SAP Data Intelligence - Teja Atluri, Dharma

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Auteur(s) : Teja Atluri, Dharma - Bardhan, Devraj - Ghosh, Santanu - Ghosh, Snehasish - Saha, Arindom

Editeur : Rheinwerk Publishing Inc.

Langue : Anglais

Parution : 01/02/2022

Format : Moyen, de 350g à 1kg

Nombre de pages : 783

Expédition : 1746

Dimensions : 26.2 x 18.4 x 5.1

ISBN : 9781493221622

Résumé :

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

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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.

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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

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