Implementing SAP Business Suite on SAP HANA [prepare and execute a migration of SAP Business Suite from Any DB to SAP HANA ; understand how to test and monitor your new system once live ; learn how to optimize SAP ERP settings and ABAP code for SAP HANA]

If you're thinking about moving your SAP Business Suite to an SAP HANA database, this book will serve as your go-to guide. Understand and complete all steps of the migration process, from planning the transition to making the move and going live. Then discover how to optimize your system for SAP HANA by activating enhancements and installing SAP S/4HANA Finance (SAP Simple Finance) and SAP HANA Live. Detailed instructions and guiding screenshots will help get your new platform up and running in no time! Migration Planning and Process Find out what you need to do to migrate SAP Business Suite to SAP HANA, from planning to execution. Understand how to perform the migration, and get to know the various migration tools and options available.Go Live Learn how to test your new system with SAP Solution Manager and third party tools. Discover how to monitor your database using SAP HANA Studio, SAP Solution Manager Technical Operations, and more. Optimization Discover the enhancements you can make to ensure that you get the most out of your database on SAP HANA, from optimized ABAP code to updates for SAP ERP Financials. Highlights:Landscape assessment Migration dependencies Security planning Migration via DMO or SWPM Change management Regression testing Monitoring SAP Administration Cockpit Downtime planning ABAP optimization Cutover planning SAP Simple Finance Contents 9 Preface 15 1 SAP HANA Data Models 21 1.1 SAP HANA Database Architecture Overview 21 1.2 SAP HANA Modeling Paradigms 22 1.2.1 Client and Data Connection 23 1.2.2 Modeled Views 24 1.2.3 Stored Procedures 24 1.2.4 C++ (Application Function Libraries) 25 1.2.5 L Language 25 1.2.6 R Language 25 1.3 Information Views 26 1.3.1 Attribute Views 26 1.3.2 Analytic Views 37 1.3.3 Calculation Views 52 1.4 Analytic Privileges 67 1.4.1 Classical XML-Based Analytic Privilege 67 1.4.2 SQL-Based Analytic Privilege 73 1.5 Stored Procedures 75 1.5.1 SQLScript Procedures 75 1.5.2 L Procedures 81 1.5.3 R Procedures 82 1.6 Application Function Library 86 1.6.1 Business Function Library 86 1.6.2 Predictive Analysis Library 88 1.7 Summary 90 2 Modeling Complex Logic 93 2.1 Achieving Recursive Logic with Hierarchies 93 2.1.1 Creating Hierarchies with Tables 94 2.1.2 Creating a Hierarchy in an Attribute or Calculation View 97 2.1.3 Hierarchy View Attributes 100 2.2 Transposing Columns and Rows 110 2.2.1 Column-to-Row Transposition 110 2.2.2 Row-to-Column Transposition 116 2.2.3 Reversing a Matrix 118 2.2.4 Merging Data from Multiple Records 121 2.2.5 Splitting Strings 122 2.3 Using cube() with Hierarchies 123 2.4 Calculating Running Total 127 2.5 Calculating Cumulative Sum 131 2.6 Filtering Data Based on Ranking 134 2.6.1 Using a Subquery 134 2.6.2 Using Window Functions 135 2.6.3 Manipulating Concatenated Virtual Columns 136 2.6.4 Using a Rank Node in a Calculation View 137 2.7 Controlling Join Paths via Filters 138 2.8 Full Outer Join in a Calculation View 143 2.9 Making Dynamic Queries in a Stored Procedure 148 2.9.1 Changing Tables Dynamically 148 2.9.2 Changing Filters Dynamically 150 2.9.3 Changing Output Columns Dynamically 152 2.10 Showing History Records Side