SAP Analytics Cloud Performance Optimization Guide

1. Auflage 2024
ISBN: 978-1-4932-2396-1
Verlag: Rheinwerk Verlag GmbH

Buch, Englisch, 344 Seiten, Format (B × H): 182 mm x 259 mm, Gewicht: 888 g

Reihe: SAP PRESS: englisch

Frustrated with slow performance in SAP Analytics Cloud? This guide is your answer! Learn the basics of solution performance and optimization and then start making your adjustments: get the story builder and analytics designer apps running smoothly, fine-tune your backend settings, and follow best practices for viewing dashboards. With detailed examples of performance improvements throughout, this book has everything you need for an optimal SAP Analytics Cloud experience!

Highlights include:

1) User experience

2) Dashboards

3) Performance analysis

4) Story building

5) Model building

6) Analytics designer

7) Backend and tenant optimization

8) Viewing optimizations

9) Acquired data

10) Live data connections

11) Best practices

Autoren/Hrsg.

Bertram, Erik

Dannenhauer, Carl

Holzapfel, Melanie

Loop, Sandra

Range, Stephanie

Fachgebiete

• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing
• Mathematik | Informatik EDV | Informatik Business Application Unternehmenssoftware SAP

Inhaltsverzeichnis

·. Preface. 15

·. Target Audience. 15

·. How to Read this Book. 16

·. What You Can Expect from This Book. 16

·. How This Book Is Organized. 17

·. Acknowledgments. 19

·. Conclusion. 19

· 1. Introduction. 21

· 1.1. Performance and Its Relation to User Experience. 22

· 1.2. SAP Analytics Cloud Performance. 30

· 1.3. Summary. 42

· 2. Measuring, Testing, and Monitoring Performance. 45

· 2.1. Different Types of Performance Tests. 46

· 2.2. Performance Testing versus Performance Engineering. 52

· 2.3. Performance Testing Process. 55

· 2.4. Defining the Metrics to Measure. 58

· 2.5. Setting Performance Goals. 64

· 2.6. Measuring Performance and System Key Performance Indicators. 67

· 2.7. Creating Benchmark Tests. 79

· 2.8. SAP Analytics Cloud Performance Equation. 81

· 2.9. Ten Golden Rules for Efficient SAP Analytics Cloud Dashboards. 83

· 2.10. Summary. 89

· 3. Optimizing Performance in the Story Builder. 91

· 3.1. General Settings to Improve Performance. 92

· 3.2. Optimizing the Story Design. 100

· 3.3. Optimizing Filters, Calculations, and Linked Analysis. 118

· 3.4. Optimizing Mobile Device Performance. 124

· 3.5. Using the Explorer View to Improve Performance. 128

· 3.6. Planning Scenarios: Optimizing Settings and Advanced Formulas. 131

· 3.7. The Performance Analysis Tool. 147

· 3.8. Best Practices. 159

· 3.9. Summary. 161

· 4. Analytics Designer Performance. 163

· 4.1. Avoiding Poor Performance When Creating an Analytics Application. 163

· 4.2. General Rules to Improve Performance. 168

· 4.3. Optimizing the Application Structure. 172

· 4.4. Optimizing Responsive Design Performance for Mobile and Other Devices. 184

· 4.5. Optimizing Performance for Scripts. 190

· 4.6. Analytics Application Script Performance Popup Tool. 195

· 4.7. Best Practices. 199

· 4.8. Summary. 200

· 5. Modeler Performance. 203

· 5.1. General Modeler Settings to Improve Performance. 203

· 5.2. Choosing an Efficient Model. 207

· 5.3. Performance of Acquired Models and Live Connection Models. 211

· 5.4. Optimizing Performance for Planning Models. 211

· 5.5. Best Practices. 214

· 5.6. Summary. 215

· 6. Optimizing Backend and Tenant Settings. 217

· 6.1. Optimizing Backend Data Storage Connection. 217

· 6.2. Optimizations in the Backend Data. 222

· 6.3. System Configuration Settings to Improve Performance. 224

· 6.4. Best Practices. 234

· 6.5. Summary. 235

· 7. Viewer Choices. 237

· 7.1. Optimizations When Viewing Dashboards. 237

· 7.2. Optimizations When Presenting to Others. 247

· 7.3. Best Practices. 250

· 7.4. Summary. 251

· 8. Optimized Dashboard Using Acquired Data. 253

· 8.1. Scenario. 253

· 8.2. Data Structure. 257

· 8.3. Dashboard Features. 264

· 8.4. Building an Efficient Dashboard. 265

· 8.5. Summary. 280

· 9. Optimized Dashboard Using Live Data Connection. 281

· 9.1. Live Data Connection Scenarios. 281

· 9.2. Data Structure. 284

· 9.3. Dashboard Features. 292

· 9.4. Guidelines for Building an Efficient Dashboard. 303

· 9.5. Summary. 304

· 10. Optimized Dashboard Using the Analytics Designer. 305

· 10.1. Scenario. 306

· 10.2. Data Structure. 308

· 10.3. Dashboard Features. 314

· 10.4. Building an Efficient Dashboard. 315

· 10.5. Summary. 331

·. Appendices. 333

· A. Further Reading. 333

· B. The Authors. 337

·. Index. 339

Über Autor(innen)

Bertram, Erik
Erik Bertram is a software developer who currently leads the Global Design Operations and Services team in the HANA and Analytics organization at SAP. He is responsible for all kinds of customer engagements and operational activities. Before joining SAP in 2016, Erik was working as a postdoctoral researcher at Heidelberg University. He holds a PhD in theoretical astrophysics.

Dannenhauer, Carl
Carl Dannenhauer joined SAP in 2020 and is currently a data scientist in the SAP Business Technology Platform design organization. He has spent his professional career fostering data-driven decisions by building advanced dashboards and generating actionable insights by analyzing user feedback. As an SAP Analytics Cloud expert his focus lies in developing effective and easy-to-use dashboards. Carl has a studies background in business informatics.

Holzapfel, Melanie
Melanie Holzapfel is a data scientist in the HANA & Analytics organization at SAP. She joined SAP in 2019 after finishing her physics studies at Heidelberg University. At SAP, she is responsible for analyzing customer feedback and building dashboards with suitable data visualization graphics.

Range, Stephanie
Stephanie Range joined SAP in 2021 and is currently a data scientist in the SAP Business Technology Platform design organization. Before joining SAP, Stephanie worked as a data scientist in a pharma company, where she developed and automated statistical methods, configured data bases, and validated business processes. Today, her expertise lies in optimizing and building SAP Analytics Cloud dashboards. She holds a master’s degree in physics.

Loop, Sandra
Sandra Loop joined SAP in 2016 and is a user researcher in the SAP Business Technology Platform design organization. She has been a user experience designer and user researcher for over 25 years. Prior to joining SAP, she was a developer and UX designer for a data visualization company and she was also a product manager where dashboards were a major component of the product’s user interface. Sandra believes that a good user experience is crucial for product success and that good performance is an important factor of the user experience.