimized for Query and OLTP Workload  SQL & OLAP Processing and specific Calculation Operators  Application Function Library for specific Scenarios, e.g. Predictive Analytics Modeling in SAP HANA  Information Models (SAP HANA Views) are optimized for SAP HANA Engines and Calculation Operators  Data/Columns are classified as Attributes or Measures in SAP HANA Views  Attributes – descriptive data (known as Characteristics SAP BW terminology)  Measures – data that can be quantified and calculated (known as key figures in SAP BW)  No materialized aggregates  Three levels of modeling: Attribute View > Analytic View > Calculation View
  • 6. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 6Public SAP HANA Modeling Views - Overview Attribute Views • Compose a dimensional view with a series of attributes derived from a collection of tables e.g. Master Data Views  Highly re-used and shared in Analytic- and Calculation Views  Used to build Hierarchies  Hierarchies are key elements in use with Analytic View for multi- dimensional reporting Analytic Views • Combines Fact-Tables with Attribute-Views to Star-Schema- or OLAP Cube-like objects for multidimensional reporting. • Stores no aggregates and mass- aggregates on the fly • Hierarchies are key for multi- dimensional access (navigation, filtering, slicing and aggregation) Calculation View • Great flexibility for advanced use • Approach to model custom scenarios like  Combined use of Multiple-Fact Tables/Analytics Views  Build Models on Normalized Data  Re-Use and stack views  Make use of custom scripted views
  • 7. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 7Public SAP HANA Modeling Views - Multidimensional Model Scenario Analytical View Attribute View Column Table Calculation View Reporting Tools can usually directly consume HANA Calculation Views or Analytic Views. Multidimensional Tools support Hierarchies for Navigation, Filtering and Aggregation and HANA Prompts (Variables & Input Parameters) for efficient Pre-Filtering of Data. Calculation Views are usually build upon Analytic-, Attribute- Views, and Column Tables
  • 8. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 8Public SAP HANA Modeling Views - Normalized Data Model Scenario SAP HANA Calculation Views provide the means to model sophisticate views based on normalized data structures. See SAP Note 1857202 Complex Calculation Views demand a more explicit intent and control of the modeled set-based data flow, i.e. slicing, aggregation and filtering of sets as input to joins, unions etc. SAP HANA Calculation Views typically feed data to Business Applications, like SAP HANA XS build Applications
  • 9. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 9Public The SAP HANA Studio Model Editor The Graphical Model Editor  Standardized graphical editing across different SAP HANA view types  Build of different common panels – Scenario provides Overview – Semantic node provides better summary of output structure of the model + editor view of output objects + general view properties – Logical-Join- and Data Foundation-Nodes are specific to Attribute- and Analytic Views – Calculation-Views are modeled in the scenario panel based on a palette of node-objects like join, union, etc. to compose a custom data flow. Model Scenario General View Properties, Semantic Information Logical Join of Data Foundation & Dimensions Data Foundation
  • 10. Hands-On Exercises Overview Workshop Scenario Section 1 – Designing Basic Attribute- and Analytic-Views Section 2 and 3 – Enhancing Analytic-Views Section 4 (Optional) – Advanced Scenarios
  • 11. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 11Public Exercises Scenario The Enterprise Procurement Data Model Primary Entities:  Sales Orders  Deliveries Supporting Entities:  Products  Address  Business Partner Solution Content: Exercise work area location:System Information: Exercise Data:
  • 12. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 12Public Exercises Scenario The Enterprise Procurement Data Model Two Primary Entities: • Sales Orders • Purchase Orders Supporting Entities: • Employees • Partners (Customers, Suppliers) • Addresses • Texts • Products Infrastructure Entities • Currency Rates • Unit of Measures See Apendix
  • 13. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 14Public Exercises Section 1 Designing Basic Attribute- and Analytic-Views 1.1 Build a Customer Attribute View 1.2 Build an Analytic View with SalesOrders and Customer Attribute View 1.3 Explore the Analytic View with SAP Lumira 1.4 Enhance with Calculated Attributes Analytic View Attribute View
  • 14. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 15Public Exercises Section 2 Enhancing Analytic-Views 2.1 Build a Product Attribute View (incl. Text Join, Hierarchies) 2.3 HANA Variables for Prompted Filtering 2.2 Build a Analytic View with SalesOrders, Products, Time and Customer Information (+join Order and OrderItem Data)
  • 15. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 16Public Exercises Section 3 Enhancing Analytic-Views 3.1 Using prompted HANA parameters for dynamic calculations 3.2 Using HANA Unit Conversion (Optional) 3.3 Using HANA Currency Conversion
  • 16. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 17Public Exercises Section 4 (Optional Section) Advanced Multi-dimensional Model Scenarios 4.1 Using Temporal Joins which changing attribute data 4.2 Combining Actuals- and Plan-Measures in Calculation Views
  • 17. Hands-On Time. Now it’s your time! Good Luck!
  • 18. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 19Public SAP HANA General Modeling Principles Column Store Analytical Views Attribute Views Calculation Views Client / Application A B C D A B C D G Y A G Y A G Y Z Filter data amount as early as possible in the lower layers (CONSTRAINTS, WHERE Clause, Analytical Privileges..) Aggregate data records (e.g using GROUP BY, reducing Coulmns) Avoid transfer data of large resultsets between the HANA DB and client application - Do calculation after aggregation. - Avoid Complex expressions (IF, CASE, ... ) Join on Key Columns or Indexed Columns Avoid calculations before aggregation on line item level Reduce data transfer between views
  • 19. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 20Public Further Information SAP Education and Certification Opportunities www.sap.com/education Watch SAP TechEd Online www.sapteched.com/online SAP Public Web scn.sap.com http://scn.sap.com/community/developer-center/hana www.sap.com www.saphana.com Related Workshops/Lectures at SAP TechEd 2014 DMM270 – Advanced Data Modeling in SAP HANA, Hands-On Workshop DMM103 – New and Best Practices for Data Modeling with SAP HANA, Lecture
  • 20. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 21Public SAP d-code Virtual Hands-on Workshops and SAP d-code Online Continue your SAP d-code education after the event! SAP d-code Online  Access replays of keynotes, Demo Jam, SAP d-code live interviews, select lecture sessions, and more!  Hands-on replays http://sapdcode.com/online SAP d-code Virtual Hands-on Workshops  Access hands-on workshops post-event  Starting January 2015  Complementary with your SAP d-code registration http://sapdcodehandson.sap.com
  • 21. © 2014 SAP SE or an SAP affiliate company. All rights reserved. Thank you! Contact information: Christoph Morgen SAP HANA Product Management SAP SE | Dietmar-Hopp-Allee 16 | 69190 Walldorf | Germany christoph.morgen@sap.com | www.sap.com
  • 22. 23© 2014 SAP SE or an SAP affiliate company. All rights reserved. Feedback Please complete your session evaluation for DMM161. Thanks for attending this d-code session. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 23Public
  • 23. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 24Public © 2014 SAP SE or an SAP affiliate company. All rights reserved. No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or an SAP affiliate company. SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices. Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors. National product specifications may vary. These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty. In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward- looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.