Table of Contents

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Acknowledgments
17
Preface
19
Purpose
19
Who Should Read This Book
19
Structure of This Book
20
Part I: Introduction
20
Part II: Getting Data into SAP HANA
21
Part III: Multidimensional Modeling in SAP HANA
21
Part IV: Integrating SAP HANA with SAP Business Intelligence Tools
22
Part I: Introduction
25
1 SAP HANA, SAP BusinessObjects BI, and SAP Data Services
27
1.1 What Is SAP HANA?
28
1.1.1 Software Layers and Features
28
1.1.2 Hardware Layers and Features
32
1.2 Business Intelligence Solutions with SAP HANA
38
1.2.1 SAP BW on SAP HANA
38
1.2.2 Native Implementation of SAP HANA for Analytics
42
1.3 SAP Business Suite on SAP HANA
57
1.4 Traditional EIM with SAP Data Services
60
1.4.1 Align IT with the Business
60
1.4.2 Establish Processes to Manage the Data
61
1.4.3 Source System Analysis
61
1.4.4 Develop a Data Model
62
1.4.5 Load the Data
62
1.5 Traditional Business Intelligence with SAP BusinessObjects BI
62
1.5.1 The Semantic Layer (Universe)
63
1.5.2 Ad Hoc Reporting
64
1.5.3 Self-Service BI
64
1.5.4 IT-Developed Content
66
1.6 Solution Architectural Overview
66
1.6.1 SAP Data Services
67
1.6.2 SAP BusinessObjects BI
70
1.6.3 SAP HANA
73
1.7 Summary
76
2 Securing the SAP HANA Environment
77
2.1 Configuring the SAP HANA Environment for Development
78
2.1.1 Introduction to the SAP HANA Repository
79
2.1.2 Configuring SAP HANA Studio
80
2.1.3 Setting Up Packages and Development Projects
87
2.1.4 Setting up Schemas in SAP HANA
95
2.2 SAP HANA Authorizations
100
2.2.1 Types of SAP HANA Privileges
101
2.2.2 Granting of Privileges and the Life Cycle of a Grant
106
2.3 User and Role Provisioning
108
2.3.1 Creating Roles (the Traditional Approach)
109
2.3.2 Creating Roles as Repository Objects
110
2.3.3 Preventing Rights Escalation Scenarios
115
2.3.4 Common Role Scenarios and Their Privileges
116
2.3.5 User Provisioning
126
2.4 SAP HANA Authentication
131
2.4.1 Internal Authentication with User Name and Password
133
2.4.2 Kerberos Authentication
135
2.4.3 SAML Authentication
136
2.4.4 Other Web-Based Authentication Methods for SAP HANA XS
137
2.4.5 Summary and Recommendations
138
2.5 Case Study: An End-to-End Security Configuration
139
2.5.1 Authentication Plan
139
2.5.2 Authorization Plan
147
2.5.3 User Provisioning Plan
150
2.6 Summary
152
3 Data Storage in SAP HANA
155
3.1 OLAP and OLTP Data Storage
155
3.1.1 The Spinning Disk Problem
157
3.1.2 Combating the Problem
157
3.2 Data Storage Components
165
3.2.1 Schemas and Users
165
3.2.2 Column-Store Tables
167
3.2.3 Row-Store Tables
172
3.2.4 Use Cases for Both Row- and Column-Store Tables
173
3.3 Modeling Tables and Data Marts
175
3.3.1 Legacy Relational OLAP Modeling
176
3.3.2 SAP HANA Relational OLAP Modeling
180
3.3.3 Denormalizing Data in SAP HANA
183
3.4 Case Study: Creating Data Marts and Tables for an SAP HANA Project
186
3.4.1 Creating a Schema for the Data Mart
187
3.4.2 Creating the Fact Table and Dimension Tables in SAP HANA
189
3.5 Summary
194
Part II: Getting Data Into SAP HANA
195
4 Preprovisioning Data with SAP Data Services
197
4.1 Making the Case for Source System Analysis
197
4.2 SSA Techniques in SAP Data Services
202
4.2.1 Column Profiling
205
4.2.2 Relationship Profiling
211
4.3 SSA: Beyond Tools and Profiling
215
4.3.1 Establishing Patterns
217
4.3.2 Looking Across Sources
219
4.3.3 Treating Disparate Systems as One
219
4.3.4 Mapping Your Data
220
4.4 Summary
222
5 Provisioning Data with SAP Data Services
223
5.1 Provisioning Data Using SAP Data Services Designer
223
5.1.1 Metadata
225
5.1.2 Datastores
227
5.1.3 Jobs
231
5.1.4 Workflows
234
5.1.5 Data Flows
244
5.1.6 Transforms
255
5.1.7 Built-In Functions
277
5.1.8 Custom Functions and Scripts
281
5.1.9 File Formats
285
5.1.10 Real-Time Jobs
288
5.2 Introduction to SAP Data Services Workbench
290
5.2.1 Building a Data Flow
