SAP Hana Advanced Data Modeling -

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Auteur(s) : Collectif

Editeur : Rheinwerk Publishing Inc.

Langue : Anglais

Parution : 01/10/2015

Format : Moyen, de 350g à 1kg

Nombre de pages : 392

Expédition : 883

Dimensions : 23.6 x 18.2 x 3.0

ISBN : 9781493212361

Résumé :
Want the latest and greatest info on advanced SAP HANA data modeling from the SAP HANA architects at SAP Labs in Palo Alto? Then this is the only book for you. Through step-by-step instructions and screenshots, this book will teach you how to design predictive, simulation, and optimization models. From information views to AFL models, you'll learn to scale for large datasets and performance tune your models to perfection.

Complex Logic Learn how to design SAP HANA data models to represent complex business logic, through practical examples and step-by-step modeling how-tos.

Predictive Modeling Use PAL and R to build advanced predictive models that make the most of SAP HANA's predictive power.

Performance Tuning Discover basic tuning techniques for your SAP HANA data models, such as avoiding long join paths, defining partitions, pushing down filters, and more.

Highlights:

• Analytic, attribute, and calculation views
• Application Function Library models
• PAL
• SQL
• R procedures
• Basic and advanced predictive modeling
• Simulations and optimization
• Performance tuning
• Complex business logic
• SAP HANA Database Engine
• SAP HANA modeling paradigms

Biographie:
Anil Babu Ankisettipalli is a post graduate in computer applications and has more than 17 years of experience in developing application and technology software. Since 2010, he has designed and developed SAP HANA applications and products using predictive analytics, machine learning, and data mining algorithms. At SAP Labs, he uses data science and big data technologies within the SAP HANA platform to solve critical problems of SAP customers.

Sommaire:


1 ... SAP HANA Data Models ... 21

1.1 ... SAP HANA Database Architecture Overview ... 21

1.2 ... SAP HANA Modeling Paradigms ... 22

1.3 ... Information Views ... 26

1.4 ... Analytic Privileges ... 67

1.5 ... Stored Procedures ... 75

1.6 ... Application Function Library ... 86

1.7 ... Summary ... 90

2 ... Modeling Complex Logic ... 93

2.1 ... Achieving Recursive Logic with Hierarchies ... 93

2.2 ... Transposing Columns and Rows ... 110

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

2.7 ... 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

2.10 ... Showing History Records Side By Side ... 153

2.11 ... Sample Data ... 158

2.12 ... Using a Vertical Union to Join Tables ... 161

2.13 ... Sorting Records ... 163

2.14 ... Finding Missing Values ... 168

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

3.2 ... Using Input Parameters to Enforce Pruning ... 198

3.3 ... Creating an Index ... 201

3.4 ... Analyzing Query Performance with Tools ... 205

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

3.8 ... Time Traveling ... 236

3.9 ... Storing Temporary Data ... 243

3.10 ... Calculating Count Distinct ... 247

3.11 ... Using Cached Views ... 250

3.12 ... Summary ... 258

4 ... Basic Predictive Modeling ... 259

4.1 ... Predictive Analytics Lifecycle in SAP HANA ... 259

4.2 ... Data Exploration ... 270

4.3 ... Data Preparation ... 291

4.4 ... Modeling ... 297

4.5 ... Creating Models Using SAP Applications on SAP HANA ... 308

4.6 ... Summary ... 318

5 ... Advanced Predictive Modeling ... 319


5.1 ... R Script Modeling and Design ... 319

5.2 ... PAL Model Consumption ... 326

5.3 ... Real-Time Model Consumption vs. Batch Predictive Modeling ... 329

5.4 ... Impact of Data Partitions in Predictive Modeling ... 332

5.5 ... Using Multiple R Servers and Data Partitions ... 333

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

6.3 ... Portfolio Optimization ... 363

6.4 ... Summary ... 380

The Authors ... 381