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, youll 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 installation2) Data governance3) Data processing pipelines4) Docker images5) ML Scenario Manager6) Jupyter Notebook7) Python SDK8) Integration9) Administration10) Security11) Application lifecycle management12) Use cases

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.

... Why Read This Book? ... 21 ... Audience ... 22 ... Structure of the Book ... 23 ... Acknowledgments ... 28 ... Conclusion ... 29 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 2.1 ... 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 3.1 ... 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 4.1 ... 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 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 6.1 ... 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 7.1 ... Creating Custom Operators ... 276 7.2 ... Implementing Runtime Operators ... 288 7.3 ... Creating Data Types ... 290 7.4 ... Summary ... 293 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 9.1 ... 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 10.1 ... 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 11.1 ... 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 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 13.1 ... Non-SAP Cloud System Connectivity ... 497 13.2 ... Non-SAP On-Premise System Connectivity ... 510 13.3 ... Summary ... 513 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 15.1 ... 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 16.1 ... Overview of SAP Analytics Cloud ... 571 16.2 ... Use Operators: Read File, Formatter, and Producer ... 582 16.3 ... Pipelines to Train, Predict, and Visualize Data ... 587 16.4 ... Summary ... 591 17.1 ... System Management Command-Line Client Reference ... 595 17.2 ... Administration Applications ... 599 17.3 ... Monitoring the SAP Data Intelligence Modeler ... 616 17.4 ... SAP Data Intelligence System Logging ... 626 17.5 ... System Diagnostics ... 631 17.6 ... Summary ... 637 18.1 ... Approach to Data Protection ... 639 18.2 ... Authenticating Services and Users ... 642 18.3 ... Securely Connecting On-Premise Systems ... 658 18.4 ... Summary ... 659 19.1 ... Understanding Operational Modes or Run Levels ... 661 19.2 ... Switching the Platform to Maintenance Mode ... 662 19.3 ... Increasing System Management Persistent Volume Size ... 665 19.4 ... Performing Backups ... 668 19.5 ... Summary ... 671 20.1 ... Version Control System ... 673 20.2 ... Git ... 674 20.3 ... Continuous Integration and Continuous Delivery ... 707 20.4 ... DevOps Fundamentals and Tools ... 713 20.5 ... SAP Data Intelligence as the MLOps Platform ... 723 20.6 ... Migrating from SAP Leonardo Machine Learning Foundation ... 730 20.7 ... Summary ... 734 21.1 ... Digital Transformation and SAP Data Intelligence ... 737 21.2 ... Business Content by Industry ... 740 21.3 ... Finance Use Cases ... 746 21.4 ... Supply Chain Use Cases ... 747 21.5 ... Manufacturing Use Cases ... 749 21.6 ... Summary ... 751 A ... Outlook and Roadmap ... 753 B ... The Authors ... 763