Manage your data landscape with SAP Data Intelligence! Begin by understanding its architecture and capabilities and then see how to set up and install SAP Data Intelligence with step-by-step instructions. Walk through SAP Data Intelligence applications and learn how to use them for data governance, orchestration, and machine learning. Integrate with ABAP-based systems, SAP Vora, SAP Analytics Cloud, and more. Manage, secure, and operate SAP Data Intelligence with this all-in-one guide!

In this book, you'll learn about:

a. Configuration
Build your SAP Data Intelligence landscape! Use SAP Cloud Appliance Library for cloud deployment, including provisioning, sizing, and accessing the launchpad. Perform on-premise installations using tools like the maintenance planner.

b. Capabilities
Put the core capabilities of SAP Data Intelligence to work! Manage and govern your data with the metadata explorer, use the modeler application to create data processing pipelines, create apps with the Jupyter Notebook, and more.

c. Integration and Administration
Integrate, manage, and operate SAP Data Intelligence! Get step-by-step instructions for integration with SAP and non-SAP systems. Learn about key administration tasks and make sure your landscape is secure and running smoothly.

Highlights include:

1) Configuration and installation
2) Data governance
3) Data processing pipelines
4) Docker images
5) ML Scenario Manager
6) Jupyter Notebook
7) Python SDK
8) Integration
9) Administration
10) Security
11) Application lifecycle management
12) Use cases

Use machine learning, SAP Analytics Cloud, and SAP Data Warehouse Cloud to enrich business data

Autorentext
Arindom Saha is an SAP business intelligence consultant with more than 11 years of experience working with the SAP analytics portfolio. He has extensive experience in SAP and non-SAP reporting and visualization products. He has implemented a variety of analytics solutions on different products, including SAP BusinessObjects BI, SAP HANA, SAP Business Warehouse, and Oracle RDBMS. He also has experience working with SAP Lumira, SAP Analytics Cloud, SAP Data Intelligence, and Tableau. His recent experience with SAP Data Intelligence has been contributing business content and working closely with cloud and on-premise providers to install and provision SAP Data Intelligence 3.1 as part of ongoing global initiatives.

