SAP Data Intelligence - Teja Atluri, Dharma

. .

Résumé :
... 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

...

Biographie:
Dharma Teja Atluri is an executive architect and artificial intelligence/machine learning evangelist at IBM. He has more than 18 years of experience working in advanced analytics with both SAP and non-SAP product lines. He has provided strategic direction to clients globally regarding the adoption of SAP and non-SAP advanced analytics products for artificial intelligence/machine learning operationalization, data management, information management, and analytics. He has also carried out multiple platform comparison initiatives for reporting, extract, transform load (ETL), data warehousing, and data science products across IBM, Microsoft Azure, Google, Amazon Web Services, and SAP. He has led the SAP analytics (reporting and enterprise information management) portfolio for IBM India, and designed client architectures for analytics with SAP and IBM capabilities. Dharma is an IBM master certified data scientist, architect, and technical specialist, and also an IBM thought leader certified consultant. His most recent SAP Data Intelligence sprint was featured for global consumption by clients and nominated for SAP Innovation Awards. He can be reached at https://www.linkedin.com/in/dharma.

...

Sommaire:
... 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

...