By Side 153 2.11 Sample Data 158 2.11.1 Using RAND() 158 2.11.2 Using $rowid$ 158 2.11.3 Using Identity Columns 159 2.11.4 Using LIMIT/OFFSET 160 2.11.5 Using the TABLESAMPLE SYSTEM 160 2.12 Using a Vertical Union to Join Tables 161 2.13 Sorting Records 163 2.13.1 Sorting IP Addresses 163 2.13.2 Sorting with Exceptions 166 2.13.3 Sorting with User-Defined Rules 167 2.14 Finding Missing Values 168 2.14.1 Using the NOT IN Clause 169 2.14.2 Using a Self-Join 170 2.14.3 Using a Vertical Union 171 2.14.4 Using Window Functions 171 2.15 Using Window Functions for Complex Grouping 172 2.16 Joining Based on a Date Sequence 178 2.17 Using a Nested Calculation View 185 2.18 Summary 191 3 Scaling for Large Datasets 193 3.1 Partitioning 193 3.1.1 Round-Robin Partitioning 194 3.1.2 Range Partitioning 194 3.1.3 Hash Partitioning 195 3.1.4 Two-Level Partitioning 197 3.2 Using Input Parameters to Enforce Pruning 198 3.3 Creating an Index 201 3.4 Analyzing Query Performance with Tools 205 3.4.1 Explain Plan 205 3.4.2 Visualize Plan 207 3.4.3 Performance Trace 208 3.5 Enforcing Execution Paths 214 3.6 Using a Union with Constant Values Instead of a Join 218 3.7 Manipulating Joins in an Analytic View 224 3.7.1 Breaking a Union of Dimension Tables 225 3.7.2 Making Nonequi Joins 230 3.7.3 Modifying Tables 232 3.8 Time Traveling 236 3.8.1 History Tables 237 3.8.2 Simulated History Tables 240 3.9 Storing Temporary Data 243 3.10 Calculating Count Distinct 247 3.11 Using Cached Views 250 3.11.1 Defining a Result Cache 251 3.11.2 Defining a View Cache 254 3.12 Summary 258 4 Basic Predictive Modeling 259 4.1 Predictive Analytics Lifecycle in SAP HANA 259 4.1.1 Commonly Used Models 261 4.1.2 Predictive Algorithms in SAP HANA 264 4.1.3 Application Function Library 266 4.1.4 Business Example 269 4.2 Data Exploration 270 4.2.1 Understanding Sales Data 270 4.2.2 Correlation and Autocorrelation 279 4.2.3 Deterministic Variables 285 4.3 Data Preparation 291 4.3.1 Predictive Data Types 291 4.3.2 Cleaning and Preparing Data 292 4.4 Modeling 297 4.4.1 Predictive Modeling Tasks 297 4.4.2 Setting Control Parameters 299 4.4.3 Creating and Maintaining Models 299 4.4.4 Validating Models 299 4.4.5 Scoring Models 300 4.4.6 Business Example 300 4.5 Creating Models Using SAP Applications on SAP HANA 308 4.5.1 Application Function Modeler 308 4.5.2 SAP Predictive Analytics 311 4.6 Summary 318 5 Advanced Predictive Modeling 319 5.1 R Script Modeling and Design 319 5.1.1 SAP HANA and R Integration 319 5.1.2 Data Considerations 321 5.1.3 Data Types and RLANG Script 324 5.2 PAL Model Consumption 326 5.3 Real-Time Model Consumption vs. Batch Predictive Modeling 329 5.3.1 Real-Time Model Execution 329 5.3.2 Batch Predictive Model Execution 330 5.4 Impact of Data Partitions in Predictive Modeling 332 5.5 Using Multiple R Servers and Data Partitions 333 5.5.1 Predictive Analysis Library 333 5.5.2 Using Multiple R Servers 335 5.6 Modeling Using R and PAL Simultaneously 337 5.7 Summary 340 6 Simulations and Optimizations 341 6.1 Case Study 341 6.2 Monte Carlo Simulation of Value-at-Risk 342 6.2.1 Random Variable Generation 343 6.2.2 Simulation Model and Process 350 6.2.3 Avoiding Imperative Logic 358 6.3 Portfolio Optimization 363 6.3.1 Variance-Covariance Matrix 365 6.3.2 Modeling for Optimization Constraints and Equations 370 6.3.3 Executing Optimization Models 379 6.4 Summary 380 The Authors 381 Index 383