Inhalt
... Preface ... 21

... Why Read This Book? ... 21

... Audience ... 22

... Structure of the Book ... 23

... Acknowledgments ... 28

... Conclusion ... 29

PART I ... Getting Started ... 31

1 ... The Data Fabric for the Intelligent Enterprise ... 33

1.1 ... Data Fabric ... 34

1.2 ... Data Orchestration ... 38

1.3 ... SAP Business Technology Platform ... 40

1.4 ... SAP Data Intelligence ... 43

1.5 ... Summary ... 50

2 ... Architecture and Capabilities ... 51

2.1 ... Genesis of SAP Data Intelligence ... 52

2.2 ... SAP Data Intelligence Architecture ... 60

2.3 ... Deployment Options and Bring Your Own License Model ... 63

2.4 ... Kubernetes Cluster and Containers ... 68

2.5 ... SAP Data Intelligence Launchpad ... 86

2.6 ... Summary ... 91

3 ... Setup and Installation ... 93

3.1 ... Landscape Sizing ... 93

3.2 ... SAP Cloud Appliance Library ... 99

3.3 ... On-Demand Cloud Provisioning and Instance Sizing ... 107

3.4 ... Setting Up SAP Data Intelligence on SAP Cloud Appliance Library ... 113

3.5 ... SAP Data Intelligence 3.0 Installation On-Premise ... 150

3.6 ... Summary ... 168

4 ... Using SAP Data Intelligence Applications ... 169

4.1 ... SAP Data Intelligence Launchpad Applications ... 169

4.2 ... Applications for Data Engineers ... 172

4.3 ... Applications for Data Scientists ... 177

4.4 ... Applications for Modelers and Auditors ... 179

4.5 ... Applications for System Administrators ... 182

4.6 ... Summary ... 189

PART II ... Data Management, Orchestration, and Machine Learning ... 191

5 ... Metadata-Driven Data Governance ... 193

5.1 ... Metadata Explorer for Data Governance ... 194

5.2 ... Data Profiling to Understand Data ... 197

5.3 ... Managing Publications and Data Catalogs ... 202

5.4 ... Defining Data Quality Rules and Running Rulebooks ... 214

5.5 ... Data Lineage from Transformation History ... 230

5.6 ... Summary ... 235

6 ... Modeling Data Processing Pipelines ... 237

6.1 ... Using the SAP Data Intelligence Modeler ... 237

6.2 ... Creating and Managing Connections ... 250

6.3 ... Self-Service Data Preparation with the Metadata Explorer ... 255

6.4 ... Integrating, Processing, and Orchestrating Workflows ... 261

6.5 ... Scheduling and Monitoring Data Pipelines ... 270

6.6 ... Summary ... 273

7 ... Creating Operators and Data Types ... 275

7.1 ... Creating Custom Operators ... 276

7.2 ... Implementing Runtime Operators ... 288

7.3 ... Creating Data Types ... 290

7.4 ... Summary ... 293

8 ... Building Docker Images ... 295

8.1 ... Containers in Pods and Pods in Clusters ... 295

8.2 ... Assembling a Docker Image ... 298

8.3 ... Dockerfile Inheritance ... 303

8.4 ... Using Docker with Python ... 305

8.5 ... Summary ... 308

9 ... Machine Learning ... 309

9.1 ... Machine Learning with SAP ... 310

9.2 ... Machine Learning with SAP Data Intelligence ... 328

9.3 ... Using the ML Scenario Manager ... 333

9.4 ... ML Data Manager in Data Workspaces and Data Collections ... 365

9.5 ... Summary ... 371

10 ... Jupyter Notebook ... 373

10.1 ... Jupyter Notebook Fundamentals ... 374

10.2 ... Working with SAP HANA Cloud ... 386

10.3 ... Data Science Experiments with Jupyter Notebook ... 405

10.4 ... JupyterLab as the Next-Gen Jupyter Notebook ... 430

10.5 ... Summary ... 437

11 ... SAP Data Intelligence Python SDK ... 439

11.1 ... Using SAP Data Intelligence Python SDK ... 440

11.2 ... Accessing Artifacts Using Methods ... 448

11.3 ... Machine Learning Tracking SDK ... 450

11.4 ... Summary ... 454

PART III ... Integration ... 457

12 ... Integrating with ABAP Systems ... 459

12.1 ... Integration Scenarios ... 459

12.2 ... Provisioning Data from ABAP Systems ... 465

12.3 ... Using Operators to Trigger Execution in an ABAP System ... 472

12.4 ... SAP BW/4HANA and SAP Data Intelligence Hybrid Data Virtualization ... 478

12.5 ... Additional Connectivity ... 485

12.6 ... Summary ... 495

13 ... Integrating with Non-SAP Systems ... 497

13.1 ... Non-SAP Cloud System Connectivity ... 497

13.2 ... Non-SAP On-Premise System Connectivity ... 510

13.3 ... Summary ... 513

14 ... Integrating Big Data Workloads with SAP Vora ... 515

14.1 ... SAP Vora in Kubernetes Framework ... 516

14.2 ... Data Modeling in SAP Vora ... 524

14.3 ... Hierarchies in SAP Vora ... 536

14.4 ... Full-Text Search in SAP Vora ... 540

14.5 ... Summary ... 542

15 ... Integrating with SAP Data Warehouse Cloud ... 543

15.1 ... Overview of SAP Data Warehouse Cloud ... 543

15.2 ... Understanding Spaces ... 549

15.3 ... Exploring Connections and Using the Data Builder ... 561

15.4 ... Data Builder in SAP Data Warehouse Cloud versus Pipelines in SAP Data Intelligence ... 570

15.5 ... Summary ... 570 …