293
5.2.2 Moving Data from an Existing Data Warehouse
297
5.2.3 Porting Data with the Quick Replication Wizard
297
5.2.4 Modifying Data Flows and Jobs
304
5.3 Data Provisioning via Real-Time Replication
305
5.3.1 SAP Data Services ETL-Based Method (ETL and DQ)
306
5.3.2 SAP Landscape Transformation
307
5.4 Summary
308
6 Loading Data with SAP Data Services
309
6.1 Loading Data in a Batch
309
6.1.1 Steps
309
6.1.2 Methods
319
6.1.3 Triggers
328
6.2 Loading Data in Real Time
335
6.3 Case Study: Loading Data in a Batch
340
6.3.1 Initialization
344
6.3.2 Staging
345
6.3.3 Mart
374
6.3.4 End Script
387
6.4 Case Study: Loading Data in Real Time
389
6.5 Summary
395
Part III: Multidimensional Modeling in SAP HANA
397
7 Introduction to Multidimensional Modeling
399
7.1 Understanding Multidimensional Models
400
7.2 Benefits of SAP HANA Multidimensional Modeling
404
7.2.1 Business Benefits
404
7.2.2 Technology Benefits
408
7.3 Summary
411
8 Tools and Components of Multidimensional Modeling
413
8.1 SAP HANA Studio
413
8.1.1 Systems View
417
8.1.2 Quick Launch View
418
8.2 Schemas
420
8.3 Packages
423
8.4 Summary
426
9 Creating SAP HANA Information Views
427
9.1 Attribute Views
427
9.1.1 Creating an Attribute View
429
9.1.2 Defining Properties of an Attribute View
431
9.1.3 Creating Hierarchies
439
9.1.4 Saving and Activating the Attribute View
442
9.2 Analytic Views
444
9.2.1 Creating an Analytic View
445
9.2.2 Defining Properties of an Analytic View
447
9.2.3 Saving and Activating the Analytic View
459
9.3 Calculation Views
460
9.3.1 Creating a Calculation View
461
9.3.2 Defining a Graphical Calculation View
466
9.3.3 Defining a Script-Based Calculation View
474
9.4 Summary
478
10 Multidimensional Modeling in Practice
479
10.1 Data Processing in SAP HANA
479
10.1.1 Normalized Data versus Denormalized Data
480
10.1.2 Data Modeling versus Multidimensional Modeling
485
10.1.3 Managing Normalized Data in SAP HANA
487
10.2 Case Study 1: Modeling Sales Data to Produce Robust Analytics
490
10.2.1 Creating the Supporting Attribute Views
490
10.2.2 Creating Analytic Views
508
10.3 Case Study 2: Building Complex Calculations for Executive-Level Analysis
515
10.3.1 Creating the Package
516
10.3.2 Creating the Calculation View
518
10.3.3 Defining the Calculation View
520
10.4 Summary
533
11 Securing Data in SAP HANA
535
11.1 Introduction to Analytic Privileges
536
11.1.1 What are Analytic Privileges?
536
11.1.2 Types of Analytic Privileges
537
11.1.3 Dynamic vs. Static Value Restrictions
538
11.2 Creating Analytic Privileges
540
11.2.1 Traditional Analytic Privileges
540
11.2.2 SQL-Based Analytic Privileges
556
11.3 Applying Analytic Privileges
561
11.3.1 Applying Analytic Privileges to Information Views
561
11.3.2 Interaction of Multiple Analytic Privileges and Multiple Restrictions
563
11.3.3 Interaction of Multiple Information Views with Analytic Privileges
564
11.4 Case Study: Securing Sales Data with Analytic Privileges
567
11.4.1 Overview and Requirements
568
11.4.2 Implementation Strategy
569
11.4.3 Implementation Examples
570
11.5 Summary
581
Part IV: Integrating SAP HANA with SAP Business Intelligence Tools
583
12 Building Universes for SAP HANA
585
12.1 SAP HANA and the Universe
587
12.1.1 When to Use a Universe with SAP HANA
590
12.1.2 Connecting Universes to SAP HANA
592
12.2 Manually Building UNX Universes for SAP HANA
600
12.2.1 Creating Relational Connections
601
12.2.2 Creating OLAP Connections
610
12.2.3 Testing Connections Using the Local or Server Middleware
612
12.2.4 Creating Projects
613
12.2.5 Designing the Data Foundation
617
12.2.6 Designing the Business Layer
632
12.2.7 Publishing the Universe
639
12.3 Automatically Generating UNX Universes for SAP HANA
642
12.3.1 Creating a Local Connection
642
12.3.2 Selecting Information Views
645
12.3.3 Reviewing the Data Foundation and Business Layer
648
