SAP HANA Advanced Data Modeling
(Sprache: Englisch)
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...
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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
Inhaltsverzeichnis zu „SAP HANA Advanced Data Modeling “
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 ... FindingMissing 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 ...
... mehr
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
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
... weniger
Autoren-Porträt von Anil Babu Ankisettipalli, Hansen Chen, Pranav Wankawala
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.
Bibliographische Angaben
- Autoren: Anil Babu Ankisettipalli , Hansen Chen , Pranav Wankawala
- 2015, 392 Seiten, mit Abbildungen, Maße: 18,2 x 23,6 cm, Gebunden, Englisch
- Verlag: Rheinwerk Verlag
- ISBN-10: 1493212362
- ISBN-13: 9781493212361
- Erscheinungsdatum: 28.10.2015
Sprache:
Englisch
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