SAP HANA Advanced Data Modeling by Ankisettipalli, Anil Babu -- Read -- Imperial Library of Trantor

Index

Dear Reader Notes on Usage Table of Contents Preface 1 SAP HANA Data Models

1.1 SAP HANA Database Architecture Overview 1.2 SAP HANA Modeling Paradigms

1.2.1 Client and Data Connection 1.2.2 Modeled Views 1.2.3 Stored Procedures 1.2.4 C++ (Application Function Libraries) 1.2.5 L Language 1.2.6 R Language

1.3 Information Views

1.3.1 Attribute Views 1.3.2 Analytic Views 1.3.3 Calculation Views

1.4 Analytic Privileges

1.4.1 Classical XML-Based Analytic Privilege 1.4.2 SQL-Based Analytic Privilege

1.5 Stored Procedures

1.5.1 SQLScript Procedures 1.5.2 L Procedures 1.5.3 R Procedures

1.6 Application Function Library

1.6.1 Business Function Library 1.6.2 Predictive Analysis Library

1.7 Summary

2 Modeling Complex Logic

2.1 Achieving Recursive Logic with Hierarchies

2.1.1 Creating Hierarchies with Tables 2.1.2 Creating a Hierarchy in an Attribute or Calculation View 2.1.3 Hierarchy View Attributes

2.2 Transposing Columns and Rows

2.2.1 Column-to-Row Transposition 2.2.2 Row-to-Column Transposition 2.2.3 Reversing a Matrix 2.2.4 Merging Data from Multiple Records 2.2.5 Splitting Strings

2.3 Using cube() with Hierarchies 2.4 Calculating Running Total 2.5 Calculating Cumulative Sum 2.6 Filtering Data Based on Ranking

2.6.1 Using a Subquery 2.6.2 Using Window Functions 2.6.3 Manipulating Concatenated Virtual Columns 2.6.4 Using a Rank Node in a Calculation View

2.7 Controlling Join Paths via Filters 2.8 Full Outer Join in a Calculation View 2.9 Making Dynamic Queries in a Stored Procedure

2.9.1 Changing Tables Dynamically 2.9.2 Changing Filters Dynamically 2.9.3 Changing Output Columns Dynamically

2.10 Showing History Records Side By Side 2.11 Sample Data

2.11.1 Using RAND() 2.11.2 Using $rowid$ 2.11.3 Using Identity Columns 2.11.4 Using LIMIT/OFFSET 2.11.5 Using the TABLESAMPLE SYSTEM

2.12 Using a Vertical Union to Join Tables 2.13 Sorting Records

2.13.1 Sorting IP Addresses 2.13.2 Sorting with Exceptions 2.13.3 Sorting with User-Defined Rules

2.14 Finding Missing Values

2.14.1 Using the NOT IN Clause 2.14.2 Using a Self-Join 2.14.3 Using a Vertical Union 2.14.4 Using Window Functions

2.15 Using Window Functions for Complex Grouping 2.16 Joining Based on a Date Sequence 2.17 Using a Nested Calculation View 2.18 Summary

3 Scaling for Large Datasets

3.1 Partitioning

3.1.1 Round-Robin Partitioning 3.1.2 Range Partitioning 3.1.3 Hash Partitioning 3.1.4 Two-Level Partitioning

3.2 Using Input Parameters to Enforce Pruning 3.3 Creating an Index 3.4 Analyzing Query Performance with Tools

3.4.1 Explain Plan 3.4.2 Visualize Plan 3.4.3 Performance Trace

3.5 Enforcing Execution Paths 3.6 Using a Union with Constant Values Instead of a Join 3.7 Manipulating Joins in an Analytic View

3.7.1 Breaking a Union of Dimension Tables 3.7.2 Making Nonequi Joins 3.7.3 Modifying Tables

3.8 Time Traveling

3.8.1 History Tables 3.8.2 Simulated History Tables

3.9 Storing Temporary Data 3.10 Calculating Count Distinct 3.11 Using Cached Views

3.11.1 Defining a Result Cache 3.11.2 Defining a View Cache

3.12 Summary

4 Basic Predictive Modeling

4.1 Predictive Analytics Lifecycle in SAP HANA

4.1.1 Commonly Used Models 4.1.2 Predictive Algorithms in SAP HANA 4.1.3 Application Function Library 4.1.4 Business Example

4.2 Data Exploration

4.2.1 Understanding Sales Data 4.2.2 Correlation and Autocorrelation 4.2.3 Deterministic Variables

4.3 Data Preparation

4.3.1 Predictive Data Types 4.3.2 Cleaning and Preparing Data

4.4 Modeling

4.4.1 Predictive Modeling Tasks 4.4.2 Setting Control Parameters 4.4.3 Creating and Maintaining Models 4.4.4 Validating Models 4.4.5 Scoring Models 4.4.6 Business Example

4.5 Creating Models Using SAP Applications on SAP HANA

4.5.1 Application Function Modeler 4.5.2 SAP Predictive Analytics

4.6 Summary

5 Advanced Predictive Modeling

5.1 R Script Modeling and Design

5.1.1 SAP HANA and R Integration 5.1.2 Data Considerations 5.1.3 Data Types and RLANG Script

5.2 PAL Model Consumption 5.3 Real-Time Model Consumption vs. Batch Predictive Modeling

5.3.1 Real-Time Model Execution 5.3.2 Batch Predictive Model Execution

5.4 Impact of Data Partitions in Predictive Modeling 5.5 Using Multiple R Servers and Data Partitions

5.5.1 Predictive Analysis Library 5.5.2 Using Multiple R Servers

5.6 Modeling Using R and PAL Simultaneously 5.7 Summary

6 Simulations and Optimizations

6.1 Case Study 6.2 Monte Carlo Simulation of Value-at-Risk

6.2.1 Random Variable Generation 6.2.2 Simulation Model and Process 6.2.3 Avoiding Imperative Logic

6.3 Portfolio Optimization

6.3.1 Variance-Covariance Matrix 6.3.2 Modeling for Optimization Constraints and Equations 6.3.3 Executing Optimization Models

6.4 Summary

The Authors Index Service Pages Legal Notes

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