12.3.4 How SAP HANA Metadata Impacts the Process
655
12.4 The SAP HANA Engines in Universe Design
656
12.4.1 SAP HANA Join Engine
658
12.4.2 SAP HANA OLAP Engine
659
12.4.3 SAP HANA Calculation Engine
660
12.5 Case Study: Designing a Universe to Support Internet Sales Data
662
12.5.1 Creating the Universe Connection and Project
662
12.5.2 Designing the Data Foundation
663
12.5.3 Designing the Business Layer
671
12.5.4 Publishing the Universe
688
12.6 Summary
689
13 Predictive Analytics with SAP HANA
691
13.1 Predictive Analysis and SAP HANA: The Basics
692
13.1.1 The Predictive Analysis Process
696
13.1.2 When to Use Predictive Analytics
703
13.1.3 Predictive Tools Available in SAP HANA
706
13.2 Integrating with SAP HANA
712
13.2.1 Installing the Application Function Libraries
712
13.2.2 Deploying Rserve
712
13.2.3 Leveraging R and PAL to Produce Predictive Results
713
13.2.4 Installing SAP Predictive Analysis
714
13.2.5 User Privileges and Security with SAP Predictive Analysis
714
13.3 Integrating with SAP BusinessObjects BI
716
13.3.1 Exporting Scored Data Back to Databases
716
13.3.2 Exporting Algorithms
717
13.4 Case Study 1: Clustering Analysis
719
13.4.1 Preparing the Data
720
13.4.2 Performing Clustering Analysis
724
13.4.3 Implementing the Model
732
13.5 Case Study 2: Product Recommendation Rules
738
13.5.1 Preparing the Data
738
13.5.2 Performing Apriori Analysis
738
13.5.3 Implementing the Model
745
13.6 Summary
750
14 Professionally Authored Dashboards with SAP HANA
751
14.1 SAP HANA as a Data Source for SAP BusinessObjects Dashboards
754
14.2 SAP HANA as a Data Source for SAP BusinessObjects Design Studio
759
14.2.1 Connecting to SAP BW on SAP HANA
760
14.2.2 Connecting Directly to SAP HANA Data Sources
760
14.2.3 Connecting to the SAP HANA XS Engine
761
14.2.4 Consuming the SAP HANA Connections
763
14.3 Case Study: Exploring Data with SAP BusinessObjects Design Studio on Top of SAP HANA
764
14.3.1 Gathering Requirements
764
14.3.2 Laying Out the Components
765
14.3.3 Connecting to SAP HANA
766
14.4 Summary
769
15 Data Exploration and Self-Service Analytics with SAP HANA
771
15.1 SAP HANA as a Data Source for SAP BusinessObjects Explorer
772
15.1.1 Exploring and Indexing
773
15.1.2 Connecting SAP BusinessObjects Explorer to SAP HANA
776
15.1.3 Creating an Information Space on SAP HANA
778
15.2 SAP HANA as a Data Source for SAP Lumira
780
15.2.1 Online Connectivity
783
15.2.2 Offline Connectivity
784
15.3 Case Study: Exploring Sales Data with SAP Lumira on Top of SAP HANA
789
15.3.1 Business Requirements
790
15.3.2 Planned Solution
790
15.4 Summary
796
16 SAP BusinessObjects Web Intelligence with SAP HANA
797
16.1 Connecting SAP BusinessObjects Web Intelligence to SAP HANA
797
16.2 Report Optimization Features with SAP HANA
800
16.2.1 Usage of JOIN_BY_SQL
800
16.2.2 Merged Dimensions versus Analytic/Calculation Views
803
16.2.3 Query Drill
804
16.2.4 Query Stripping
806
16.3 Case Study: Exploring Sales Data with SAP BusinessObjects Web Intelligence on Top of SAP HANA
808
16.4 Summary
814
17 SAP Crystal Reports with SAP HANA
815
17.1 SAP HANA as a Data Source for SAP Crystal Reports
817
17.1.1 Configuring ODBC and JDBC Connections
819
17.1.2 Using SAP BusinessObjects IDT Universes
822
17.1.3 Using SAP BusinessObjects BI Relational Connections
824
17.1.4 Direct OLAP Connectivity to Analytic and Calculation Views
826
17.2 Case Study: Exploring Data with SAP Crystal Reports on Top of SAP HANA
831
17.2.1 Connecting to Data
832
17.2.2 Designing the Query
833
17.2.3 Limiting Query Results with Filter
834
17.2.4 Formatting the Report Display
835
17.3 Summary
836
Appendices
837
A Source System Analysis with SAP Information Steward Data Insight
837
A.1 Column Profiling
841
A.2 Address Profiling
843
A.3 Dependency Profiling
845
A.4 Redundancy Profiling
846
A.5 Uniqueness Profiling
847
A.6 Summary
848
B The Authors
849
Index
851